From 8b81fdd96eeecf1d0e479d7908376b8e57dc356d Mon Sep 17 00:00:00 2001 From: Candace Savonen Date: Thu, 22 Oct 2020 11:04:43 -0400 Subject: [PATCH 01/19] Mechanics for CSS file and navbar add feedback URL (#303) * Adding in some style with css * Use css magic * Try making the navbar blue * Add survey link * Make font smaller * Need a comma * Change to normalizePath * normalizepath separate step references.bib * Move references.bib to component folder * Made ccs modifications, added logo file Made changes to css/navbar.html Tried to add the logo but it but it cuts out and not sure how to make it decent. * Resolve render-notebooks.R conflict * Remove testing html from file diff * uncommented mobile nav Co-authored-by: dvenprasad --- 02-microarray/00-intro-to-microarray.html | 162 ++++++++++++++------ CONTRIBUTING.md | 2 +- Snakefile | 2 +- components/_navbar.html | 22 ++- references.bib => components/references.bib | 0 components/refine-bio-ex-logo.svg | 57 +++++++ components/styles.css | 69 +++++++++ scripts/render-notebooks.R | 8 +- 8 files changed, 260 insertions(+), 62 deletions(-) rename references.bib => components/references.bib (100%) create mode 100644 components/refine-bio-ex-logo.svg create mode 100644 components/styles.css diff --git a/02-microarray/00-intro-to-microarray.html b/02-microarray/00-intro-to-microarray.html index 45b74393..4694e5c0 100644 --- a/02-microarray/00-intro-to-microarray.html +++ b/02-microarray/00-intro-to-microarray.html @@ -1263,25 +1263,22 @@ }; - - + - - - @@ -2608,6 +2590,73 @@ } + @@ -2874,15 +2923,20 @@ @@ -3023,34 +3083,34 @@

0.4.1 Why doesn’t the gene I ca

References

-

Dai M., P. Wang, A. D. Boyd, G. Kostov, and B. Athey et al., 2005 Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res 33: e175.

+

Dai M., P. Wang, A. D. Boyd, G. Kostov, and B. Athey et al., 2005 Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Research 33: e175. https://doi.org/10.1093/nar/gni179

-

Farina D., 2020 Gene expression analysis and DNA microarray assays

+

Farina D., 2020 Gene expression analysis and DNA microarray assays. https://www.youtube.com/watch?v=Hv5flUOsE0s

-

Govindarajan R., J. Duraiyan, K. Kaliyappan, and M. Palanisamy, 2012 Microarray and its applications. J Pharm Bioallied Sci 4: S310–312.

+

Govindarajan R., J. Duraiyan, K. Kaliyappan, and M. Palanisamy, 2012 Microarray and its applications. Journal of Pharmacy and Bioallied Sciences 4: S310–312. https://doi.org/10.4103/0975-7406.100283

-

LCSciences, 2014 Microarray or RNA sequencing?

+

LCSciences, 2014 Microarray or RNA sequencing? https://www.lcsciences.com/news/microarray-or-rna-sequencing/

-

Mantione K. J., R. M. Kream, H. Kuzelova, R. Ptacek, and J. Raboch et al., 2014 Comparing bioinformatic gene expression profiling methods: microarray and RNA-Seq. Med Sci Monit Basic Res 20: 138–142.

+

Mantione K. J., R. M. Kream, H. Kuzelova, R. Ptacek, and J. Raboch et al., 2014 Comparing bioinformatic gene expression profiling methods: Microarray and RNA-Seq. Medical Science Monitor Basic Research 20: 138–142. https://doi.org/10.12659/MSMBR.892101

-

Piccolo S. R., Y. Sun, J. D. Campbell, M. E. Lenburg, and A. H. Bild et al., 2012 A single-sample microarray normalization method to facilitate personalized-medicine workflows. Genomics 100: 337–344.

+

Piccolo S. R., Y. Sun, J. D. Campbell, M. E. Lenburg, and A. H. Bild et al., 2012 A single-sample microarray normalization method to facilitate personalized-medicine workflows. Genomics 100: 337–344. https://doi.org/10.1016/j.ygeno.2012.08.003

-

Sánchez A., and M. C. R. de Villa, 2008 A tutorial review of microarray data analysis

+

Sánchez A., and M. C. R. de Villa, 2008 A tutorial review of microarray data analysis. http://www.ub.edu/stat/docencia/bioinformatica/microarrays/ADM/slides/A_Tutorial_Review_of_Microarray_data_Analysis_17-06-08.pdf

-

Slonim D. K., and I. Yanai, 2009 Getting started in gene expression microarray analysis. PLoS Comput. Biol. 5: e1000543.

+

Slonim D. K., and I. Yanai, 2009 Getting started in gene expression microarray analysis. PLOS Computational Biology 5: e1000543. https://doi.org/10.1371/journal.pcbi.1000543

-

Tarca A. L., R. Romero, and S. Draghici, 2006 Analysis of microarray experiments of gene expression profiling. Am. J. Obstet. Gynecol. 195: 373–388.

+

Tarca A. L., R. Romero, and S. Draghici, 2006 Analysis of microarray experiments of gene expression profiling. American Journal of Obstetrics and Gynecology 195: 373–388. https://doi.org/10.1016/j.ajog.2006.07.001

-

Wu H., Introduction to gene expression microarray data analysis

+

Wu H., Introduction to gene expression microarray data analysis. http://web1.sph.emory.edu/users/hwu30/teaching/bioc/GE1.pdf

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 4e49ccdf..71fc5447 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -347,7 +347,7 @@ The `render-notebooks.R` script adds a `bibliography:` specification in the `.Rm **Options:** - `--rmd`: provided by snakemake, the input `.Rmd` file to render. - `--bib_file`: File path for the `bibliography:` header option. -Default is the `references.bib` script at the top of the repository. +Default is the `references.bib` in the `components` folder. - `--html`: Default is to save the output `.html` file the same name as the input `.Rmd` file. This option allows you to specify an output file name. Default is used by snakemake. ### Add new analyses to the Snakefile diff --git a/Snakefile b/Snakefile index 60ca9dcd..4909b910 100644 --- a/Snakefile +++ b/Snakefile @@ -27,7 +27,7 @@ rule render_citations: shell: "Rscript scripts/render-notebooks.R" " --rmd {input.rmd}" - " --bib_file references.bib" + " --bib_file components/references.bib" " --cite_style components/genetics.csl" " --html {output}" " --style" diff --git a/components/_navbar.html b/components/_navbar.html index 8ecdf1e4..1ed6c957 100644 --- a/components/_navbar.html +++ b/components/_navbar.html @@ -1,15 +1,20 @@ diff --git a/references.bib b/components/references.bib similarity index 100% rename from references.bib rename to components/references.bib diff --git a/components/refine-bio-ex-logo.svg b/components/refine-bio-ex-logo.svg new file mode 100644 index 00000000..e001c3ee --- /dev/null +++ b/components/refine-bio-ex-logo.svg @@ -0,0 +1,57 @@ + + + + Logo + Created with Sketch. + + + + + + \ No newline at end of file diff --git a/components/styles.css b/components/styles.css new file mode 100644 index 00000000..9ef20671 --- /dev/null +++ b/components/styles.css @@ -0,0 +1,69 @@ +html, +body { + font-family: 'Lato', sans-serif; + font-size: 16px; + -webkit-font-smoothing: antialiased; + line-height: 1.5; +} +h1, h2, h3, h4, h5, h6 { + font-family: 'Rubik', sans-serif; +} +h1 { + font-size: 1.625rem; +} +h2 { + font-size: 1.375rem; +} +h3 { + font-size: 1.25rem +} +h4 { + font-size: 1.125rem; +} +h5, h6 { + font-size: 1rem +} +p { + font-size: 1rem; + margin: 0 0 0.5rem 0; + line-height: 1.5; +} +a { + text-decoration: none; + color: #386db0; + line-height: 1; +} +.navbar-default, .navbar-nav, .navbar-inverse{ + font-size: 1rem; + max-width: 100%; + background-color: #386db0 !important; + color: #FDFDFD !important; +} + +.navbar-nav>li>a { + font-size: 1rem; + background-color: #386db0 !important; + color: #FDFDFD !important; +} + +.navbar-default .navbar-brand{ + color: #FDFDFD; !important; + float: left; + max-width: 100%; + display: block; + overflow: visible; + padding-bottom: 32px; +} + +ul.dropdown-menu { + background-color: #FDFDFD !important; + color: #000000 !important; +} +.dropdown-menu>li>a:hover, .dropdown-menu>li>a:active { + background-color: #386db0 !important; + color: #FDFDFD !important; +} +.list-group-item.active, .list-group-item.active:focus, .list-group-item.active:hover { + background-color: #386db0 !important; + color: #FDFDFD !important; +} diff --git a/scripts/render-notebooks.R b/scripts/render-notebooks.R index 3e9e2861..61adb6eb 100644 --- a/scripts/render-notebooks.R +++ b/scripts/render-notebooks.R @@ -54,6 +54,9 @@ opt <- parse_args(OptionParser(option_list = option_list)) # Get working directory base_dir <- getwd() +# Normalize file path +opt$bib_file <- normalizePath(opt$bib_file) + # Check that the rmd file exists if (!file.exists(opt$rmd)) { stop("Rmd file specified with --rmd is not found.") @@ -63,7 +66,7 @@ if (!file.exists(opt$rmd)) { if (!file.exists(opt$bib_file)) { stop("File specified for --bib_file option is not at the specified file path.") } else { - header_line <- paste("bibliography:", normalizePath(opt$bib_file)) + header_line <- paste("bibliography:", opt$bib_file) } # Check for a citation style if (!is.null(opt$cite_style)){ @@ -112,8 +115,9 @@ rmarkdown::render(tmp_file, output_format = rmarkdown::html_document( toc = TRUE, toc_depth = 2, toc_float = TRUE, number_sections = TRUE, + highlight = "haddock", df_print = "paged", - highlight = "haddock" + css = normalizePath(file.path("components", "styles.css")) ), # Save to original html output file name output_file = output_file From 052286cb4bf60b6dd7703593ce697d15e861f8cc Mon Sep 17 00:00:00 2001 From: Candace Savonen Date: Mon, 26 Oct 2020 14:31:13 -0400 Subject: [PATCH 02/19] Making staging changes live (#329) * Adding in some style with css * Use css magic * Try making the navbar blue * Add survey link * Make font smaller * Need a comma * Change to normalizePath * normalizepath separate step references.bib * Move references.bib to component folder * Update github actions to reflect staging branch (#311) * Update github actions to reflect staging branch * Add libglpk40 to Dockerfile * Make it gh-pages-stages! * Remove dockerfile change that should have been on its own all along * Does this work? * Declare a uses * Switch how env is declared * Force it to run so we can test it * try no curly brackets * What's up with the branch * Move to bash if instead * Need quotes? * forgot a `then` * Try dollar signs * Doesn't like the `.`? * Use curly brackets * Try ${GITHUB_REF} * Try ${BRANCH_NAME} * try ${GITHUB_REF#refs/*/} * use jashapiro suggestion * Change to base ref * Change back to `github.ref` * Get rid of PR `on:` * Try another test * Docker dep fix: Add lib package 40 thing that clusterprofiler needs (#316) * Add lib package 40 thing that clusterprofiler needs * Try adding options(warn = 2) * Test if options(warn =2) means it breaks like it should * Revert "Test if options(warn =2) means it breaks like it should" This reverts commit d9f688f68448ef69fe4c1caa48af23051cd7f4e3. * Revert "Try another test" This reverts commit 845cf1aff92ea7b83f402bbefd563562b44e5eac. * Add google analytics to renderings (#314) * Try adding google analytics * Add to header using includes * temporary file snuck in there * Restore master version so they aren't in the review * Let's call an html file and html file * Docker dep fix: Add lib package 40 thing that clusterprofiler needs (#316) * Add lib package 40 thing that clusterprofiler needs * Try adding options(warn = 2) * Test if options(warn =2) means it breaks like it should * Revert "Test if options(warn =2) means it breaks like it should" This reverts commit d9f688f68448ef69fe4c1caa48af23051cd7f4e3. * Only push if we are in master. For simplicity, we will now run this even if the dockerfile hasn't changed. * Add test target * test staging workflow with this branch * back to latest tag * Try separate push step * change tags to test push * Revert "change tags to test push" This reverts commit 6a38574d312cee82c90c3c036ac9033f9af7f7ec. * Remove this branch from triggers * Push staging, retag and push master Okay, so the branch name is now inaccurate, but that is fine... * Made ccs modifications, added logo file Made changes to css/navbar.html Tried to add the logo but it but it cuts out and not sure how to make it decent. * Resolve render-notebooks.R conflict * Remove testing html from file diff * uncommented mobile nav * Update scripts/render-notebooks.R * Add some issue templates (#319) * Add some rough draft issue templates * Incorporate cbethell review * Get rid of `Other` labels that aren't useful * Update diagrams showing how microarray/RNA-seq work (#326) * Mechanics for CSS file and navbar add feedback URL (#303) * Adding in some style with css * Use css magic * Try making the navbar blue * Add survey link * Make font smaller * Need a comma * Change to normalizePath * normalizepath separate step references.bib * Move references.bib to component folder * Made ccs modifications, added logo file Made changes to css/navbar.html Tried to add the logo but it but it cuts out and not sure how to make it decent. * Resolve render-notebooks.R conflict * Remove testing html from file diff * uncommented mobile nav Co-authored-by: dvenprasad * Update microarray and RNAseq overview figures - add context re figures - change .jpg to .png for consistency * Revert "Mechanics for CSS file and navbar add feedback URL (#303)" This reverts commit 8b81fdd96eeecf1d0e479d7908376b8e57dc356d. * update links to diagrams * @dvenprasad updated figure spacing * add the right updated figure * replace section of link to figures with updated commit id * incorporate @cansavvy's suggested changes Co-authored-by: Candace Savonen Co-authored-by: dvenprasad Co-authored-by: Joshua Shapiro Co-authored-by: dvenprasad Co-authored-by: Chante Bethell <43576623+cbethell@users.noreply.github.com> --- .github/ISSUE_TEMPLATE/new-analysis.md | 26 +++++ .github/ISSUE_TEMPLATE/other-issue.md | 26 +++++ .github/workflows/docker-build-push.yml | 69 ++++++----- .github/workflows/docker-build.yml | 4 +- .github/workflows/style-and-sp-check.yml | 2 +- 02-microarray/00-intro-to-microarray.Rmd | 5 +- 02-microarray/00-intro-to-microarray.html | 6 +- 03-rnaseq/00-intro-to-rnaseq.Rmd | 5 +- 03-rnaseq/00-intro-to-rnaseq.html | 129 +++++++++------------ components/figures/microarray-overview.jpg | Bin 44998 -> 0 bytes components/figures/microarray-overview.png | Bin 0 -> 107241 bytes components/figures/rnaseq-overview.png | Bin 86861 -> 99450 bytes components/google-analytics.html | 8 ++ scripts/render-notebooks.R | 6 +- 14 files changed, 171 insertions(+), 115 deletions(-) create mode 100644 .github/ISSUE_TEMPLATE/new-analysis.md create mode 100644 .github/ISSUE_TEMPLATE/other-issue.md delete mode 100644 components/figures/microarray-overview.jpg create mode 100644 components/figures/microarray-overview.png create mode 100644 components/google-analytics.html diff --git a/.github/ISSUE_TEMPLATE/new-analysis.md b/.github/ISSUE_TEMPLATE/new-analysis.md new file mode 100644 index 00000000..c6a53856 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/new-analysis.md @@ -0,0 +1,26 @@ +--- +name: New analysis example +about: Use this issue template for filing a new analysis issue +title: 'New Analysis Example:' +labels: new analysis example +assignees: '' + +--- + +### What are the goals of this new example analysis? + + + + + +### What kind of dataset will this need? + + + +### What steps should be included in this analysis? + + + +### What packages/methods do you recommend using or looking into for this analysis? + + diff --git a/.github/ISSUE_TEMPLATE/other-issue.md b/.github/ISSUE_TEMPLATE/other-issue.md new file mode 100644 index 00000000..c253dbbf --- /dev/null +++ b/.github/ISSUE_TEMPLATE/other-issue.md @@ -0,0 +1,26 @@ +--- +name: Other issue +about: Use this issue template to describe a current issue with an analysis or documentation (that is not a new example analysis) +assignees: '' + +--- + +### Background + + + + +### Problem + + + +#### What potential "gotchas" do we know of? + + + +### What are the recommended next steps? + + + + + diff --git a/.github/workflows/docker-build-push.yml b/.github/workflows/docker-build-push.yml index 47cc48fd..b3832343 100644 --- a/.github/workflows/docker-build-push.yml +++ b/.github/workflows/docker-build-push.yml @@ -4,7 +4,7 @@ name: Build, Render, and Push # events only for the master branch on: push: - branches: [ master ] + branches: [ staging, master ] jobs: # This workflow contains a single job called "build-all" @@ -21,54 +21,54 @@ jobs: fetch-depth: 0 # use alexslemonade-docs-bot token: ${{ secrets.DOCS_BOT_GITHUB_TOKEN }} + - name: Checkout pages branch and sync with changes run: | + echo $GITHUB_REF + if [ $GITHUB_REF == 'refs/heads/master' ] + then + pages_branch="gh-pages" + elif [ $GITHUB_REF == 'refs/heads/staging' ] + then + pages_branch="gh-pages-stages" + fi git config --local user.email "actions@github.com" git config --local user.name "Alex's Lemonade Docs Bot" - git checkout gh-pages + git checkout $pages_branch git merge -s recursive --strategy-option=theirs ${{ github.event.after }} - # Test if Dockerfile has changed - # sets steps.check_docker.outputs.changed to 1 if the Dockerfile has changed, 0 otherwise - - name: Check Dockerfile for changes - id: check_docker - env: - BEFORE: ${{ github.event.before }} - run: | - git diff-index --name-only $BEFORE > changes.txt - if grep "docker/Dockerfile" changes.txt ; then - echo "Dockerfile changed" - echo "::set-output name=changed::1" - else - echo "No change to Dockerfile" - echo "::set-output name=changed::0" - fi - rm changes.txt - # Login to Dockerhub - name: Login to DockerHub - if: steps.check_docker.outputs.changed == 1 uses: docker/login-action@v1 with: username: ${{ secrets.DOCKER_ID }} password: ${{ secrets.DOCKER_PASSWORD }} # set up Docker build - name: Set up Docker Buildx - if: steps.check_docker.outputs.changed == 1 uses: docker/setup-buildx-action@v1 - # Build docker image (We are not using caching here to force a clean build) - - name: Build and Push Docker image - if: steps.check_docker.outputs.changed == 1 + # Build the Docker image + - name: Build and Load Docker image uses: docker/build-push-action@v2 with: - push: true + push: false + load: true context: docker file: docker/Dockerfile tags: ccdl/refinebio-examples:latest + # push the Docker image if this is staging + - name: Push Docker image + if: github.ref == 'refs/heads/staging' + run: docker push ccdl/refinebio-examples:latest + # retag and push the Docker image if this is master + - name: Push release Docker image + if: github.ref == 'refs/heads/master' + run: | + docker tag ccdl/refinebio-examples:latest ccdl/refinebio-examples:release + docker push ccdl/refinebio-examples:release + # download data - name: Download data run: bash scripts/download-data.sh - - name: Render all pages to html run: | docker run \ @@ -76,11 +76,18 @@ jobs: ccdl/refinebio-examples \ snakemake --cores 2 --forceall - - name: Commit changed html + # If we are on the staging branch, do not publish to github pages + - name: Commit changed html back to non-public pages + if: github.ref == 'refs/heads/staging' run: | git add -A - git commit -m 'Render html' || echo "No changes to commit" - git push origin gh-pages || echo "No changes to push" - - + git commit -m 'Render html, do not publish' || echo "No changes to commit" + git push origin gh-pages-stages || echo "No changes to push" + # If we are on the master branch, publish to github pages! + - name: Commit changed html to public pages + if: github.ref == 'refs/heads/master' + run: | + git add -A + git commit -m 'Render html and publish' || echo "No changes to commit" + git push origin gh-pages || echo "No changes to push" diff --git a/.github/workflows/docker-build.yml b/.github/workflows/docker-build.yml index e5da75b0..aa065d43 100644 --- a/.github/workflows/docker-build.yml +++ b/.github/workflows/docker-build.yml @@ -1,10 +1,10 @@ name: Build Docker -# Controls when the action will run. Triggers the workflow for a pull request for +# Controls when the action will run. Triggers the workflow for a pull request for # master on: pull_request: - branches: [ master ] + branches: [ staging, master ] paths: [ docker/Dockerfile ] # A workflow run is made up of one or more jobs that can run sequentially or in parallel diff --git a/.github/workflows/style-and-sp-check.yml b/.github/workflows/style-and-sp-check.yml index cf536f9e..314b12eb 100644 --- a/.github/workflows/style-and-sp-check.yml +++ b/.github/workflows/style-and-sp-check.yml @@ -5,7 +5,7 @@ name: Style and spell check R markdowns # events but only for the master branch on: pull_request: - branches: [ master ] + branches: [ staging, master ] # A workflow run is made up of one or more jobs that can run sequentially or in parallel jobs: diff --git a/02-microarray/00-intro-to-microarray.Rmd b/02-microarray/00-intro-to-microarray.Rmd index ae4f3159..b0b00b25 100644 --- a/02-microarray/00-intro-to-microarray.Rmd +++ b/02-microarray/00-intro-to-microarray.Rmd @@ -32,12 +32,13 @@ Microarrays measure gene expression using chips filled with oligonucleotide prob After hybridization, the microarrays are scanned, and the fluorescence intensity for each probe is measured. The fluorescence intensity indicates the number of labeled fragments bound and therefore the relative quantity of the transcript the probe is designed for. - + -[@microarray-video] +[based on diagram from @microarray-video] There are many different kinds of microarray platforms, which can be broadly separated into single-color and [two-color arrays](https://www.ebi.ac.uk/training/online/course/functional-genomics-ii-common-technologies-and-data-analysis-methods/microarrays). At this time, refine.bio only supports single-color arrays, so our examples and advice are generally from the perspective of using single-color array. +The diagram above shows an overview of the single-color array process which includes extracting the total RNA from a sample, labeling the RNA with fluorescent dye, hybridizing the labels, and scanning the fluorescent image to analyze the fluorescence intensity. The two most common microarray platforms on refine.bio are [Affymetrix GeneChips](https://www.thermofisher.com/us/en/home/life-science/microarray-analysis/transcriptome-profiling-microarrays.html) and [Illumina BeadArray](https://www.illumina.com/documents/products/datasheets/datasheet_gene_exp_analysis.pdf). A longer list of specific arrays that are supported by refine.bio can be found [here](https://github.com/AlexsLemonade/refinebio/blob/dev/config/supported_microarray_platforms.csv). diff --git a/02-microarray/00-intro-to-microarray.html b/02-microarray/00-intro-to-microarray.html index 4694e5c0..b9d85539 100644 --- a/02-microarray/00-intro-to-microarray.html +++ b/02-microarray/00-intro-to-microarray.html @@ -3032,9 +3032,9 @@

CCDL for ALSF

0.1 Introduction to microarray technology

Microarrays measure gene expression using chips filled with oligonucleotide probes designed to hybridize to labeled RNA samples. After hybridization, the microarrays are scanned, and the fluorescence intensity for each probe is measured. The fluorescence intensity indicates the number of labeled fragments bound and therefore the relative quantity of the transcript the probe is designed for.

-

-

(Farina 2020)

-

There are many different kinds of microarray platforms, which can be broadly separated into single-color and two-color arrays. At this time, refine.bio only supports single-color arrays, so our examples and advice are generally from the perspective of using single-color array.

+

+

(based on diagram from Farina 2020)

+

There are many different kinds of microarray platforms, which can be broadly separated into single-color and two-color arrays. At this time, refine.bio only supports single-color arrays, so our examples and advice are generally from the perspective of using single-color array. The diagram above shows an overview of the single-color array process which includes extracting the total RNA from a sample, labeling the RNA with fluorescent dye, hybridizing the labels, and scanning the fluorescent image to analyze the fluorescence intensity.

The two most common microarray platforms on refine.bio are Affymetrix GeneChips and Illumina BeadArray. A longer list of specific arrays that are supported by refine.bio can be found here.

As with all experimental methods, microarrays have strengths and limitations that you should consider in regards to your scientific questions.

diff --git a/03-rnaseq/00-intro-to-rnaseq.Rmd b/03-rnaseq/00-intro-to-rnaseq.Rmd index 584ea4c5..909232c0 100644 --- a/03-rnaseq/00-intro-to-rnaseq.Rmd +++ b/03-rnaseq/00-intro-to-rnaseq.Rmd @@ -36,10 +36,11 @@ This tutorial has example analyses [organized by technology](../01-getting-start RNA-sequencing measures gene expression by direct high-throughput sequencing methods after the RNA has been isolated from a sample. - + -[@rnaseq-blog] +[based on diagram from @rnaseq-blog] +The diagram above provides a brief overview of the RNA-seq process, which includes extracting the total RNA from a tissue population, isolating the specific RNA species, converting the RNA to cDNA, and constructing a sequencing library to perform PCR amplification and sequencing. As with all experimental methods, RNA-seq has strengths and limitations that you should consider in regards to your scientific questions. ### RNA-seq data **strengths** diff --git a/03-rnaseq/00-intro-to-rnaseq.html b/03-rnaseq/00-intro-to-rnaseq.html index c48b0903..bda2b6cc 100644 --- a/03-rnaseq/00-intro-to-rnaseq.html +++ b/03-rnaseq/00-intro-to-rnaseq.html @@ -1263,25 +1263,22 @@ }; - - + - - - @@ -2900,6 +2882,7 @@
  • Differential Expression - Several groups
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • +
  • Pathway Analysis - ORA
  • Ensembl Gene ID Annotation
  • Ortholog Mapping
    • @@ -2982,14 +2965,14 @@

    CCDL for ALSF

    0.1 Introduction to RNA-seq technology

    RNA-sequencing measures gene expression by direct high-throughput sequencing methods after the RNA has been isolated from a sample.

    -

    -

    (“An introduction to rna-seq methods, applications, experimental design, and technical challenges” 2015)

    -

    As with all experimental methods, RNA-seq has strengths and limitations that you should consider in regards to your scientific questions.

    +

    +

    (based on diagram from “An introduction to RNA-Seq methods, applications, experimental design, and technical challenges” 2015)

    +

    The diagram above provides a brief overview of the RNA-seq process, which includes extracting the total RNA from a tissue population, isolating the specific RNA species, converting the RNA to cDNA, and constructing a sequencing library to perform PCR amplification and sequencing. As with all experimental methods, RNA-seq has strengths and limitations that you should consider in regards to your scientific questions.

    0.1.1 RNA-seq data strengths

      -
    • RNA-seq can assay unknown transcripts, as it is not bound to a pre-determined set of probes like microarrays (Wang et al.).
      • -
    • Its values are considered more dynamic than microarray values which are constrained to a smaller range based on background signal and probe sets being saturated (Wang et al.).
      • +
    • RNA-seq can assay unknown transcripts, as it is not bound to a pre-determined set of probes like microarrays (Wang et al. 2009).
      • +
    • Its values are considered more dynamic than microarray values which are constrained to a smaller range based on background signal and probe sets being saturated (Wang et al. 2009).
    @@ -3019,8 +3002,8 @@

    0.1.3 About quantile normalizatio

    0.2.2 DESeq2 transformation methods

    -

    Our examples recommend using DESeq2 for normalizing your RNA-seq data. You may have heard about or worked with FPKM, TPM, RPKMs; how does DESeq2’s normalization compare? This handy table from an online Harvard Bioinformatics Core course nicely summarizes and compares these different methods (Harvard Chan Bioinformatics Core (HBC)). For more about the steps behind DESeq2 normalization, we highly recommend this StatQuest video which explains it quite nicely (Josh Starmer 2017b).

    -

    To normalize and transform our data with DESeq2, we generally use vst() (variance stabilizing transformation) or rlog() (regularized logarithm transformation). Both methods are very similar. Both normalize your data by correcting for library size differences but they also transform your data removing the dependence of the variance on the mean, meaning that low mean genes won’t have inflated variance from just one or a few samples having higher values than the rest (Michael I. Love, Simon Anders, and Wolfgang Huber 2020). Of the two methods, rlog() takes a bit longer to run (Michael I. Love and Huber 2019). If you end up using a larger dataset and rlog() transformation takes a bit too long, you can switch to using vst() with confidence since they yield similar results given the dataset is large enough (Michael I. Love and Huber 2019).

    +

    Our examples recommend using DESeq2 for normalizing your RNA-seq data. You may have heard about or worked with FPKM, TPM, RPKMs; how does DESeq2’s normalization compare? This handy table from an online Harvard Bioinformatics Core course nicely summarizes and compares these different methods (Harvard Chan Bioinformatics Core). For more about the steps behind DESeq2 normalization, we highly recommend this StatQuest video which explains it quite nicely (Starmer 2017a).

    +

    To normalize and transform our data with DESeq2, we generally use vst() (variance stabilizing transformation) or rlog() (regularized logarithm transformation). Both methods are very similar. Both normalize your data by correcting for library size differences but they also transform your data removing the dependence of the variance on the mean, meaning that low mean genes won’t have inflated variance from just one or a few samples having higher values than the rest (Michael I. Love and Huber 2020). Of the two methods, rlog() takes a bit longer to run (Michael I. Love and Huber 2019). If you end up using a larger dataset and rlog() transformation takes a bit too long, you can switch to using vst() with confidence since they yield similar results given the dataset is large enough (Michael I. Love and Huber 2019).

    0.2.3 Further resources for DESeq2

    @@ -3063,7 +3046,7 @@

    0.3.0.1 Why isn’t the gene I ca

    0.3.0.2 What about edgeR?

    In short, both edgeR and DESeq2 are good options and we at the CCDL just went with one of our preferences! See this blog that summarizes these – by one of the creators of DESeq2 – he agrees edgeR is also great.

    -

    If you have strong preferences for edgeR, you can definitely use your refine.bio data with it, but we currently do not have examples of that. In this case, we’d refer you to edgeR’s section of this example analysis and wish you the best of luck on your data adventures (Kasper D. Hansen)!

    +

    If you have strong preferences for edgeR, you can definitely use your refine.bio data with it, but we currently do not have examples of that. In this case, we’d refer you to edgeR’s section of this example analysis and wish you the best of luck on your data adventures (Hansen)!

    0.3.0.3 What if I care about isoforms?

    @@ -3075,58 +3058,58 @@

    0.3.0.3 What if I care about isof

    References

    -

    An introduction to rna-seq methods, applications, experimental design, and technical challenges, 2015

    +

    An introduction to RNA-Seq methods, applications, experimental design, and technical challenges, 2015. https://rna-seqblog.com/an-introduction-to-rna-seq-methods-applications-experimental-design-and-technical-challenges/

    +
    +
    +

    Hadfield J., 2016 An introduction to RNA-seq. https://bitesizebio.com/13542/what-everyone-should-know-about-rna-seq/

    -

    Hansen K. D., S. E. Brenner, and S. Dudoit, 2010 Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res. 38: e131.

    +

    Hansen K. D., S. E. Brenner, and S. Dudoit, 2010 Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Research 38: e131. https://doi.org/10.1093/nar/gkq224

    +
    +
    -

    Harvard Chan Bioinformatics Core (HBC), Introduction to dge - deseq2 analysis

    +

    Harvard Chan Bioinformatics Core, Introduction to DGE - DESeq2 analysis. https://hbctraining.github.io/DGE_workshop/lessons/04_DGE_DESeq2_analysis.html

    -

    Harvard Chan Bioinformatics Core (HBC), Introduction to dge - count normalization

    -
    -
    -

    James Hadfield, 2016 An introduction to rna-seq

    -
    -
    -

    Josh Starmer, 2017a StatQuest: A gentle introduction to rna-seq

    -
    -
    -

    Josh Starmer, 2017b StatQuest: DESeq2, part 1, library normalization

    -
    -
    -

    Kasper D. Hansen, Count based rna-seq analysis

    +

    Harvard Chan Bioinformatics Core, Introduction to DGE - count normalization. https://hbctraining.github.io/DGE_workshop_salmon/lessons/02_DGE_count_normalization.html

    -

    Love M. I., W. Huber, and S. Anders, 2014 Moderated estimation of fold change and dispersion for rna-seq data with deseq2. Genome Biology 15. https://doi.org/10.1186/s13059-014-0550-8

    +

    Love M. I., W. Huber, and S. Anders, 2014 Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biology 15. https://doi.org/10.1186/s13059-014-0550-8

    -

    Love M. I., 2016 RNA-seq fragment sequence bias

    +

    Love M. I., 2016 RNA-seq fragment sequence bias. https://mikelove.wordpress.com/2016/09/26/rna-seq-fragment-sequence-bias/

    -

    Love M. I., J. B. Hogenesch, and R. A. Irizarry, 2016 Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. Nat. Biotechnol. 34: 1287–1291.

    +

    Love M. I., J. B. Hogenesch, and R. A. Irizarry, 2016 Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. Nature Biotechnology 34: 1287–1291. https://doi.org/10.1038/nbt.3682

    -

    Michael I. Love Simon Anders, and W. Huber, 2014 Beginner’s guide to using the deseq2 package

    +

    Michael I. Love Simon Anders, and W. Huber, 2014 Beginner’s guide to using the DESeq2 package. https://bioc.ism.ac.jp/packages/2.14/bioc/vignettes/DESeq2/inst/doc/beginner.pdf

    -

    Michael I. Love V. K. Simon Anders, and W. Huber, 2019 RNA-seq workflow: Gene-level exploratory analysis and differential expression

    +

    Michael I. Love V. K. Simon Anders, and W. Huber, 2019 RNA-seq workflow: Gene-level exploratory analysis and differential expression. http://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html#the-variance-stabilizing-transformation-and-the-rlog

    -

    Michael I. Love, Simon Anders, and Wolfgang Huber, 2020 Analyzing rna-seq data with deseq2

    +

    Michael I. Love Simon Anders, and W. Huber, 2020 Analyzing RNA-seq data with DESeq2. https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html

    -

    Pepke S., B. Wold, and A. Mortazavi, 2009 Computation for ChIP-seq and RNA-seq studies. Nat. Methods 6: 22–32.

    +

    Pepke S., B. Wold, and A. Mortazavi, 2009 Computation for ChIP-seq and RNA-seq studies. Nature Methods 6: 22–32. https://doi.org/10.1038/nmeth.1371

    -

    Soneson C., M. I. Love, and M. D. Robinson, 2015 Differential analyses for rna-seq: Transcript-level estimates improve gene-level inferences. F1000Research 4. https://doi.org/10.12688/f1000research.7563.1

    +

    Soneson C., M. I. Love, and M. D. Robinson, 2015 Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Research 4. https://doi.org/10.12688/f1000research.7563.2

    +
    +
    +

    Starmer J., 2017a StatQuest: DESeq2, part 1, library normalization. https://www.youtube.com/watch?v=UFB993xufUU

    +
    +
    +

    Starmer J., 2017b StatQuest: A gentle introduction to RNA-seq. https://www.youtube.com/watch?v=tlf6wYJrwKY

    -

    Wang Z., M. Gerstein, and M. Snyder, RNA-seq: A revolutionary tool for transcriptomics. Nature reviews. Genetics 10: 57–63. https://doi.org/10.1038/nrg2484

    +

    Wang Z., M. Gerstein, and M. Snyder, 2009 RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics 10: 57–63. https://doi.org/10.1038/nrg2484

    -

    Zhang C., B. Zhang, L. L. Lin, and S. Zhao, 2017 Evaluation and comparison of computational tools for RNA-seq isoform quantification. BMC Genomics 18: 583.

    +

    Zhang C., B. Zhang, L. L. Lin, and S. Zhao, 2017 Evaluation and comparison of computational tools for RNA-seq isoform quantification. BMC Genomics 18: 583. https://doi.org/10.1186/s12864-017-4002-1

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i^#K3?1QY-O08mQ>1^@s6009620Ac_D0BpAe0001iM!_cl literal 0 HcmV?d00001 diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd new file mode 100644 index 00000000..65a3cf30 --- /dev/null +++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd @@ -0,0 +1,480 @@ +--- +title: "Gene set enrichment analysis - Microarray" +author: "CCDL for ALSF" +date: "October 2020" +output: + html_notebook: + toc: true + toc_float: true + number_sections: true +--- + +# Purpose of this analysis + +This example is one of pathway analysis module set, we recommend looking at the [pathway analysis introduction](https://alexslemonade.github.io/refinebio-examples/02-microarray/pathway-analysis_microarray_00_intro.html) to help you determine which pathway analysis method is best suited for your purposes. + +This particular example analysis shows how you can use gene set enrichment analysis (GSEA) to detect situations where all genes in a predefined set change in a coordinated way, detecting even small statistical but coordinated changes between two biological states. + +⬇️ [**Jump to the analysis code**](#analysis) ⬇️ + +# How to run this example + +For general information about our tutorials and the basic software packages you will need, please see our ['Getting Started' section](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#how-this-tutorial-is-structured). +We recommend taking a look at our [Resources for Learning R](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#resources-for-learning-r) if you have not written code in R before. + +## Obtain the `.Rmd` file + +To run this example yourself, [download the `.Rmd` for this analysis by clicking this link](https://alexslemonade.github.io/refinebio-examples/02-microarray/pathway-analysis_microarray_03_gsea.Rmd). + +Clicking this link will most likely send this to your downloads folder on your computer. +Move this `.Rmd` file to where you would like this example and its files to be stored. + +You can open this `.Rmd` file in RStudio and follow the rest of these steps from there. (See our [section about getting started with R notebooks](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#how-to-get-and-use-rmds) if you are unfamiliar with `.Rmd` files.) + +## Set up your analysis folders + +Good file organization is helpful for keeping your data analysis project on track! +We have set up some code that will automatically set up a folder structure for you. +Run this next chunk to set up your folders! + +If you have trouble running this chunk, see our [introduction to using `.Rmd`s](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#how-to-get-and-use-rmds) for more resources and explanations. + +```{r} +# Create the data folder if it doesn't exist +if (!dir.exists("data")) { + dir.create("data") +} + +# Define the file path to the plots directory +plots_dir <- "plots" # Can replace with path to desired output plots directory + +# Create the plots folder if it doesn't exist +if (!dir.exists(plots_dir)) { + dir.create(plots_dir) +} + +# Define the file path to the results directory +results_dir <- "results" # Can replace with path to desired output results directory + +# Create the results folder if it doesn't exist +if (!dir.exists(results_dir)) { + dir.create(results_dir) +} +``` + +In the same place you put this `.Rmd` file, you should now have three new empty folders called `data`, `plots`, and `results`! + +## Obtain the gene set for this example + +In this example, we are using differential expression results table we obtained from an [example analysis of zebrafish samples overexpressing human CREB experiment](https://alexslemonade.github.io/refinebio-examples/02-microarray/differential-expression_microarray_01_2-groups.html) using [`limma`](https://bioconductor.org/packages/release/bioc/html/limma.html) [@Ritchie2015]. +The table contains summary statistics including Ensembl gene IDs, t-statistic values, and adjusted p-values (FDR in this case). + +We have provided this file for you and the code in this notebook will read in the results that are stored online, but if you'd like to follow the steps for obtaining this results file yourself, we suggest going through [that differential expression analysis example](https://alexslemonade.github.io/refinebio-examples/02-microarray/differential-expression_microarray_01_2-groups.html). + +## About the dataset we are using for this example + +For this example analysis, we will use this [CREB overexpression zebrafish experiment](https://www.refine.bio/experiments/GSE71270/creb-overexpression-induces-leukemia-in-zebrafish-by-blocking-myeloid-differentiation-process) [@Tregnago2016]. +@Tregnago2016 measured microarray gene expression of zebrafish samples overexpressing human CREB, as well as control samples. + +## Check out our file structure! + +Your new analysis folder should contain: + +- The example analysis `.Rmd` you downloaded +- A folder called `data` (currently empty) +- A folder for `plots` (currently empty) +- A folder for `results` (currently empty) + +Your example analysis folder should contain your `.Rmd` and three empty folders (which won't be empty for long!). + +If the concept of a "file path" is unfamiliar to you; we recommend taking a look at our [section about file paths](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#an-important-note-about-file-paths-and-Rmds). + +# Using a different refine.bio dataset with this analysis? + +If you'd like to adapt an example analysis to use a different dataset from [refine.bio](https://www.refine.bio/), we recommend placing the files in the `data/` directory you created and changing the filenames and paths in the notebook to match these files (we've put comments to signify where you would need to change the code). +We suggest saving plots and results to `plots/` and `results/` directories, respectively, as these are automatically created by the notebook. +From here you can customize this analysis example to fit your own scientific questions and preferences. + +*** + +   + +# Gene set enrichment analysis - Microarray + +## Install libraries + +See our Getting Started page with [instructions for package installation](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#what-you-need-to-install) for a list of the other software you will need, as well as more tips and resources. + +In this analysis, we will be using [`clusterProfiler`](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) package to perform GSEA and the [`msigdbr`](https://cran.r-project.org/web/packages/msigdbr/index.html) package which contains gene sets from the [Molecular Signatures Database (MSigDB)](https://www.gsea-msigdb.org/gsea/msigdb/index.jsp) already in the tidy format required by `clusterProfiler` [@Yu2012; @Igor2020; @Subramanian2005]. + +We will also need the [`org.Dr.eg.db`](https://bioconductor.org/packages/release/data/annotation/html/org.Dr.eg.db.html) package to perform gene identifier conversion [@Carlson2019-zebrafish]. + +```{r} +if (!("clusterProfiler" %in% installed.packages())) { + # Install this package if it isn't installed yet + BiocManager::install("clusterProfiler", update = FALSE) +} + +if (!("msigdbr" %in% installed.packages())) { + # Install this package if it isn't installed yet + BiocManager::install("msigdbr", update = FALSE) +} + +if (!("org.Dr.eg.db" %in% installed.packages())) { + # Install this package if it isn't installed yet + BiocManager::install("org.Dr.eg.db", update = FALSE) +} +``` + +Attach the packages we need for this analysis. + +```{r} +# Attach the library +library(clusterProfiler) + +# Package that contains MSigDB gene sets in tidy format +library(msigdbr) + +# Zebrafish annotation package we'll use for gene identifier conversion +library(org.Dr.eg.db) + +# We will need this so we can use the pipe: %>% +library(magrittr) +``` + +## Import data + +We will read in the differential expression results we will download from online. +These results are from a zebrafish microarray experiment we used for [differential expression analysis for two groups](https://alexslemonade.github.io/refinebio-examples/02-microarray/differential-expression_microarray_02_2-groups.html) using [`limma`](https://bioconductor.org/packages/release/bioc/html/limma.html) [@Ritchie2015]. +The table contains summary statistics including Ensembl gene IDs, t-statistic values, and adjusted p-values (FDR in this case). +We can identify differentially regulated genes by filtering these results and use this list as input to GSEA. + +Instead of using the URL below, you can use a file path to a TSV file with your desired gene list results. +First we will assign the URL to its own variable called, `dge_url`. + +```{r} +# Define the url to your differential expression results file +dge_url <- "https://refinebio-examples.s3.us-east-2.amazonaws.com/02-microarray/results/GSE71270/GSE71270_limma_results.tsv" +``` + +Read in the file that has differential expression results. +Here we are using the URL we set up above, but this can be a local file path instead _i.e._ you can replace `dge_url` in the code below with a path to file you have on your computer like: `file.path("results", "GSE71270_limma_results.tsv")`. + +```{r} +# Read in the contents of your differential expression results file +# `dge_url` can be replaced with a file path to a TSV file with your +# desired gene list results +dge_df <- readr::read_tsv(dge_url) +``` + +`read_tsv()` can read TSV files online and doesn't necessarily require you download the file first. +Let's take a look at what these contrast results from the differential expression analysis look like. + +```{r} +dge_df +``` + +## Getting familiar with `clusterProfiler`'s options + +Let's take a look at what organisms the package supports. + +```{r} +msigdbr_species() +``` + +MSigDB contains 8 different gene set collections [@Subramanian2005]. + + H: hallmark gene sets + C1: positional gene sets + C2: curated gene sets + C3: motif gene sets + C4: computational gene sets + C5: GO gene sets + C6: oncogenic signatures + C7: immunologic signatures + +MSigDB includes a collection called Hallmark gene sets. +Here's an excerpt of the collection description [@Liberzon2015]: + +Hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression. +These gene sets were generated by a computational methodology based on identifying gene set overlaps and retaining genes that display coordinate expression. +The hallmarks reduce noise and redundancy and provide a better delineated biological space for GSEA. +We'll use the Hallmark collection for GSEA +Notably, there are only 50 gene sets included in this collection. +The fewer gene sets we test, the lower our multiple hypothesis testing burden. + +The data we're interested in here comes from zebrafish samples, so we can obtain just the gene sets relevant to _D. rerio_ by specifying `species = "Danio rerio"` and only the Hallmark gene sets by specifying `category = "H"` to the `msigdbr()` function. + +```{r} +dr_hallmark_df <- msigdbr( + species = "Danio rerio", # Replace with species name relevant to your data + category = "H" +) +``` + +If you want a less curated dataset, run the chunk above without specifying a `category` to the `msigdbr()` function. + +In our differential expression results data frame, `dge_df` we have Ensembl gene identifiers. +So we will need to convert our Ensembl IDs into either gene symbols or Entrez IDs for GSEA. + +## Gene identifier conversion + +We're going to convert our identifiers in `dge_df` to Entrez IDs, but you can, with the change of a single argument, use the same code to convert to many other types of identifiers! + +The annotation package `org.Dr.eg.db` contains information for different identifiers [@Carlson2019-zebrafish]. +`org.Dr.eg.db` is specific to _Danio rerio_ -- this is what the `Dr` in the package name is referencing. + +We can see what types of IDs are available to us in an annotation package with `keytypes()`. + +```{r} +keytypes(org.Dr.eg.db) +``` + +Even though we'll use this package to convert from Ensembl gene IDs (`ENSEMBL`) to Entrez IDs (`ENTREZID`), we could just as easily use it to convert from an Ensembl transcript ID (`ENSEMBLTRANS`) to gene symbols (`SYMBOL`). + +Take a look at our other gene identifier conversion examples for examples with different species and gene ID types: +[the microarray example](https://alexslemonade.github.io/refinebio-examples/02-microarray/gene-id-annotation_microarray_01_ensembl.html) and [the RNA-seq example](https://alexslemonade.github.io/refinebio-examples/03-rnaseq/gene-id-annotation_rnaseq_01_ensembl.html). + +The function we will use to map from Ensembl gene IDs to Entrez gene IDs is called `mapIds()`. + +Let's create a data frame that shows the mapped Entrez IDs along with the differential expression stats for the respective Ensembl IDs. + +```{r} +# First let's create a mapped data frame we can join to the differential expression stats +dge_mapped_df <- data.frame( + "entrez_id" = mapIds( + org.Dr.eg.db, # Replace with annotation package for the organism relevant to your data + keys = dge_df$Gene, + column = "ENTREZID", # Replace with the type of gene identifiers you would like to map to + keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data + multiVals = "first" # This will keep only the first mapped value for each Ensembl ID + ) +) %>% + # If an Ensembl gene identifier doesn't map to a Entrez gene identifier, drop that + # from the data frame + dplyr::filter(!is.na(entrez_id)) %>% # Make an `Ensembl` column to store the rownames + tibble::rownames_to_column("Ensembl") %>% # Now let's join the rest of the expression data + dplyr::inner_join(dge_df, by = c("Ensembl" = "Gene")) +``` + +This `1:many mapping between keys and columns` message means that some Ensembl gene identifiers map to multiple Entrez IDs. +In this case, it's also possible that a Entrez ID will map to multiple Ensembl IDs. +For the purpose of performing GSEA later in this notebook, we keep only the first mapped IDs. +For more about how to explore this, take a look at our [microarray gene ID conversion example](https://alexslemonade.github.io/refinebio-examples/02-microarray/gene-id-annotation_microarray_01_ensembl.html). + +Let's see a preview of `dge_mapped_df`. + +```{r} +head(dge_mapped_df) +``` + +Now let's check to see if we have any Entrez IDs that mapped to multiple Ensembl IDs. + +```{r} +any(duplicated(dge_mapped_df$entrez_id)) +``` + +Looks like we do have duplicated Entrez IDs. +Let's find out which Entrez IDs have been duplicated. + +```{r} +dge_mapped_df %>% + dplyr::filter(duplicated(entrez_id)) %>% + dplyr::pull(entrez_id) +``` + +Now let's take a look at the rows associated with one of the duplicated Entrez IDs. + +```{r} +dge_mapped_df %>% + dplyr::filter(entrez_id == "336702") +``` +We can see that the associated values vary for each row although the Entrez ID is the same. + +We do not want duplicated gene identifiers for the GSEA steps later, so let's keep the Entrez IDs associated with the higher t-statistic value. + +```{r} +filtered_dge_mapped_df <- dge_mapped_df %>% + # Sort so that highest t-statistic values are at the top + dplyr::arrange(dplyr::desc(t)) %>% + # Filter out the duplicated rows using `dplyr::distinct()`-- this will keep + # the first row with the duplicated value thus keeping the row with the + # highest t-statistic value + dplyr::distinct(entrez_id, .keep_all = TRUE) +``` + +Let's check to see that we removed the duplicate Entrez IDs and kept the row with the higher t-statistic value. + +```{r} +filtered_dge_mapped_df %>% + dplyr::filter(entrez_id == "336702") +``` + +Looks like we were able to successfully get rid of the duplicate gene identifiers and keep the observation with the higher t-statistic value! +Note however, that a caveat in using this approach is that the genes that have duplicate identifiers could be enriched in a particular pathway/gene set and we may get an overly optimistic view of how perturbed that pathway truly is. + +## Perform gene set enrichment analysis (GSEA) + +The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout the pre-ranked gene list or primarily found at the top or bottom of the list. +Genes are ranked based on phenotype so we expect that sets related to a distinct phenotype will tend to show the latter distribution [@clusterProfiler-book]. + +### Determine our pre-ranked genes list + +The `GSEA()` function takes a pre-ranked (sorted) named vector of statistics, where the names in the vector are gene identifiers. +This is step 1 -- gene-level statistics. + +```{r} +# Let's create a named vector ranked based on the t-statistic values -- we +# already sorted in the t-stastic values in descending order in the previous +# chunk so we won't need to do that here +t_vector <- filtered_dge_mapped_df$t +names(t_vector) <- filtered_dge_mapped_df$entrez_id +``` + +```{r} +# Look at first entries of the ranked t-statistic vector +head(t_vector) +``` + +### Run GSEA using the `GSEA()` function + +The enrichment score (ES) for a pathway, a pathway-level statistic, is calculated using our gene-level statistics. +Genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic. +A running score is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. +The ES is the maximum deviation from zero. +Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. +Normalized enrichment scores (NES) are enrichment scores that are scaled to make gene sets that contain different number of genes comparable [@Subramanian2005; @Korotkevich2019]. + +We can use the `GSEA()` function to perform GSEA with any generic set of gene sets, but there are several functions for using specific, commonly used gene sets (e.g., `gseKEGG()`). + +```{r} +gsea_results <- GSEA( + geneList = t_vector, # ordered ranked gene list + minGSSize = 25, # minimum gene set size + maxGSSize = 500, # maximum gene set set + pvalueCutoff = 0.05, # p value cutoff + eps = 0, # boundary for calculating the p value + pAdjustMethod = "BH", # Benjamini-Hochberg correction + TERM2GENE = dplyr::select( + dr_hallmark_df, + gs_name, + entrez_gene + ) +) +``` + +Let's take a look at the results. + +```{r} +# We can access the results from our gseaResult object using `@result` +head(gsea_results@result) +``` + +Looks like we have gene sets returned as significant at FDR of `0.05`. +If we didn't have any, our visualizations below would show up blank as nothing would have met our `pvalueCutoff` above. + +The information we're most likely interested in is in the `results` slot. +Let's convert this into a data frame that we can use for further analysis and write to file. + +```{r} +gsea_result_df <- data.frame(gsea_results@result) +``` + +## Visualizing results + +We can visualize GSEA results for individual pathways or gene sets using `enrichplot::gseaplot()`. +Let's take a look at 2 different pathways -- one with a highly positive NES and one with a highly negative NES -- to get more insight into how ES are calculated. + +### Highly Positive NES + +Let's look for the gene set with the highest positive NES. + +```{r} +gsea_result_df %>% + # Combining the `dplyr::desc()` function with `dplyr::arrange()` sorts the + # values in descending order + dplyr::arrange(dplyr::desc(NES)) +``` + +The gene set `HALLMARK_TNFA_SIGNALING_VIA_NFKB` has the highest NES score. + +```{r} +enrichplot::gseaplot(gsea_results, + geneSetID = "HALLMARK_TNFA_SIGNALING_VIA_NFKB", + title = "HALLMARK_TNFA_SIGNALING_VIA_NFKB", + color.line = "#0d76ff" +) +``` + +Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. + +The plots returned by `enrichplot::gseaplot` are ggplots, so we can use `ggplot2::ggsave()` to save them to file. + +Let's save to PNG. + +```{r} +ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"), + plot = ggplot2::last_plot() +) +``` + +### Highly Negative NES + +Let's look for the gene set with the highest negative NES. + +```{r} +gsea_result_df %>% + # The `dplyr::arrange()` function sorts the values in ascending order + dplyr::arrange(NES) +``` + +The gene set `HALLMARK_E2F_TARGETS` has a highly negative NES. + +```{r} +enrichplot::gseaplot(gsea_results, + geneSetID = "HALLMARK_E2F_TARGETS", + title = "HALLMARK_E2F_TARGETS", + color.line = "#0d76ff" +) +``` + +This gene set shows the opposite pattern -- genes in the pathway tend to be on the right side of the graph. + +Let's save this plot to PNG. + +```{r} +ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"), + plot = ggplot2::last_plot() +) +``` + +## Write results to file + +```{r} +readr::write_tsv( + gsea_result_df, + file.path( + results_dir, + "GSE71270_gsea_results.tsv" + ) +) +``` + +# Resources for further learning + +- [clusterProfiler paper](https://doi.org/10.1089/omi.2011.0118) [@Yu2012]. +- [clusterProfiler book](https://yulab-smu.github.io/clusterProfiler-book/index.html) [@clusterProfiler-book]. +- [This handy review](https://doi.org/10.1371/journal.pcbi.1002375) which summarizes the different types of pathway analysis and their limitations [@Khatri2012]. +- See this [Broad Institute page](https://www.gsea-msigdb.org/gsea/index.jsp) for more on GSEA and MSigDB [@GSEA-broad-institute]. + +# Session info + +At the end of every analysis, before saving your notebook, we recommend printing out your session info. +This helps make your code more reproducible by recording what versions of software and packages you used to run this. + +```{r} +# Print session info +sessioninfo::session_info() +``` + +# References diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html new file mode 100644 index 00000000..a183df43 --- /dev/null +++ b/02-microarray/pathway-analysis_microarray_03_gsea.html @@ -0,0 +1,4438 @@ + + + + + + + + + + + + + + +Gene set enrichment analysis - Microarray + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    + + + +
    +
    +
    +
    +
    + +
    + + + + + + + + + +
    +

    1 Purpose of this analysis

    +

    This example is one of pathway analysis module set, we recommend looking at the pathway analysis introduction to help you determine which pathway analysis method is best suited for your purposes.

    +

    This particular example analysis shows how you can use gene set enrichment analysis (GSEA) to detect situations where all genes in a predefined set change in a coordinated way, detecting even small statistical but coordinated changes between two biological states.

    +

    ⬇️ Jump to the analysis code ⬇️

    +
    +
    +

    2 How to run this example

    +

    For general information about our tutorials and the basic software packages you will need, please see our ‘Getting Started’ section. We recommend taking a look at our Resources for Learning R if you have not written code in R before.

    +
    +

    2.1 Obtain the .Rmd file

    +

    To run this example yourself, download the .Rmd for this analysis by clicking this link.

    +

    Clicking this link will most likely send this to your downloads folder on your computer. Move this .Rmd file to where you would like this example and its files to be stored.

    +

    You can open this .Rmd file in RStudio and follow the rest of these steps from there. (See our section about getting started with R notebooks if you are unfamiliar with .Rmd files.)

    +
    +
    +

    2.2 Set up your analysis folders

    +

    Good file organization is helpful for keeping your data analysis project on track! We have set up some code that will automatically set up a folder structure for you. Run this next chunk to set up your folders!

    +

    If you have trouble running this chunk, see our introduction to using .Rmds for more resources and explanations.

    +
    # Create the data folder if it doesn't exist
+if (!dir.exists("data")) {
+  dir.create("data")
+}
+
+# Define the file path to the plots directory
+plots_dir <- "plots" # Can replace with path to desired output plots directory
+
+# Create the plots folder if it doesn't exist
+if (!dir.exists(plots_dir)) {
+  dir.create(plots_dir)
+}
+
+# Define the file path to the results directory
+results_dir <- "results" # Can replace with path to desired output results directory
+
+# Create the results folder if it doesn't exist
+if (!dir.exists(results_dir)) {
+  dir.create(results_dir)
+}
    +

    In the same place you put this .Rmd file, you should now have three new empty folders called data, plots, and results!

    +
    +
    +

    2.3 Obtain the gene set for this example

    +

    In this example, we are using differential expression results table we obtained from an example analysis of zebrafish samples overexpressing human CREB experiment using limma (Ritchie et al. 2015). The table contains summary statistics including Ensembl gene IDs, t-statistic values, and adjusted p-values (FDR in this case).

    +

    We have provided this file for you and the code in this notebook will read in the results that are stored online, but if you’d like to follow the steps for obtaining this results file yourself, we suggest going through that differential expression analysis example.

    +
    +
    +

    2.4 About the dataset we are using for this example

    +

    For this example analysis, we will use this CREB overexpression zebrafish experiment (Tregnago et al. 2016). Tregnago et al. (2016) measured microarray gene expression of zebrafish samples overexpressing human CREB, as well as control samples.

    +
    +
    +

    2.5 Check out our file structure!

    +

    Your new analysis folder should contain:

    +
      +
    • The example analysis .Rmd you downloaded
      +
      • +
    • A folder called data (currently empty)
      • +
    • A folder for plots (currently empty)
      +
      • +
    • A folder for results (currently empty)
      • +
    +

    Your example analysis folder should contain your .Rmd and three empty folders (which won’t be empty for long!).

    +

    If the concept of a “file path” is unfamiliar to you; we recommend taking a look at our section about file paths.

    +
    +
    +
    +

    3 Using a different refine.bio dataset with this analysis?

    +

    If you’d like to adapt an example analysis to use a different dataset from refine.bio, we recommend placing the files in the data/ directory you created and changing the filenames and paths in the notebook to match these files (we’ve put comments to signify where you would need to change the code). We suggest saving plots and results to plots/ and results/ directories, respectively, as these are automatically created by the notebook. From here you can customize this analysis example to fit your own scientific questions and preferences.

    +
    + +

     

    +
    +
    +

    4 Gene set enrichment analysis - Microarray

    +
    +

    4.1 Install libraries

    +

    See our Getting Started page with instructions for package installation for a list of the other software you will need, as well as more tips and resources.

    +

    In this analysis, we will be using clusterProfiler package to perform GSEA and the msigdbr package which contains gene sets from the Molecular Signatures Database (MSigDB) already in the tidy format required by clusterProfiler (Yu et al. 2012; Dolgalev 2020; Subramanian et al. 2005).

    +

    We will also need the org.Dr.eg.db package to perform gene identifier conversion (Carlson 2019).

    +
    if (!("clusterProfiler" %in% installed.packages())) {
+  # Install this package if it isn't installed yet
+  BiocManager::install("clusterProfiler", update = FALSE)
+}
+
+if (!("msigdbr" %in% installed.packages())) {
+  # Install this package if it isn't installed yet
+  BiocManager::install("msigdbr", update = FALSE)
+}
+
+if (!("org.Dr.eg.db" %in% installed.packages())) {
+  # Install this package if it isn't installed yet
+  BiocManager::install("org.Dr.eg.db", update = FALSE)
+}
    +

    Attach the packages we need for this analysis.

    +
    # Attach the library
+library(clusterProfiler)
    +
    ##
    +
    ## clusterProfiler v3.16.1  For help: https://guangchuangyu.github.io/software/clusterProfiler
+## 
+## If you use clusterProfiler in published research, please cite:
+## Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
    +
    ## 
+## Attaching package: 'clusterProfiler'
    +
    ## The following object is masked from 'package:stats':
+## 
+##     filter
    +
    # Package that contains MSigDB gene sets in tidy format
+library(msigdbr)
+
+# Zebrafish annotation package we'll use for gene identifier conversion
+library(org.Dr.eg.db)
    +
    ## Loading required package: AnnotationDbi
    +
    ## Loading required package: stats4
    +
    ## Loading required package: BiocGenerics
    +
    ## Loading required package: parallel
    +
    ## 
+## Attaching package: 'BiocGenerics'
    +
    ## The following objects are masked from 'package:parallel':
+## 
+##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
+##     clusterExport, clusterMap, parApply, parCapply, parLapply,
+##     parLapplyLB, parRapply, parSapply, parSapplyLB
    +
    ## The following objects are masked from 'package:stats':
+## 
+##     IQR, mad, sd, var, xtabs
    +
    ## The following objects are masked from 'package:base':
+## 
+##     anyDuplicated, append, as.data.frame, basename, cbind, colnames,
+##     dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
+##     grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
+##     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
+##     rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
+##     union, unique, unsplit, which, which.max, which.min
    +
    ## Loading required package: Biobase
    +
    ## Welcome to Bioconductor
+## 
+##     Vignettes contain introductory material; view with
+##     'browseVignettes()'. To cite Bioconductor, see
+##     'citation("Biobase")', and for packages 'citation("pkgname")'.
    +
    ## Loading required package: IRanges
    +
    ## Loading required package: S4Vectors
    +
    ## 
+## Attaching package: 'S4Vectors'
    +
    ## The following object is masked from 'package:clusterProfiler':
+## 
+##     rename
    +
    ## The following object is masked from 'package:base':
+## 
+##     expand.grid
    +
    ## 
+## Attaching package: 'IRanges'
    +
    ## The following object is masked from 'package:clusterProfiler':
+## 
+##     slice
    +
    ## 
+## Attaching package: 'AnnotationDbi'
    +
    ## The following object is masked from 'package:clusterProfiler':
+## 
+##     select
    +
    ##
    +
    # We will need this so we can use the pipe: %>%
+library(magrittr)
    +
    +
    +

    4.2 Import data

    +

    We will read in the differential expression results we will download from online. These results are from a zebrafish microarray experiment we used for differential expression analysis for two groups using limma (Ritchie et al. 2015). The table contains summary statistics including Ensembl gene IDs, t-statistic values, and adjusted p-values (FDR in this case). We can identify differentially regulated genes by filtering these results and use this list as input to GSEA.

    +

    Instead of using the URL below, you can use a file path to a TSV file with your desired gene list results. First we will assign the URL to its own variable called, dge_url.

    +
    # Define the url to your differential expression results file
+dge_url <- "https://refinebio-examples.s3.us-east-2.amazonaws.com/02-microarray/results/GSE71270/GSE71270_limma_results.tsv"
    +

    Read in the file that has differential expression results. Here we are using the URL we set up above, but this can be a local file path instead i.e. you can replace dge_url in the code below with a path to file you have on your computer like: file.path("results", "GSE71270_limma_results.tsv").

    +
    # Read in the contents of your differential expression results file
+# `dge_url` can be replaced with a file path to a TSV file with your
+# desired gene list results
+dge_df <- readr::read_tsv(dge_url)
    +
    ## 
+## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────
+## cols(
+##   Gene = col_character(),
+##   logFC = col_double(),
+##   AveExpr = col_double(),
+##   t = col_double(),
+##   P.Value = col_double(),
+##   adj.P.Val = col_double(),
+##   B = col_double()
+## )
    +

    read_tsv() can read TSV files online and doesn’t necessarily require you download the file first. Let’s take a look at what these contrast results from the differential expression analysis look like.

    +
    dge_df
    +
    + +
    +
    +
    +

    4.3 Getting familiar with clusterProfiler’s options

    +

    Let’s take a look at what organisms the package supports.

    +
    msigdbr_species()
    +
    + +
    +

    MSigDB contains 8 different gene set collections (Subramanian et al. 2005).

    +
    H: hallmark gene sets
+C1: positional gene sets
+C2: curated gene sets
+C3: motif gene sets
+C4: computational gene sets
+C5: GO gene sets
+C6: oncogenic signatures
+C7: immunologic signatures
    +

    MSigDB includes a collection called Hallmark gene sets. Here’s an excerpt of the collection description (Liberzon et al. 2015):

    +

    Hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression. These gene sets were generated by a computational methodology based on identifying gene set overlaps and retaining genes that display coordinate expression. The hallmarks reduce noise and redundancy and provide a better delineated biological space for GSEA. We’ll use the Hallmark collection for GSEA Notably, there are only 50 gene sets included in this collection. The fewer gene sets we test, the lower our multiple hypothesis testing burden.

    +

    The data we’re interested in here comes from zebrafish samples, so we can obtain just the gene sets relevant to D. rerio by specifying species = "Danio rerio" and only the Hallmark gene sets by specifying category = "H" to the msigdbr() function.

    +
    dr_hallmark_df <- msigdbr(
+  species = "Danio rerio", # Replace with species name relevant to your data
+  category = "H"
+)
    +

    If you want a less curated dataset, run the chunk above without specifying a category to the msigdbr() function.

    +

    In our differential expression results data frame, dge_df we have Ensembl gene identifiers. So we will need to convert our Ensembl IDs into either gene symbols or Entrez IDs for GSEA.

    +
    +
    +

    4.4 Gene identifier conversion

    +

    We’re going to convert our identifiers in dge_df to Entrez IDs, but you can, with the change of a single argument, use the same code to convert to many other types of identifiers!

    +

    The annotation package org.Dr.eg.db contains information for different identifiers (Carlson 2019). org.Dr.eg.db is specific to Danio rerio – this is what the Dr in the package name is referencing.

    +

    We can see what types of IDs are available to us in an annotation package with keytypes().

    +
    keytypes(org.Dr.eg.db)
    +
    ##  [1] "ACCNUM"       "ALIAS"        "ENSEMBL"      "ENSEMBLPROT"  "ENSEMBLTRANS"
+##  [6] "ENTREZID"     "ENZYME"       "EVIDENCE"     "EVIDENCEALL"  "GENENAME"    
+## [11] "GO"           "GOALL"        "IPI"          "ONTOLOGY"     "ONTOLOGYALL" 
+## [16] "PATH"         "PFAM"         "PMID"         "PROSITE"      "REFSEQ"      
+## [21] "SYMBOL"       "UNIGENE"      "UNIPROT"      "ZFIN"
    +

    Even though we’ll use this package to convert from Ensembl gene IDs (ENSEMBL) to Entrez IDs (ENTREZID), we could just as easily use it to convert from an Ensembl transcript ID (ENSEMBLTRANS) to gene symbols (SYMBOL).

    +

    Take a look at our other gene identifier conversion examples for examples with different species and gene ID types: the microarray example and the RNA-seq example.

    +

    The function we will use to map from Ensembl gene IDs to Entrez gene IDs is called mapIds().

    +

    Let’s create a data frame that shows the mapped Entrez IDs along with the differential expression stats for the respective Ensembl IDs.

    +
    # First let's create a mapped data frame we can join to the differential expression stats
+dge_mapped_df <- data.frame(
+  "entrez_id" = mapIds(
+    org.Dr.eg.db, # Replace with annotation package for the organism relevant to your data
+    keys = dge_df$Gene,
+    column = "ENTREZID", # Replace with the type of gene identifiers you would like to map to
+    keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data
+    multiVals = "first" # This will keep only the first mapped value for each Ensembl ID
+  )
+) %>%
+  # If an Ensembl gene identifier doesn't map to a Entrez gene identifier, drop that
+  # from the data frame
+  dplyr::filter(!is.na(entrez_id)) %>% # Make an `Ensembl` column to store the rownames
+  tibble::rownames_to_column("Ensembl") %>% # Now let's join the rest of the expression data
+  dplyr::inner_join(dge_df, by = c("Ensembl" = "Gene"))
    +
    ## 'select()' returned 1:many mapping between keys and columns
    +

    This 1:many mapping between keys and columns message means that some Ensembl gene identifiers map to multiple Entrez IDs. In this case, it’s also possible that a Entrez ID will map to multiple Ensembl IDs. For the purpose of performing GSEA later in this notebook, we keep only the first mapped IDs. For more about how to explore this, take a look at our microarray gene ID conversion example.

    +

    Let’s see a preview of dge_mapped_df.

    +
    head(dge_mapped_df)
    +
    + +
    +

    Now let’s check to see if we have any Entrez IDs that mapped to multiple Ensembl IDs.

    +
    any(duplicated(dge_mapped_df$entrez_id))
    +
    ## [1] TRUE
    +

    Looks like we do have duplicated Entrez IDs. Let’s find out which Entrez IDs have been duplicated.

    +
    dge_mapped_df %>%
+  dplyr::filter(duplicated(entrez_id)) %>%
+  dplyr::pull(entrez_id)
    +
    ## [1] "336702" "57924"
    +

    Now let’s take a look at the rows associated with one of the duplicated Entrez IDs.

    +
    dge_mapped_df %>%
+  dplyr::filter(entrez_id == "336702")
    +
    + +
    +

    We can see that the associated values vary for each row although the Entrez ID is the same.

    +

    We do not want duplicated gene identifiers for the GSEA steps later, so let’s keep the Entrez IDs associated with the higher t-statistic value.

    +
    filtered_dge_mapped_df <- dge_mapped_df %>%
+  # Sort so that highest t-statistic values are at the top
+  dplyr::arrange(dplyr::desc(t)) %>%
+  # Filter out the duplicated rows using `dplyr::distinct()`-- this will keep
+  # the first row with the duplicated value thus keeping the row with the
+  # highest t-statistic value
+  dplyr::distinct(entrez_id, .keep_all = TRUE)
    +

    Let’s check to see that we removed the duplicate Entrez IDs and kept the row with the higher t-statistic value.

    +
    filtered_dge_mapped_df %>%
+  dplyr::filter(entrez_id == "336702")
    +
    + +
    +

    Looks like we were able to successfully get rid of the duplicate gene identifiers and keep the observation with the higher t-statistic value! Note however, that a caveat in using this approach is that the genes that have duplicate identifiers could be enriched in a particular pathway/gene set and we may get an overly optimistic view of how perturbed that pathway truly is.

    +
    +
    +

    4.5 Perform gene set enrichment analysis (GSEA)

    +

    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout the pre-ranked gene list or primarily found at the top or bottom of the list. Genes are ranked based on phenotype so we expect that sets related to a distinct phenotype will tend to show the latter distribution (Yu).

    +
    +

    4.5.1 Determine our pre-ranked genes list

    +

    The GSEA() function takes a pre-ranked (sorted) named vector of statistics, where the names in the vector are gene identifiers. This is step 1 – gene-level statistics.

    +
    # Let's create a named vector ranked based on the t-statistic values -- we
+# already sorted in the t-stastic values in descending order in the previous
+# chunk so we won't need to do that here
+t_vector <- filtered_dge_mapped_df$t
+names(t_vector) <- filtered_dge_mapped_df$entrez_id
    +
    # Look at first entries of the ranked t-statistic vector
+head(t_vector)
    +
    ##   555053   140633   407728   368924   335916   323329 
+## 20.22172 14.48634 13.88657 12.45258 11.24450 10.92140
    +
    +
    +

    4.5.2 Run GSEA using the GSEA() function

    +

    The enrichment score (ES) for a pathway, a pathway-level statistic, is calculated using our gene-level statistics. Genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic. A running score is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. The ES is the maximum deviation from zero. Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Normalized enrichment scores (NES) are enrichment scores that are scaled to make gene sets that contain different number of genes comparable (Korotkevich et al. 2019; Subramanian et al. 2005).

    +

    We can use the GSEA() function to perform GSEA with any generic set of gene sets, but there are several functions for using specific, commonly used gene sets (e.g., gseKEGG()).

    +
    gsea_results <- GSEA(
+  geneList = t_vector, # ordered ranked gene list
+  minGSSize = 25, # minimum gene set size
+  maxGSSize = 500, # maximum gene set set
+  pvalueCutoff = 0.05, # p value cutoff
+  eps = 0, # boundary for calculating the p value
+  pAdjustMethod = "BH", # Benjamini-Hochberg correction
+  TERM2GENE = dplyr::select(
+    dr_hallmark_df,
+    gs_name,
+    entrez_gene
+  )
+)
    +
    ## preparing geneSet collections...
    +
    ## GSEA analysis...
    +
    ## leading edge analysis...
    +
    ## done...
    +

    Let’s take a look at the results.

    +
    # We can access the results from our gseaResult object using `@result`
+head(gsea_results@result)
    +
    + +
    +

    Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    +

    The information we’re most likely interested in is in the results slot. Let’s convert this into a data frame that we can use for further analysis and write to file.

    +
    gsea_result_df <- data.frame(gsea_results@result)
    +
    +
    +
    +

    4.6 Visualizing results

    +

    We can visualize GSEA results for individual pathways or gene sets using enrichplot::gseaplot(). Let’s take a look at 2 different pathways – one with a highly positive NES and one with a highly negative NES – to get more insight into how ES are calculated.

    +
    +

    4.6.1 Highly Positive NES

    +

    Let’s look for the gene set with the highest positive NES.

    +
    gsea_result_df %>%
+  # Combining the `dplyr::desc()` function with `dplyr::arrange()` sorts the
+  # values in descending order
+  dplyr::arrange(dplyr::desc(NES))
    +
    + +
    +

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest NES score.

    +
    enrichplot::gseaplot(gsea_results,
+  geneSetID = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
+  title = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
+  color.line = "#0d76ff"
+)
    +

    +

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores.

    +

    The plots returned by enrichplot::gseaplot are ggplots, so we can use ggplot2::ggsave() to save them to file.

    +

    Let’s save to PNG.

    +
    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
+  plot = ggplot2::last_plot()
+)
    +
    ## Saving 7 x 5 in image
    +
    +
    +

    4.6.2 Highly Negative NES

    +

    Let’s look for the gene set with the highest negative NES.

    +
    gsea_result_df %>%
+  # The `dplyr::arrange()` function sorts the values in ascending order
+  dplyr::arrange(NES)
    +
    + +
    +

    The gene set HALLMARK_E2F_TARGETS has a highly negative NES.

    +
    enrichplot::gseaplot(gsea_results,
+  geneSetID = "HALLMARK_E2F_TARGETS",
+  title = "HALLMARK_E2F_TARGETS",
+  color.line = "#0d76ff"
+)
    +

    +

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph.

    +

    Let’s save this plot to PNG.

    +
    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
+  plot = ggplot2::last_plot()
+)
    +
    ## Saving 7 x 5 in image
    +
    +
    +
    +

    4.7 Write results to file

    +
    readr::write_tsv(
+  gsea_result_df,
+  file.path(
+    results_dir,
+    "GSE71270_gsea_results.tsv"
+  )
+)
    +
    +
    +
    +

    5 Resources for further learning

    + +
    +
    +

    6 Session info

    +

    At the end of every analysis, before saving your notebook, we recommend printing out your session info. This helps make your code more reproducible by recording what versions of software and packages you used to run this.

    +
    # Print session info
+sessioninfo::session_info()
    +
    ## ─ Session info ───────────────────────────────────────────────────────────────
+##  setting  value                       
+##  version  R version 4.0.2 (2020-06-22)
+##  os       Ubuntu 20.04 LTS            
+##  system   x86_64, linux-gnu           
+##  ui       X11                         
+##  language (EN)                        
+##  collate  en_US.UTF-8                 
+##  ctype    en_US.UTF-8                 
+##  tz       Etc/UTC                     
+##  date     2020-11-05                  
+## 
+## ─ Packages ───────────────────────────────────────────────────────────────────
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+## 
+## [1] /usr/local/lib/R/site-library
+## [2] /usr/local/lib/R/library
    +
    +
    +

    References

    +
    +
    +

    Carlson M., 2019 Genome wide annotation for zebrafish. https://bioconductor.org/packages/release/data/annotation/html/org.Dr.eg.db.html

    +
    +
    +

    Diego U. S., and B. I. Team, GSEA: Gene set enrichment analysis. https://www.gsea-msigdb.org/gsea/index.jsp

    +
    +
    +

    Dolgalev I., 2020 Msigdbr: MSigDB gene sets for multiple organisms in a tidy data format. https://cran.r-project.org/web/packages/msigdbr/index.html

    +
    +
    +

    Khatri P., M. Sirota, and A. J. Butte, 2012 Ten years of pathway analysis: Current approaches and outstanding challenges. PLOS Computational Biology 8: e1002375. https://doi.org/10.1371/journal.pcbi.1002375

    +
    +
    +

    Korotkevich G., V. Sukhov, and A. Sergushichev, 2019 Fast gene set enrichment analysis. bioRxiv. https://doi.org/10.1101/060012

    +
    +
    +

    Liberzon A., C. Birger, H. Thorvaldsdóttir, M. Ghandi, and J. P. Mesirov et al., 2015 The molecular signatures database hallmark gene set collection. Cell Systems 1. https://doi.org/10.1016/j.cels.2015.12.004

    +
    +
    +

    Ritchie M. E., B. Phipson, D. Wu, Y. Hu, and C. W. Law et al., 2015 limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43: e47. https://doi.org/10.1093/nar/gkv007

    +
    +
    +

    Subramanian A., P. Tamayo, V. K. Mootha, S. Mukherjee, and B. L. Ebert et al., 2005 Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences 102: 15545–15550. https://doi.org/10.1073/pnas.0506580102

    +
    +
    +

    Tregnago C., E. Manara, M. Zampini, V. Bisio, and C. Borga et al., 2016 CREB engages C/EBPδ to initiate leukemogenesis. Leukemia 30: 1887–1896. https://doi.org/10.1038/leu.2016.98

    +
    +
    +

    Yu G., L.-G. Wang, Y. Han, and Q.-Y. He, 2012 clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology 16: 284–287. https://doi.org/10.1089/omi.2011.0118

    +
    +
    +

    Yu G., clusterProfiler: Universal enrichment tool for functional and comparative study. http://yulab-smu.top/clusterProfiler-book/index.html

    +
    +
    +
    + + + + +
    +
    + +
    + + + + + + + + + + + + + + + + diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 71fc5447..9f950be9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -31,11 +31,18 @@ - [Mechanics of the rendering](#mechanics-of-the-rendering) - [How to re-render the notebooks locally](#how-to-re-render-the-notebooks-locally) - [Run snakemake without queueing up a web browser for the Docker container](#run-snakemake-without-queueing-up-a-web-browser-for-the-docker-container) - - [Automatic rendering using GitHub actions](#automatic-rendering-using-github-actions) +- [Pull Requests](#pull-requests) + - [Add to the testing branch: merges to staging](#add-to-the-testing-branch-merges-to-staging) + - [Publish: staged changes merged to master](#publish-staged-changes-merged-to-master) + - [Publish a change, but quickly: direct merges to master, hotfixes](#publish-a-change-but-quickly-direct-merges-to-master-hotfixes) + - [A summary of types of PRs.](#a-summary-of-types-of-prs) + - [Github actions](#github-actions) + - [Automatic Spell checking and styling](#automatic-spell-checking-and-styling) + - [Automatic Docker image and rendering](#automatic-docker-image-and-rendering) + - [Automatic rendering](#automatic-rendering) - [About the render-notebooks.R script](#about-the-render-notebooksr-script) - [Add new analyses to the Snakefile](#add-new-analyses-to-the-snakefile) - [Add new analyses to the navbar](#add-new-analyses-to-the-navbar) -- [Pull request status checks](#pull-request-status-checks) @@ -172,6 +179,9 @@ These analyses follow the [Google R Style Guide](https://google.github.io/styleg Snakemake will automatically runs the [r-lib/styler package](https://github.com/r-lib/styler) on each `.Rmd` file called in the `Snakefile`. This will help fix some spacing and formatting issues automatically. +Github actions will also automatically run styler and commit back to your branch any changes. +See the [Github actions section](#github-actions) for more info on how the automation steps for this works. + #### Session Info `sessioninfo::session_info()` should always be printed out at the end. @@ -284,7 +294,7 @@ Had no year associated with it, so it has keywords for its tag `pca-visually-exp #### Spell checking -Spell checks are run automatically using GitHub actions upon opening a PR for master or prior to merging to master. +Spell checks are [run automatically using GitHub actions](#github-actions) upon opening a PR for `master` or `staging`. GitHub actions will abort if there are more than 2 spelling errors and you will need to fix those before continuing. You can obtain the list of spelling errors on GitHub by going to `Actions` and clicking the workflow of PR you are working on. Click on the `style-n-check` step and in the upper right hand corner, there is a button that says "Artifacts" which should list a file called `spell-check-results`. @@ -300,11 +310,11 @@ If you want to run a spell check of all `.Rmd` files locally, you can use run `R ### Mechanics of the rendering -The `Snakefile` calls the `scripts/render-notebooks.R` which renders the `.html` files but leaves these `.Rmd` files ready for download and use [without the `pandoc` error](https://github.com/AlexsLemonade/refinebio-examples/pull/148#issuecomment-669170681). +The [`Snakefile`](#add-new-analyses-to-the-snakefile) calls the [`scripts/render-notebooks.R`](#about-the-render-notebooksr-script) which renders the `.html` files but leaves these `.Rmd` files ready for download and use [without the `pandoc` error](https://github.com/AlexsLemonade/refinebio-examples/pull/148#issuecomment-669170681). However, the `snakemake` workflow should also be run locally during development so that the author and reviewers can see the rendered output of the new material during the `Pull Request` process. Ideally snakemake will not re-render the `.html` for `.Rmd` files you have not edited, but if it does, you should only commit and push the files you have intended to change. -All `.html` files will be re-rendered upon merging to master, but by not committing files that are only altered incidentally, the `Files changed` page of your PR on GitHub will be more focused, easing the burden on reviewers. +All `.html` files will be re-rendered upon merging to `master` or `staging`, but by not committing files that are only altered incidentally, the `Files changed` page of your PR on GitHub will be more focused, easing the burden on reviewers. ### How to re-render the notebooks locally @@ -329,11 +339,137 @@ If you already have the `refinebio-examples` docker image: docker run --mount type=bind,target=/home/rstudio,source=$PWD ccdl/refinebio-examples snakemake --cores 4 ``` -### Automatic rendering using GitHub actions +## Pull Requests + +There are two protected branches in this repo: + +`staging` - where changes are initially sent for testing and previewing. +`master` - where changes that were tested are made user-facing. + +This two branch set up allows us to make incremental changes that we can test before publishing to our user-facing github pages. +It also means that pull requests have some extra methodology. + +#### Add to the testing branch: merges to staging + +After you've prepared the new or altered material for this repository, you can file a pull request to the `staging` branch this is the default branch for this repository so you should not have to change anything. +The `staging` branch does not feed into the published, user-facing material so you can feel free to make your pull requests as iterative and incremental as it is useful. +Use `squash and merge` when merging these pull requests into `staging` after approval. +`squash and merge` will be helpful for us if we need to "cherry pick" commits if particular project or example is not yet ready for user-facing but other material is. + +After merging a pull request into `staging`, check that all the changes are correct by reviewing the newly generated pages using html preview. +Go here and navigate to the pertinent pages that you've changed. +`http://htmlpreview.github.io/?https://github.com/AlexsLemonade/refinebio-examples/gh-pages-stages/01-getting-started/getting-started.html` +You may also want to spot-check other pages to be sure that there have not been inadvertent or unexpected changes introduced. + +#### Publish: staged changes merged to master + +Once a set of changes that are merged to `staging` are ready to be published, you can file a merge to `master` pull request. + +These types of PRs should only involve well-polished and ready for the public material. + +**Scenario 1: All changes from staging should be published** + +- Create a new branch from the most up-to-date `master` branch, call it `publish-mychanges` where `mychanges` is a short tag relevant to the changes. +- Checkout your newly created `publish` branch. +- If you are certain that all changes in `staging` should be carried over to `master` (aka published to Github pages), then you can do a `git merge staging` into your new branch. +- Now you can use the new branch to create the pull request to master as you would normally do. +- Try to be as specific as possible about what PRs (and by relation, their commits) you are requesting to merge to `master`. + + + +In this example above, the blue project is ready to be published and there are no other incomplete projects that have been merged in, so all commits from `staging` are wanted to be brought over `master`. + +By checking out a separate `publish` branch, we can resolve any merge conflicts in this `publish` branch so we don't do a bad thing by committing conflict resolutions directly to `staging`. +This also provides us with a "snapshot" if merges are continuing to happen to `staging` on other PRs -- this can make review hard if it keeps changing. + +**Scenario 2: Only some changes from staging should be published** + +- Create a new branch from the most up-to-date `master` branch, call it `publish-mychanges` where `mychanges` is a short tag relevant to the changes. +- Checkout your newly created `publish` branch. +- For each commit that needs to be published, add it to your new branch by using `git cherry-pick `. +Or in GitKraken, you can right click on the commit and choose `Cherry pick commit`. +- Now you can use the new branch to [create the pull request where the `master` branch](https://docs.github.com/en/free-pro-team@latest/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request#changing-the-branch-range-and-destination-repository) is the base branch. +- Try to be as specific as possible about what PRs (and by relation, their commits) you are requesting to merge to `master`. + + + +In this example above, the blue project is ready to be published, but the red project is not. +So in this scenario, we would cherry pick both commits from the blue project but ignore the one commit from the incomplete red project. + +This allows us to move forward changes from `staging` that are ready to be public facing even if other changes aren't ready (or if there isn't great timing for when the other changes will be ready). + +_Tips for getting commits ids_ +To perform the cherry picks, you will need the commit ids for finished projects only. +GitKraken shows commits, but sometimes I find it hard to follow which commits belong to which branch. +If you checkout `staging` and use `git log` you can see all the most recent commits for the `staging` branch. +If you want to save it to a file for easy browsing and copy/pasting you can use this command `git log --pretty=format:'%h was %an, %ar, message: %s' > git.log` + +#### Publish a change, but quickly: direct merges to master, hotfixes + +In (hopefully rare) scenarios where something that has already been published is noted to be broken and should be addressed quickly, [hotfix branch](https://nvie.com/posts/a-successful-git-branching-model/#hotfix-branches) pull requests are allowed. +These PRs should only be fairly small PRs and not anything that would require intense review. +This more for situations where "this is broken and here's a fix". + +- Create a new branch from the most up-to-date `master` branch and call it `hotfix-mybug`, where where `mybug` is a short tag relevant to the changes. +- Checkout your newly created `hotfix` branch. +- Make the hotfix change. +- Create a [pull request with `master` branch as the base branch](https://docs.github.com/en/free-pro-team@latest/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request#changing-the-branch-range-and-destination-repository). +- After your PR to `master` is approved and merged, merge the most up-to-date `staging` branch into your `hotfix` branch. +- File a second PR for `hotfix` but this time with [`staging` as the base branch](https://docs.github.com/en/free-pro-team@latest/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request#changing-the-branch-range-and-destination-repository). + + + +In this scenario, we start by making a branch from `master` and make the hotfix change on this `hotfix` branch. +Once the change is approved and merged into `master` from the first PR, we start the second PR and merge in `staging` to our `hotfix` branch. +This makes sure we don't accidentally undo any changes in `staging` that have not made it to `master` at this time. + +#### A summary of types of PRs. + +- `some-branch` -> `staging` -> NOT published to user-facing content. +This will be how most material is prepared so we can be more incremental with our changes and ultimately make sure only the very polished material is made user-facing. + +- Group of changes in `staging` -> `master` -> published to user-facing. +This is for when the updates from the previous kinds of PRs are "ready for prime time". + +- Hotfix PR: `hotfix` -> `master` -> published to user-facing. +For "this-is-broken" type changes that should be hastened to the user-facing content. +This requires a follow up pull request and merge to `staging`. + +### Github actions + +This repository has a lot of little moving parts, so to help make sure changes are where they are supposed to be and that some items aren't missed, we use Github actions to automate things where we can. + +Github action workflows are initiated either when a pull request is started or when a merge to `master` or `staging` is initiated. +The following sections explain more details about which Github actions happen when and what they do. + +#### Automatic Spell checking and styling + +When pull requests are initiated, spell check and styler are run by Github actions. + + + +If the styling introduces changes to the files, these changes will be committed back to your branch. +However if there are no styling changes, no commit will be made. + +If spell check finds more than 2 errors, Github actions will fail. +See the [spell check section](#spell-checking) for instructions on how to see your spelling errors and otherwise use spell check. + +#### Automatic Docker image and rendering + +After a pull request to `staging` or `master` branch is approved and a merge to one of these branches has been initiated, a sequence of Github actions makes sure that the rendered html files are pushed to the correct branch and that the updated docker image is pushed to Dockerhub. + + + +See the [Docker](#docker-for-refinebio-examples) and the next section about automatic rendering for more on how these steps are conducted. + +#### Automatic rendering This repository uses [snakemake](https://snakemake.readthedocs.io/en/stable/getting_started/installation.html) to render all notebooks. -All notebooks are automatically re-rendered by GitHub actions upon merges to master. -The newly rendered html files are all pushed to the `gh-pages` branch which will publish the material to https://alexslemonade.github.io/refinebio-examples/. +All notebooks are automatically re-rendered by GitHub actions upon merges to `master` or `staging`. +The newly rendered html files are all pushed to the `gh-pages` branch (if merging to `master`) and `gh-pages-stages` branch (if merging to `staging`). + +The `gh-pages` branch is where material is publshed to https://alexslemonade.github.io/refinebio-examples/. +The `gh-pages-stages` staging branch can be viewed using html preview at `http://htmlpreview.github.io/?https://github.com/AlexsLemonade/refinebio-examples/gh-pages-stages/01-getting-started/getting-started.html` If this automatic rendering fails, you will see a failed check at at the bottom of your PR on GitHub (and probably an email). You can see the details of this error on by going to `Actions` and clicking the workflow of PR you are working on that also says `Build Docker` underneath. @@ -343,6 +479,7 @@ Hopefully the error message helps you track down the problem, but you can also c ### About the render-notebooks.R script The `render-notebooks.R` script adds a `bibliography:` specification in the `.Rmd` header so all citations are automatically rendered. +It also adds other components like CSS styling, a footer, and Google Analytics (these items are all hard-coded into the script). **Options:** - `--rmd`: provided by snakemake, the input `.Rmd` file to render. @@ -377,11 +514,3 @@ Follow these steps to add the `.html` link to the navigation bar upon rendering. 6) Replace `tech-section`, `analysis_file_name` with the corresponding file names. 7) Save the file! 8) After you [render the notebook with snakemake](#rendering-notebooks), test the link to make sure it works. - -## Pull request status checks - -To require that branches are up-to-date with `master` before merging, we need to require that a status check passes before merging to `master`. -Turning on this setting mitigates the risk that changes that have been merged will be undone by a pull request that was filed first and alters the same file. -The status check used is a GitHub Action that test builds the docker image. -Most of the time, this should pull cached docker layers, so this will complete in a matter of minutes. -As a bonus, this process also checks that any changes to the Dockerfile result in a buildable image. diff --git a/Snakefile b/Snakefile index 4909b910..7fe64d3f 100644 --- a/Snakefile +++ b/Snakefile @@ -9,6 +9,7 @@ rule target: "02-microarray/dimension-reduction_microarray_02_umap.html", "02-microarray/gene-id-annotation_microarray_01_ensembl.html", "02-microarray/pathway-analysis_microarray_02_ora.html", + "02-microarray/pathway-analysis_microarray_03_gsea.html", 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z5cu@qwo88M?${TbIDfT?YIwGJLV?ud?Ah5ACI*Xck-dCF?9!(#l`)AoC<)udeq_Qe^qt+26S?+l4IlwaPPrZEC0`#SVT z{=;Mmj9CAgLBB%SWAo~!u$A5V9NKbmK0=uJKN|%2W|YviWlFgC;^ZSyRtFQH15elZ&PlBY6oM!p*vuI3<(L1l z&=V4T%uLRIOc$^|@f1L+t)u;K|JO6oz-Q9=*C|362%TcH2| literal 0 HcmV?d00001 diff --git a/components/references.bib b/components/references.bib index 79623326..b198d56a 100644 --- a/components/references.bib +++ b/components/references.bib @@ -195,6 +195,12 @@ @article{Gray2015 url = {https://pubmed.ncbi.nlm.nih.gov/25361968/} } +@website{GSEA-broad-institute, + title = {{GSEA}: Gene Set Enrichment Analysis}, + author = {UC San Diego and Broad Institute Team}, + url = {https://www.gsea-msigdb.org/gsea/index.jsp} +} + @article{Gu2016, title = {Complex heatmaps reveal patterns and correlations in multidimensional genomic data}, author = {Zuguang Gu and Roland Eils and Matthias Schlesner}, @@ -315,6 +321,16 @@ @manual{Konopka2020 url = {https://cran.r-project.org/web/packages/umap/umap.pdf} } +@article{Korotkevich2019, + author = {Gennady Korotkevich and Vladimir Sukhov and Alexey Sergushichev}, + title = {Fast gene set enrichment analysis}, + journal = {bioRxiv}, + year = {2019}, + number = {060012}, + doi = {10.1101/060012}, + url = {https://doi.org/10.1101/060012} +} + @website{LCSciences2014, title = {Microarray or {RNA} Sequencing?}, author = {LCSciences}, @@ -323,6 +339,17 @@ @website{LCSciences2014 url = {https://www.lcsciences.com/news/microarray-or-rna-sequencing/} } +@article{Liberzon2015, + title = {The Molecular Signatures Database Hallmark Gene Set Collection}, + author = {Arthur Liberzon and Chet Birger and Helga Thorvaldsdóttir and Mahmoud Ghandi and Jill P. Mesirov and Pablo Tamayo}, + year = {2015}, + journal = {Cell Systems}, + volume = {1}, + number = {6}, + doi = {10.1016/j.cels.2015.12.004}, + url = {https://dx.doi.org/10.1016%2Fj.cels.2015.12.004} +} + @article{Love2014, title = {Moderated estimation of fold change and dispersion for {RNA-Seq} data with {DESeq2}}, author = {Michael I. Love and Wolfgang Huber and Simon Anders}, diff --git a/scripts/render-notebooks.R b/scripts/render-notebooks.R index 8521d438..8de069aa 100644 --- a/scripts/render-notebooks.R +++ b/scripts/render-notebooks.R @@ -113,6 +113,9 @@ readr::write_lines(new_lines, tmp_file) # Declare path to google analytics bit google_analytics_file <- normalizePath(file.path("components", "google-analytics.html")) +# Declare path to footer +footer_file <- normalizePath(file.path("components", "footer.html")) + # Render the header added notebook rmarkdown::render(tmp_file, output_format = rmarkdown::html_document( @@ -121,7 +124,8 @@ rmarkdown::render(tmp_file, highlight = "haddock", df_print = "paged", css = normalizePath(file.path("components", "styles.css")), - includes = rmarkdown::includes(in_header = google_analytics_file) + includes = rmarkdown::includes(in_header = google_analytics_file, + after_body = footer_file) ), # Save to original html output file name output_file = output_file From 4912ec6245e1193d00405575c51caba8dacd0ba7 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Wed, 4 Nov 2020 20:55:00 -0500 Subject: [PATCH 04/19] add incode prompt --- 02-microarray/pathway-analysis_microarray_03_gsea.Rmd | 5 ++++- 02-microarray/pathway-analysis_microarray_03_gsea.html | 7 ++++--- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd index 65a3cf30..d9e4b89c 100644 --- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd +++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd @@ -289,6 +289,7 @@ Now let's take a look at the rows associated with one of the duplicated Entrez I dge_mapped_df %>% dplyr::filter(entrez_id == "336702") ``` + We can see that the associated values vary for each row although the Entrez ID is the same. We do not want duplicated gene identifiers for the GSEA steps later, so let's keep the Entrez IDs associated with the higher t-statistic value. @@ -312,7 +313,9 @@ filtered_dge_mapped_df %>% Looks like we were able to successfully get rid of the duplicate gene identifiers and keep the observation with the higher t-statistic value! Note however, that a caveat in using this approach is that the genes that have duplicate identifiers could be enriched in a particular pathway/gene set and we may get an overly optimistic view of how perturbed that pathway truly is. - + +**START REVIEW HERE** + ## Perform gene set enrichment analysis (GSEA) The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout the pre-ranked gene list or primarily found at the top or bottom of the list. diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html index a183df43..a69de22e 100644 --- a/02-microarray/pathway-analysis_microarray_03_gsea.html +++ b/02-microarray/pathway-analysis_microarray_03_gsea.html @@ -4058,6 +4058,7 @@

    4.4 Gene identifier conversion

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rmarkdown::render(tmp_file, toc_float = TRUE, number_sections = TRUE, highlight = "haddock", df_print = "paged", - css = normalizePath(file.path("components", "styles.css")) + css = normalizePath(file.path("components", "styles.css")), + includes = rmarkdown::includes(in_header = google_analytics_file) ), # Save to original html output file name output_file = output_file From 4c0138bfd8879e36cb7feb59290a50bee477ada9 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Wed, 4 Nov 2020 20:42:32 -0500 Subject: [PATCH 03/19] add latter half of GSEA microarray example (includes GSEA steps) fix merge conflicts --- .github/ISSUE_TEMPLATE/other-issue.md | 11 +- .github/PULL_REQUEST_TEMPLATE.md | 47 +- .github/PULL_REQUEST_TEMPLATE/hotfix-pr.md | 23 + .../PULL_REQUEST_TEMPLATE/new-analysis-pr.md | 47 + .github/PULL_REQUEST_TEMPLATE/publish-pr.md | 19 + .gitignore | 3 + 01-getting-started/getting-started.html.zip | Bin 0 -> 309220 bytes .../pathway-analysis_microarray_03_gsea.Rmd | 480 ++ .../pathway-analysis_microarray_03_gsea.html | 4438 +++++++++++++++++ CONTRIBUTING.md | 161 +- Snakefile | 1 + components/footer.html | 5 + components/pr-diagrams/all-changes-pr.png | Bin 0 -> 95128 bytes components/pr-diagrams/gha-docker.png | Bin 0 -> 184596 bytes components/pr-diagrams/gha-spell-check.png | Bin 0 -> 89060 bytes components/pr-diagrams/hotfix-pr.png | Bin 0 -> 65450 bytes components/pr-diagrams/some-changes-pr.png | Bin 0 -> 134957 bytes components/references.bib | 27 + scripts/render-notebooks.R | 6 +- 19 files changed, 5228 insertions(+), 40 deletions(-) create mode 100644 .github/PULL_REQUEST_TEMPLATE/hotfix-pr.md create mode 100644 .github/PULL_REQUEST_TEMPLATE/new-analysis-pr.md create mode 100644 .github/PULL_REQUEST_TEMPLATE/publish-pr.md create mode 100644 01-getting-started/getting-started.html.zip create mode 100644 02-microarray/pathway-analysis_microarray_03_gsea.Rmd create mode 100644 02-microarray/pathway-analysis_microarray_03_gsea.html create mode 100644 components/footer.html create mode 100644 components/pr-diagrams/all-changes-pr.png create mode 100644 components/pr-diagrams/gha-docker.png create mode 100644 components/pr-diagrams/gha-spell-check.png create mode 100644 components/pr-diagrams/hotfix-pr.png create mode 100644 components/pr-diagrams/some-changes-pr.png diff --git a/.github/ISSUE_TEMPLATE/other-issue.md b/.github/ISSUE_TEMPLATE/other-issue.md index c253dbbf..6ec2f944 100644 --- a/.github/ISSUE_TEMPLATE/other-issue.md +++ b/.github/ISSUE_TEMPLATE/other-issue.md @@ -8,13 +8,13 @@ assignees: '' ### Background - + ### Problem -#### What potential "gotchas" do we know of? +### What potential "gotchas" do we know of? @@ -23,4 +23,11 @@ assignees: '' + + +### Is there a particular timeframe for this issue? + + + + diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index e14de305..a95941d8 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,28 +1,33 @@ -## Purpose: -What issue(s) does your PR address? -## Strategy -What was your strategy for this new or edited analysis? +## Use the 'Preview' view to click on a link below to choose an appropriate PR template: -## Concerns/Questions for reviewers: -What things should reviewers look out for? +For any stage of adding a new analysis example: + New analysis PR -## Analysis Pull Request Check List (roughly in order): +For publishing changes to Github pages: + Publishing PR -### Content checks -* [ ] All `{{BLANKS}}` have been replaced with the correct content. -* [ ] [Sources are cited](https://github.com/AlexsLemonade/refinebio-examples/blob/master/CONTRIBUTING.md#citing-sources-in-text) -* [ ] Seed is set (if applicable) +For either stage of a hotfix PR -- something that is straightforward and needs to be user-facing quickly: + Hotfix PR -### Formatting Checks -* [ ] Removed any manual numbering of sections. -* [ ] Removed any instances of chunk naming. -* [ ] Comments and documentation are up to date. -* [ ] All links have been checked and are properly formatted. +## For any other types of PRs that don't fit any of the above categories, delete the previous section (including this line) and use the template below. -### Add datasets to S3 -* [ ] Added [data and metadata files to S3](https://github.com/AlexsLemonade/refinebio-examples/blob/master/CONTRIBUTING.md#adding-datasets-to-the-S3-bucket). +### Purpose -### Docker/Snakemake rendering components -* [ ] Added the `.html` [link to the navigation bar](https://github.com/AlexsLemonade/refinebio-examples/blob/master/CONTRIBUTING.md#add-new-analyses-to-the-navbar). -* [ ] Any not yet added packages needed for this analysis have been added to the Dockerfile and it successfully builds. + + + + +### Issue addressed + + + +#### Gotchas the reviewer should know about + + + +## Remaining concerns and questions + + + + diff --git a/.github/PULL_REQUEST_TEMPLATE/hotfix-pr.md b/.github/PULL_REQUEST_TEMPLATE/hotfix-pr.md new file mode 100644 index 00000000..42345361 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE/hotfix-pr.md @@ -0,0 +1,23 @@ +## Hotfix Purpose + + + +## Pull Request Stage + + + +This is the **first PR**, to `master` -- it has not been reviewed at all. + +This is the **second PR**, to `staging` -- it was reviewed for the `master` branch on and now should be reviewed mostly for correct merge conflict resolutions. + +## Explain the Fix + + + + + +## Comments/Questions for the Reviewer + + + + diff --git a/.github/PULL_REQUEST_TEMPLATE/new-analysis-pr.md b/.github/PULL_REQUEST_TEMPLATE/new-analysis-pr.md new file mode 100644 index 00000000..736a9e0f --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE/new-analysis-pr.md @@ -0,0 +1,47 @@ +## Analysis Purpose + + + +## Pull Request Stage + + + +This is a **Draft PR** - needs review of big concepts and outline + +This is a **Refined PR** - needs review of details and polishing + + + +## Strategy + + + + + +## Concerns/Questions for reviewers: + + + + + + + +## Analysis Pull Request Check List (roughly in order): + +### Content checks +* [ ] All `{{BLANKS}}` have been replaced with the correct content. +* [ ] [Sources are cited](https://github.com/AlexsLemonade/refinebio-examples/blob/master/CONTRIBUTING.md#citing-sources-in-text) +* [ ] Seed is set (if applicable) + +### Formatting Checks +* [ ] Removed any manual numbering of sections. +* [ ] Removed any instances of chunk naming. +* [ ] Comments and documentation are up to date. +* [ ] All links have been checked and are properly formatted. + +### Add datasets to S3 +* [ ] Added [data and metadata files to S3](https://github.com/AlexsLemonade/refinebio-examples/blob/master/CONTRIBUTING.md#adding-datasets-to-the-S3-bucket). + +### Docker/Snakemake rendering components +* [ ] Added the `.html` [link to the navigation bar](https://github.com/AlexsLemonade/refinebio-examples/blob/master/CONTRIBUTING.md#add-new-analyses-to-the-navbar). +* [ ] Any not yet added packages needed for this analysis have been added to the Dockerfile and it successfully builds. diff --git a/.github/PULL_REQUEST_TEMPLATE/publish-pr.md b/.github/PULL_REQUEST_TEMPLATE/publish-pr.md new file mode 100644 index 00000000..7abea311 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE/publish-pr.md @@ -0,0 +1,19 @@ +## Changes Being Published + + + +## List of Commits/PR's Included + + + +- PR# Added such and such + +## Link to html Preview + +For reviewing purposes, you can take a browse through htmlpreview: http://htmlpreview.github.io/?https://github.com/AlexsLemonade/refinebio-examples/gh-pages-stages/01-getting-started/getting-started.html + +## Publishing Checklist: + +- [ ] Does everything look good using html preview -- having taken a particularly close look at any new html files? + +- [ ] Have any new examples been added to the [refinebio-examples feedback survey](https://app.hubspot.com/forms/5187852/a50f293c-1ef4-4ee1-b7ee-c563afe2ad5c/performance)? 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    Looks like we were able to successfully get rid of the duplicate gene identifiers and keep the observation with the higher t-statistic value! Note however, that a caveat in using this approach is that the genes that have duplicate identifiers could be enriched in a particular pathway/gene set and we may get an overly optimistic view of how perturbed that pathway truly is.

    +

    START REVIEW HERE

    Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    @@ -4121,7 +4122,7 @@

    4.6.1 Highly Positive NES

    dplyr::arrange(dplyr::desc(NES))

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest NES score.

    @@ -4147,7 +4148,7 @@

    4.6.2 Highly Negative NES

    dplyr::arrange(NES)

    The gene set HALLMARK_E2F_TARGETS has a highly negative NES.

    From cb075f3cec61a4e99c4373ab1fb33e0186d7ac5a Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Wed, 4 Nov 2020 21:00:13 -0500 Subject: [PATCH 05/19] revert commit that snuck in --- 02-microarray/00-intro-to-microarray.html | 162 ++++++-------------- CONTRIBUTING.md | 2 +- Snakefile | 2 +- components/_navbar.html | 22 +-- components/refine-bio-ex-logo.svg | 57 ------- components/styles.css | 69 --------- components/references.bib => references.bib | 0 scripts/render-notebooks.R | 6 +- 8 files changed, 61 insertions(+), 259 deletions(-) delete mode 100644 components/refine-bio-ex-logo.svg delete mode 100644 components/styles.css rename components/references.bib => references.bib (100%) diff --git a/02-microarray/00-intro-to-microarray.html b/02-microarray/00-intro-to-microarray.html index b9d85539..b1bd28a0 100644 --- a/02-microarray/00-intro-to-microarray.html +++ b/02-microarray/00-intro-to-microarray.html @@ -1263,22 +1263,25 @@ }; - + + + + + @@ -2590,73 +2608,6 @@ } - @@ -2923,20 +2874,15 @@ @@ -3083,34 +3023,34 @@

    0.4.1 Why doesn’t the gene I ca

    References

    -

    Dai M., P. Wang, A. D. Boyd, G. Kostov, and B. Athey et al., 2005 Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Research 33: e175. https://doi.org/10.1093/nar/gni179

    +

    Dai M., P. Wang, A. D. Boyd, G. Kostov, and B. Athey et al., 2005 Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res 33: e175.

    -

    Farina D., 2020 Gene expression analysis and DNA microarray assays. https://www.youtube.com/watch?v=Hv5flUOsE0s

    +

    Farina D., 2020 Gene expression analysis and DNA microarray assays

    -

    Govindarajan R., J. Duraiyan, K. Kaliyappan, and M. Palanisamy, 2012 Microarray and its applications. Journal of Pharmacy and Bioallied Sciences 4: S310–312. https://doi.org/10.4103/0975-7406.100283

    +

    Govindarajan R., J. Duraiyan, K. Kaliyappan, and M. Palanisamy, 2012 Microarray and its applications. J Pharm Bioallied Sci 4: S310–312.

    -

    LCSciences, 2014 Microarray or RNA sequencing? https://www.lcsciences.com/news/microarray-or-rna-sequencing/

    +

    LCSciences, 2014 Microarray or RNA sequencing?

    -

    Mantione K. J., R. M. Kream, H. Kuzelova, R. Ptacek, and J. Raboch et al., 2014 Comparing bioinformatic gene expression profiling methods: Microarray and RNA-Seq. Medical Science Monitor Basic Research 20: 138–142. https://doi.org/10.12659/MSMBR.892101

    +

    Mantione K. J., R. M. Kream, H. Kuzelova, R. Ptacek, and J. Raboch et al., 2014 Comparing bioinformatic gene expression profiling methods: microarray and RNA-Seq. Med Sci Monit Basic Res 20: 138–142.

    -

    Piccolo S. R., Y. Sun, J. D. Campbell, M. E. Lenburg, and A. H. Bild et al., 2012 A single-sample microarray normalization method to facilitate personalized-medicine workflows. Genomics 100: 337–344. https://doi.org/10.1016/j.ygeno.2012.08.003

    +

    Piccolo S. R., Y. Sun, J. D. Campbell, M. E. Lenburg, and A. H. Bild et al., 2012 A single-sample microarray normalization method to facilitate personalized-medicine workflows. Genomics 100: 337–344.

    -

    Sánchez A., and M. C. R. de Villa, 2008 A tutorial review of microarray data analysis. http://www.ub.edu/stat/docencia/bioinformatica/microarrays/ADM/slides/A_Tutorial_Review_of_Microarray_data_Analysis_17-06-08.pdf

    +

    Sánchez A., and M. C. R. de Villa, 2008 A tutorial review of microarray data analysis

    -

    Slonim D. K., and I. Yanai, 2009 Getting started in gene expression microarray analysis. PLOS Computational Biology 5: e1000543. https://doi.org/10.1371/journal.pcbi.1000543

    +

    Slonim D. K., and I. Yanai, 2009 Getting started in gene expression microarray analysis. PLoS Comput. Biol. 5: e1000543.

    -

    Tarca A. L., R. Romero, and S. Draghici, 2006 Analysis of microarray experiments of gene expression profiling. American Journal of Obstetrics and Gynecology 195: 373–388. https://doi.org/10.1016/j.ajog.2006.07.001

    +

    Tarca A. L., R. Romero, and S. Draghici, 2006 Analysis of microarray experiments of gene expression profiling. Am. J. Obstet. Gynecol. 195: 373–388.

    -

    Wu H., Introduction to gene expression microarray data analysis. http://web1.sph.emory.edu/users/hwu30/teaching/bioc/GE1.pdf

    +

    Wu H., Introduction to gene expression microarray data analysis

    diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9f950be9..8e6a9e9e 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -484,7 +484,7 @@ It also adds other components like CSS styling, a footer, and Google Analytics ( **Options:** - `--rmd`: provided by snakemake, the input `.Rmd` file to render. - `--bib_file`: File path for the `bibliography:` header option. -Default is the `references.bib` in the `components` folder. +Default is the `references.bib` script at the top of the repository. - `--html`: Default is to save the output `.html` file the same name as the input `.Rmd` file. This option allows you to specify an output file name. Default is used by snakemake. ### Add new analyses to the Snakefile diff --git a/Snakefile b/Snakefile index 7fe64d3f..808a543d 100644 --- a/Snakefile +++ b/Snakefile @@ -28,7 +28,7 @@ rule render_citations: shell: "Rscript scripts/render-notebooks.R" " --rmd {input.rmd}" - " --bib_file components/references.bib" + " --bib_file references.bib" " --cite_style components/genetics.csl" " --html {output}" " --style" diff --git a/components/_navbar.html b/components/_navbar.html index 1ed6c957..8ecdf1e4 100644 --- a/components/_navbar.html +++ b/components/_navbar.html @@ -1,20 +1,15 @@ diff --git a/components/refine-bio-ex-logo.svg b/components/refine-bio-ex-logo.svg deleted file mode 100644 index e001c3ee..00000000 --- a/components/refine-bio-ex-logo.svg +++ /dev/null @@ -1,57 +0,0 @@ - - - - Logo - Created with Sketch. - - - - - - \ No newline at end of file diff --git a/components/styles.css b/components/styles.css deleted file mode 100644 index 9ef20671..00000000 --- a/components/styles.css +++ /dev/null @@ -1,69 +0,0 @@ -html, -body { - font-family: 'Lato', sans-serif; - font-size: 16px; - -webkit-font-smoothing: antialiased; - line-height: 1.5; -} -h1, h2, h3, h4, h5, h6 { - font-family: 'Rubik', sans-serif; -} -h1 { - font-size: 1.625rem; -} -h2 { - font-size: 1.375rem; -} -h3 { - font-size: 1.25rem -} -h4 { - font-size: 1.125rem; -} -h5, h6 { - font-size: 1rem -} -p { - font-size: 1rem; - margin: 0 0 0.5rem 0; - line-height: 1.5; -} -a { - text-decoration: none; - color: #386db0; - line-height: 1; -} -.navbar-default, .navbar-nav, .navbar-inverse{ - font-size: 1rem; - max-width: 100%; - background-color: #386db0 !important; - color: #FDFDFD !important; -} - -.navbar-nav>li>a { - font-size: 1rem; - background-color: #386db0 !important; - color: #FDFDFD !important; -} - -.navbar-default .navbar-brand{ - color: #FDFDFD; !important; - float: left; - max-width: 100%; - display: block; - overflow: visible; - padding-bottom: 32px; -} - -ul.dropdown-menu { - background-color: #FDFDFD !important; - color: #000000 !important; -} -.dropdown-menu>li>a:hover, .dropdown-menu>li>a:active { - background-color: #386db0 !important; - color: #FDFDFD !important; -} -.list-group-item.active, .list-group-item.active:focus, .list-group-item.active:hover { - background-color: #386db0 !important; - color: #FDFDFD !important; -} diff --git a/components/references.bib b/references.bib similarity index 100% rename from components/references.bib rename to references.bib diff --git a/scripts/render-notebooks.R b/scripts/render-notebooks.R index 8de069aa..529f035c 100644 --- a/scripts/render-notebooks.R +++ b/scripts/render-notebooks.R @@ -54,9 +54,6 @@ opt <- parse_args(OptionParser(option_list = option_list)) # Get working directory base_dir <- getwd() -# Normalize file path -opt$bib_file <- normalizePath(opt$bib_file) - # Check that the rmd file exists if (!file.exists(opt$rmd)) { stop("Rmd file specified with --rmd is not found.") @@ -66,7 +63,7 @@ if (!file.exists(opt$rmd)) { if (!file.exists(opt$bib_file)) { stop("File specified for --bib_file option is not at the specified file path.") } else { - header_line <- paste("bibliography:", opt$bib_file) + header_line <- paste("bibliography:", normalizePath(opt$bib_file)) } # Check for a citation style if (!is.null(opt$cite_style)){ @@ -121,7 +118,6 @@ rmarkdown::render(tmp_file, output_format = rmarkdown::html_document( toc = TRUE, toc_depth = 2, toc_float = TRUE, number_sections = TRUE, - highlight = "haddock", df_print = "paged", css = normalizePath(file.path("components", "styles.css")), includes = rmarkdown::includes(in_header = google_analytics_file, From d6d57844d158664660817b587ae5dc9bddddc758 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Wed, 4 Nov 2020 21:02:39 -0500 Subject: [PATCH 06/19] revert commit --- .github/ISSUE_TEMPLATE/new-analysis.md | 26 ----- .github/ISSUE_TEMPLATE/other-issue.md | 33 ------ .github/workflows/docker-build-push.yml | 69 +++++------ .github/workflows/docker-build.yml | 4 +- .github/workflows/style-and-sp-check.yml | 2 +- 02-microarray/00-intro-to-microarray.Rmd | 5 +- 02-microarray/00-intro-to-microarray.html | 6 +- 03-rnaseq/00-intro-to-rnaseq.Rmd | 5 +- 03-rnaseq/00-intro-to-rnaseq.html | 129 ++++++++++++--------- components/figures/microarray-overview.jpg | Bin 0 -> 44998 bytes components/figures/microarray-overview.png | Bin 107241 -> 0 bytes components/figures/rnaseq-overview.png | Bin 99450 -> 86861 bytes components/google-analytics.html | 8 -- scripts/render-notebooks.R | 10 +- 14 files changed, 115 insertions(+), 182 deletions(-) delete mode 100644 .github/ISSUE_TEMPLATE/new-analysis.md delete mode 100644 .github/ISSUE_TEMPLATE/other-issue.md create mode 100644 components/figures/microarray-overview.jpg delete mode 100644 components/figures/microarray-overview.png delete mode 100644 components/google-analytics.html diff --git a/.github/ISSUE_TEMPLATE/new-analysis.md b/.github/ISSUE_TEMPLATE/new-analysis.md deleted file mode 100644 index c6a53856..00000000 --- a/.github/ISSUE_TEMPLATE/new-analysis.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -name: New analysis example -about: Use this issue template for filing a new analysis issue -title: 'New Analysis Example:' -labels: new analysis example -assignees: '' - ---- - -### What are the goals of this new example analysis? - - - - - -### What kind of dataset will this need? - - - -### What steps should be included in this analysis? - - - -### What packages/methods do you recommend using or looking into for this analysis? - - diff --git a/.github/ISSUE_TEMPLATE/other-issue.md b/.github/ISSUE_TEMPLATE/other-issue.md deleted file mode 100644 index 6ec2f944..00000000 --- a/.github/ISSUE_TEMPLATE/other-issue.md +++ /dev/null @@ -1,33 +0,0 @@ ---- -name: Other issue -about: Use this issue template to describe a current issue with an analysis or documentation (that is not a new example analysis) -assignees: '' - ---- - -### Background - - - - -### Problem - - - -### What potential "gotchas" do we know of? - - - -### What are the recommended next steps? - - - - - - - -### Is there a particular timeframe for this issue? - - - - diff --git a/.github/workflows/docker-build-push.yml b/.github/workflows/docker-build-push.yml index b3832343..47cc48fd 100644 --- a/.github/workflows/docker-build-push.yml +++ b/.github/workflows/docker-build-push.yml @@ -4,7 +4,7 @@ name: Build, Render, and Push # events only for the master branch on: push: - branches: [ staging, master ] + branches: [ master ] jobs: # This workflow contains a single job called "build-all" @@ -21,54 +21,54 @@ jobs: fetch-depth: 0 # use alexslemonade-docs-bot token: ${{ secrets.DOCS_BOT_GITHUB_TOKEN }} - - name: Checkout pages branch and sync with changes run: | - echo $GITHUB_REF - if [ $GITHUB_REF == 'refs/heads/master' ] - then - pages_branch="gh-pages" - elif [ $GITHUB_REF == 'refs/heads/staging' ] - then - pages_branch="gh-pages-stages" - fi git config --local user.email "actions@github.com" git config --local user.name "Alex's Lemonade Docs Bot" - git checkout $pages_branch + git checkout gh-pages git merge -s recursive --strategy-option=theirs ${{ github.event.after }} + # Test if Dockerfile has changed + # sets steps.check_docker.outputs.changed to 1 if the Dockerfile has changed, 0 otherwise + - name: Check Dockerfile for changes + id: check_docker + env: + BEFORE: ${{ github.event.before }} + run: | + git diff-index --name-only $BEFORE > changes.txt + if grep "docker/Dockerfile" changes.txt ; then + echo "Dockerfile changed" + echo "::set-output name=changed::1" + else + echo "No change to Dockerfile" + echo "::set-output name=changed::0" + fi + rm changes.txt + # Login to Dockerhub - name: Login to DockerHub + if: steps.check_docker.outputs.changed == 1 uses: docker/login-action@v1 with: username: ${{ secrets.DOCKER_ID }} password: ${{ secrets.DOCKER_PASSWORD }} # set up Docker build - name: Set up Docker Buildx + if: steps.check_docker.outputs.changed == 1 uses: docker/setup-buildx-action@v1 - # Build the Docker image - - name: Build and Load Docker image + # Build docker image (We are not using caching here to force a clean build) + - name: Build and Push Docker image + if: steps.check_docker.outputs.changed == 1 uses: docker/build-push-action@v2 with: - push: false - load: true + push: true context: docker file: docker/Dockerfile tags: ccdl/refinebio-examples:latest - # push the Docker image if this is staging - - name: Push Docker image - if: github.ref == 'refs/heads/staging' - run: docker push ccdl/refinebio-examples:latest - # retag and push the Docker image if this is master - - name: Push release Docker image - if: github.ref == 'refs/heads/master' - run: | - docker tag ccdl/refinebio-examples:latest ccdl/refinebio-examples:release - docker push ccdl/refinebio-examples:release - # download data - name: Download data run: bash scripts/download-data.sh + - name: Render all pages to html run: | docker run \ @@ -76,18 +76,11 @@ jobs: ccdl/refinebio-examples \ snakemake --cores 2 --forceall - # If we are on the staging branch, do not publish to github pages - - name: Commit changed html back to non-public pages - if: github.ref == 'refs/heads/staging' + - name: Commit changed html run: | git add -A - git commit -m 'Render html, do not publish' || echo "No changes to commit" - git push origin gh-pages-stages || echo "No changes to push" - - # If we are on the master branch, publish to github pages! - - name: Commit changed html to public pages - if: github.ref == 'refs/heads/master' - run: | - git add -A - git commit -m 'Render html and publish' || echo "No changes to commit" + git commit -m 'Render html' || echo "No changes to commit" git push origin gh-pages || echo "No changes to push" + + + diff --git a/.github/workflows/docker-build.yml b/.github/workflows/docker-build.yml index aa065d43..e5da75b0 100644 --- a/.github/workflows/docker-build.yml +++ b/.github/workflows/docker-build.yml @@ -1,10 +1,10 @@ name: Build Docker -# Controls when the action will run. Triggers the workflow for a pull request for +# Controls when the action will run. Triggers the workflow for a pull request for # master on: pull_request: - branches: [ staging, master ] + branches: [ master ] paths: [ docker/Dockerfile ] # A workflow run is made up of one or more jobs that can run sequentially or in parallel diff --git a/.github/workflows/style-and-sp-check.yml b/.github/workflows/style-and-sp-check.yml index 314b12eb..cf536f9e 100644 --- a/.github/workflows/style-and-sp-check.yml +++ b/.github/workflows/style-and-sp-check.yml @@ -5,7 +5,7 @@ name: Style and spell check R markdowns # events but only for the master branch on: pull_request: - branches: [ staging, master ] + branches: [ master ] # A workflow run is made up of one or more jobs that can run sequentially or in parallel jobs: diff --git a/02-microarray/00-intro-to-microarray.Rmd b/02-microarray/00-intro-to-microarray.Rmd index b0b00b25..ae4f3159 100644 --- a/02-microarray/00-intro-to-microarray.Rmd +++ b/02-microarray/00-intro-to-microarray.Rmd @@ -32,13 +32,12 @@ Microarrays measure gene expression using chips filled with oligonucleotide prob After hybridization, the microarrays are scanned, and the fluorescence intensity for each probe is measured. The fluorescence intensity indicates the number of labeled fragments bound and therefore the relative quantity of the transcript the probe is designed for. - + -[based on diagram from @microarray-video] +[@microarray-video] There are many different kinds of microarray platforms, which can be broadly separated into single-color and [two-color arrays](https://www.ebi.ac.uk/training/online/course/functional-genomics-ii-common-technologies-and-data-analysis-methods/microarrays). At this time, refine.bio only supports single-color arrays, so our examples and advice are generally from the perspective of using single-color array. -The diagram above shows an overview of the single-color array process which includes extracting the total RNA from a sample, labeling the RNA with fluorescent dye, hybridizing the labels, and scanning the fluorescent image to analyze the fluorescence intensity. The two most common microarray platforms on refine.bio are [Affymetrix GeneChips](https://www.thermofisher.com/us/en/home/life-science/microarray-analysis/transcriptome-profiling-microarrays.html) and [Illumina BeadArray](https://www.illumina.com/documents/products/datasheets/datasheet_gene_exp_analysis.pdf). A longer list of specific arrays that are supported by refine.bio can be found [here](https://github.com/AlexsLemonade/refinebio/blob/dev/config/supported_microarray_platforms.csv). diff --git a/02-microarray/00-intro-to-microarray.html b/02-microarray/00-intro-to-microarray.html index b1bd28a0..45b74393 100644 --- a/02-microarray/00-intro-to-microarray.html +++ b/02-microarray/00-intro-to-microarray.html @@ -2972,9 +2972,9 @@

    CCDL for ALSF

    0.1 Introduction to microarray technology

    Microarrays measure gene expression using chips filled with oligonucleotide probes designed to hybridize to labeled RNA samples. After hybridization, the microarrays are scanned, and the fluorescence intensity for each probe is measured. The fluorescence intensity indicates the number of labeled fragments bound and therefore the relative quantity of the transcript the probe is designed for.

    -

    -

    (based on diagram from Farina 2020)

    -

    There are many different kinds of microarray platforms, which can be broadly separated into single-color and two-color arrays. At this time, refine.bio only supports single-color arrays, so our examples and advice are generally from the perspective of using single-color array. The diagram above shows an overview of the single-color array process which includes extracting the total RNA from a sample, labeling the RNA with fluorescent dye, hybridizing the labels, and scanning the fluorescent image to analyze the fluorescence intensity.

    +

    +

    (Farina 2020)

    +

    There are many different kinds of microarray platforms, which can be broadly separated into single-color and two-color arrays. At this time, refine.bio only supports single-color arrays, so our examples and advice are generally from the perspective of using single-color array.

    The two most common microarray platforms on refine.bio are Affymetrix GeneChips and Illumina BeadArray. A longer list of specific arrays that are supported by refine.bio can be found here.

    As with all experimental methods, microarrays have strengths and limitations that you should consider in regards to your scientific questions.

    diff --git a/03-rnaseq/00-intro-to-rnaseq.Rmd b/03-rnaseq/00-intro-to-rnaseq.Rmd index 909232c0..584ea4c5 100644 --- a/03-rnaseq/00-intro-to-rnaseq.Rmd +++ b/03-rnaseq/00-intro-to-rnaseq.Rmd @@ -36,11 +36,10 @@ This tutorial has example analyses [organized by technology](../01-getting-start RNA-sequencing measures gene expression by direct high-throughput sequencing methods after the RNA has been isolated from a sample. - + -[based on diagram from @rnaseq-blog] +[@rnaseq-blog] -The diagram above provides a brief overview of the RNA-seq process, which includes extracting the total RNA from a tissue population, isolating the specific RNA species, converting the RNA to cDNA, and constructing a sequencing library to perform PCR amplification and sequencing. As with all experimental methods, RNA-seq has strengths and limitations that you should consider in regards to your scientific questions. ### RNA-seq data **strengths** diff --git a/03-rnaseq/00-intro-to-rnaseq.html b/03-rnaseq/00-intro-to-rnaseq.html index bda2b6cc..c48b0903 100644 --- a/03-rnaseq/00-intro-to-rnaseq.html +++ b/03-rnaseq/00-intro-to-rnaseq.html @@ -1263,22 +1263,25 @@ }; - + + + + + @@ -2882,7 +2900,6 @@
  • Differential Expression - Several groups
  • Dimension Reduction - PCA
  • Dimension Reduction - UMAP
    • -
  • Pathway Analysis - ORA
  • Ensembl Gene ID Annotation
  • Ortholog Mapping
    • @@ -2965,14 +2982,14 @@

    CCDL for ALSF

    0.1 Introduction to RNA-seq technology

    RNA-sequencing measures gene expression by direct high-throughput sequencing methods after the RNA has been isolated from a sample.

    -

    -

    (based on diagram from “An introduction to RNA-Seq methods, applications, experimental design, and technical challenges” 2015)

    -

    The diagram above provides a brief overview of the RNA-seq process, which includes extracting the total RNA from a tissue population, isolating the specific RNA species, converting the RNA to cDNA, and constructing a sequencing library to perform PCR amplification and sequencing. As with all experimental methods, RNA-seq has strengths and limitations that you should consider in regards to your scientific questions.

    +

    +

    (“An introduction to rna-seq methods, applications, experimental design, and technical challenges” 2015)

    +

    As with all experimental methods, RNA-seq has strengths and limitations that you should consider in regards to your scientific questions.

    0.1.1 RNA-seq data strengths

      -
    • RNA-seq can assay unknown transcripts, as it is not bound to a pre-determined set of probes like microarrays (Wang et al. 2009).
      • -
    • Its values are considered more dynamic than microarray values which are constrained to a smaller range based on background signal and probe sets being saturated (Wang et al. 2009).
      • +
    • RNA-seq can assay unknown transcripts, as it is not bound to a pre-determined set of probes like microarrays (Wang et al.).
      • +
    • Its values are considered more dynamic than microarray values which are constrained to a smaller range based on background signal and probe sets being saturated (Wang et al.).
    @@ -3002,8 +3019,8 @@

    0.1.3 About quantile normalizatio

    0.2.2 DESeq2 transformation methods

    -

    Our examples recommend using DESeq2 for normalizing your RNA-seq data. You may have heard about or worked with FPKM, TPM, RPKMs; how does DESeq2’s normalization compare? This handy table from an online Harvard Bioinformatics Core course nicely summarizes and compares these different methods (Harvard Chan Bioinformatics Core). For more about the steps behind DESeq2 normalization, we highly recommend this StatQuest video which explains it quite nicely (Starmer 2017a).

    -

    To normalize and transform our data with DESeq2, we generally use vst() (variance stabilizing transformation) or rlog() (regularized logarithm transformation). Both methods are very similar. Both normalize your data by correcting for library size differences but they also transform your data removing the dependence of the variance on the mean, meaning that low mean genes won’t have inflated variance from just one or a few samples having higher values than the rest (Michael I. Love and Huber 2020). Of the two methods, rlog() takes a bit longer to run (Michael I. Love and Huber 2019). If you end up using a larger dataset and rlog() transformation takes a bit too long, you can switch to using vst() with confidence since they yield similar results given the dataset is large enough (Michael I. Love and Huber 2019).

    +

    Our examples recommend using DESeq2 for normalizing your RNA-seq data. You may have heard about or worked with FPKM, TPM, RPKMs; how does DESeq2’s normalization compare? This handy table from an online Harvard Bioinformatics Core course nicely summarizes and compares these different methods (Harvard Chan Bioinformatics Core (HBC)). For more about the steps behind DESeq2 normalization, we highly recommend this StatQuest video which explains it quite nicely (Josh Starmer 2017b).

    +

    To normalize and transform our data with DESeq2, we generally use vst() (variance stabilizing transformation) or rlog() (regularized logarithm transformation). Both methods are very similar. Both normalize your data by correcting for library size differences but they also transform your data removing the dependence of the variance on the mean, meaning that low mean genes won’t have inflated variance from just one or a few samples having higher values than the rest (Michael I. Love, Simon Anders, and Wolfgang Huber 2020). Of the two methods, rlog() takes a bit longer to run (Michael I. Love and Huber 2019). If you end up using a larger dataset and rlog() transformation takes a bit too long, you can switch to using vst() with confidence since they yield similar results given the dataset is large enough (Michael I. Love and Huber 2019).

    0.2.3 Further resources for DESeq2

    @@ -3046,7 +3063,7 @@

    0.3.0.1 Why isn’t the gene I ca

    0.3.0.2 What about edgeR?

    In short, both edgeR and DESeq2 are good options and we at the CCDL just went with one of our preferences! See this blog that summarizes these – by one of the creators of DESeq2 – he agrees edgeR is also great.

    -

    If you have strong preferences for edgeR, you can definitely use your refine.bio data with it, but we currently do not have examples of that. In this case, we’d refer you to edgeR’s section of this example analysis and wish you the best of luck on your data adventures (Hansen)!

    +

    If you have strong preferences for edgeR, you can definitely use your refine.bio data with it, but we currently do not have examples of that. In this case, we’d refer you to edgeR’s section of this example analysis and wish you the best of luck on your data adventures (Kasper D. Hansen)!

    0.3.0.3 What if I care about isoforms?

    @@ -3058,58 +3075,58 @@

    0.3.0.3 What if I care about isof

    References

    -

    An introduction to RNA-Seq methods, applications, experimental design, and technical challenges, 2015. https://rna-seqblog.com/an-introduction-to-rna-seq-methods-applications-experimental-design-and-technical-challenges/

    -
    -
    -

    Hadfield J., 2016 An introduction to RNA-seq. https://bitesizebio.com/13542/what-everyone-should-know-about-rna-seq/

    +

    An introduction to rna-seq methods, applications, experimental design, and technical challenges, 2015

    -

    Hansen K. D., S. E. Brenner, and S. Dudoit, 2010 Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Research 38: e131. https://doi.org/10.1093/nar/gkq224

    -
    -
    -

    Hansen K. D., Count based RNA-seq analysis. https://kasperdanielhansen.github.io/genbioconductor/html/Count_Based_RNAseq.html

    +

    Hansen K. D., S. E. Brenner, and S. Dudoit, 2010 Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res. 38: e131.

    -

    Harvard Chan Bioinformatics Core, Introduction to DGE - DESeq2 analysis. https://hbctraining.github.io/DGE_workshop/lessons/04_DGE_DESeq2_analysis.html

    +

    Harvard Chan Bioinformatics Core (HBC), Introduction to dge - deseq2 analysis

    -

    Harvard Chan Bioinformatics Core, Introduction to DGE - count normalization. https://hbctraining.github.io/DGE_workshop_salmon/lessons/02_DGE_count_normalization.html

    +

    Harvard Chan Bioinformatics Core (HBC), Introduction to dge - count normalization

    +
    +
    +

    James Hadfield, 2016 An introduction to rna-seq

    +
    +
    +

    Josh Starmer, 2017a StatQuest: A gentle introduction to rna-seq

    +
    +
    +

    Josh Starmer, 2017b StatQuest: DESeq2, part 1, library normalization

    +
    +
    +

    Kasper D. Hansen, Count based rna-seq analysis

    -

    Love M. I., W. Huber, and S. Anders, 2014 Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biology 15. https://doi.org/10.1186/s13059-014-0550-8

    +

    Love M. I., W. Huber, and S. Anders, 2014 Moderated estimation of fold change and dispersion for rna-seq data with deseq2. Genome Biology 15. https://doi.org/10.1186/s13059-014-0550-8

    -

    Love M. I., 2016 RNA-seq fragment sequence bias. https://mikelove.wordpress.com/2016/09/26/rna-seq-fragment-sequence-bias/

    +

    Love M. I., 2016 RNA-seq fragment sequence bias

    -

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header_range[1]) # Write to an tmp file readr::write_lines(new_lines, tmp_file) -# Declare path to google analytics bit -google_analytics_file <- normalizePath(file.path("components", "google-analytics.html")) - -# Declare path to footer -footer_file <- normalizePath(file.path("components", "footer.html")) - # Render the header added notebook rmarkdown::render(tmp_file, output_format = rmarkdown::html_document( toc = TRUE, toc_depth = 2, toc_float = TRUE, number_sections = TRUE, df_print = "paged", - css = normalizePath(file.path("components", "styles.css")), - includes = rmarkdown::includes(in_header = google_analytics_file, - after_body = footer_file) + css = normalizePath(file.path("components", "styles.css")) ), # Save to original html output file name output_file = output_file From d20af9ca17020e517f1117905fba1a491f563009 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Thu, 5 Nov 2020 09:01:21 -0500 Subject: [PATCH 07/19] set seed and re-run --- .../pathway-analysis_microarray_03_gsea.Rmd | 10 + .../pathway-analysis_microarray_03_gsea.html | 211 +++++++++--------- 2 files changed, 118 insertions(+), 103 deletions(-) diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd index d9e4b89c..f91c217a 100644 --- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd +++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd @@ -142,6 +142,13 @@ library(org.Dr.eg.db) library(magrittr) ``` +The GSEA algorithm utilizes random sampling so we are going to set the seed to make our results reproducible. + +```{r} +# Set the seed so our results are reproducible: +set.seed(12345) +``` + ## Import data We will read in the differential expression results we will download from online. @@ -334,6 +341,8 @@ t_vector <- filtered_dge_mapped_df$t names(t_vector) <- filtered_dge_mapped_df$entrez_id ``` +Let's preview our pre-ranked named vector. + ```{r} # Look at first entries of the ranked t-statistic vector head(t_vector) @@ -357,6 +366,7 @@ gsea_results <- GSEA( maxGSSize = 500, # maximum gene set set pvalueCutoff = 0.05, # p value cutoff eps = 0, # boundary for calculating the p value + seed = TRUE, # set seed to make results reproducible pAdjustMethod = "BH", # Benjamini-Hochberg correction TERM2GENE = dplyr::select( dr_hallmark_df, diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html index a69de22e..25d197d9 100644 --- a/02-microarray/pathway-analysis_microarray_03_gsea.html +++ b/02-microarray/pathway-analysis_microarray_03_gsea.html @@ -3926,20 +3926,23 @@

    4.1 Install libraries

    ## 
    # We will need this so we can use the pipe: %>%
 library(magrittr)
    +

    The GSEA algorithm utilizes random sampling so we are going to set the seed to make our results reproducible.

    +
    # Set the seed so our results are reproducible:
+set.seed(12345)

    4.2 Import data

    We will read in the differential expression results we will download from online. These results are from a zebrafish microarray experiment we used for differential expression analysis for two groups using limma (Ritchie et al. 2015). The table contains summary statistics including Ensembl gene IDs, t-statistic values, and adjusted p-values (FDR in this case). We can identify differentially regulated genes by filtering these results and use this list as input to GSEA.

    Instead of using the URL below, you can use a file path to a TSV file with your desired gene list results. First we will assign the URL to its own variable called, dge_url.

    -
    # Define the url to your differential expression results file
-dge_url <- "https://refinebio-examples.s3.us-east-2.amazonaws.com/02-microarray/results/GSE71270/GSE71270_limma_results.tsv"
    +
    # Define the url to your differential expression results file
+dge_url <- "https://refinebio-examples.s3.us-east-2.amazonaws.com/02-microarray/results/GSE71270/GSE71270_limma_results.tsv"

    Read in the file that has differential expression results. Here we are using the URL we set up above, but this can be a local file path instead i.e. you can replace dge_url in the code below with a path to file you have on your computer like: file.path("results", "GSE71270_limma_results.tsv").

    -
    # Read in the contents of your differential expression results file
-# `dge_url` can be replaced with a file path to a TSV file with your
-# desired gene list results
-dge_df <- readr::read_tsv(dge_url)
    +
    # Read in the contents of your differential expression results file
+# `dge_url` can be replaced with a file path to a TSV file with your
+# desired gene list results
+dge_df <- readr::read_tsv(dge_url)
    ## 
-## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────────────────────────────
 ## cols(
 ##   Gene = col_character(),
 ##   logFC = col_double(),
@@ -3950,7 +3953,7 @@ 
    4.2 Import data
    
 ##   B = col_double()
 ## )

    read_tsv() can read TSV files online and doesn’t necessarily require you download the file first. Let’s take a look at what these contrast results from the differential expression analysis look like.

    -
    dge_df
    +
    dge_df

    Now let’s check to see if we have any Entrez IDs that mapped to multiple Ensembl IDs.

    -
    any(duplicated(dge_mapped_df$entrez_id))
    +
    any(duplicated(dge_mapped_df$entrez_id))
    ## [1] TRUE

    Looks like we do have duplicated Entrez IDs. Let’s find out which Entrez IDs have been duplicated.

    -
    dge_mapped_df %>%
-  dplyr::filter(duplicated(entrez_id)) %>%
-  dplyr::pull(entrez_id)
    +
    dge_mapped_df %>%
+  dplyr::filter(duplicated(entrez_id)) %>%
+  dplyr::pull(entrez_id)
    ## [1] "336702" "57924"

    Now let’s take a look at the rows associated with one of the duplicated Entrez IDs.

    -
    dge_mapped_df %>%
-  dplyr::filter(entrez_id == "336702")
    +
    dge_mapped_df %>%
+  dplyr::filter(entrez_id == "336702")

    Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    The information we’re most likely interested in is in the results slot. Let’s convert this into a data frame that we can use for further analysis and write to file.

    -
    gsea_result_df <- data.frame(gsea_results@result)
    +
    gsea_result_df <- data.frame(gsea_results@result)
    @@ -4116,65 +4121,65 @@

    4.6 Visualizing results

    4.6.1 Highly Positive NES

    Let’s look for the gene set with the highest positive NES.

    -
    gsea_result_df %>%
-  # Combining the `dplyr::desc()` function with `dplyr::arrange()` sorts the
-  # values in descending order
-  dplyr::arrange(dplyr::desc(NES))
    +
    gsea_result_df %>%
+  # Combining the `dplyr::desc()` function with `dplyr::arrange()` sorts the
+  # values in descending order
+  dplyr::arrange(dplyr::desc(NES))

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest NES score.

    -
    enrichplot::gseaplot(gsea_results,
-  geneSetID = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
-  title = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
-  color.line = "#0d76ff"
-)
    +
    enrichplot::gseaplot(gsea_results,
+  geneSetID = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
+  title = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
+  color.line = "#0d76ff"
+)

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores.

    The plots returned by enrichplot::gseaplot are ggplots, so we can use ggplot2::ggsave() to save them to file.

    Let’s save to PNG.

    -
    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
-  plot = ggplot2::last_plot()
-)
    +
    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
+  plot = ggplot2::last_plot()
+)
    ## Saving 7 x 5 in image

    4.6.2 Highly Negative NES

    Let’s look for the gene set with the highest negative NES.

    -
    gsea_result_df %>%
-  # The `dplyr::arrange()` function sorts the values in ascending order
-  dplyr::arrange(NES)
    +
    gsea_result_df %>%
+  # The `dplyr::arrange()` function sorts the values in ascending order
+  dplyr::arrange(NES)

    The gene set HALLMARK_E2F_TARGETS has a highly negative NES.

    -
    enrichplot::gseaplot(gsea_results,
-  geneSetID = "HALLMARK_E2F_TARGETS",
-  title = "HALLMARK_E2F_TARGETS",
-  color.line = "#0d76ff"
-)
    +
    enrichplot::gseaplot(gsea_results,
+  geneSetID = "HALLMARK_E2F_TARGETS",
+  title = "HALLMARK_E2F_TARGETS",
+  color.line = "#0d76ff"
+)

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph.

    Let’s save this plot to PNG.

    -
    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
-  plot = ggplot2::last_plot()
-)
    +
    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
+  plot = ggplot2::last_plot()
+)
    ## Saving 7 x 5 in image

    4.7 Write results to file

    -
    readr::write_tsv(
-  gsea_result_df,
-  file.path(
-    results_dir,
-    "GSE71270_gsea_results.tsv"
-  )
-)
    +
    readr::write_tsv(
+  gsea_result_df,
+  file.path(
+    results_dir,
+    "GSE71270_gsea_results.tsv"
+  )
+)
    @@ -4189,8 +4194,8 @@

    5 Resources for further learning<

    6 Session info

    At the end of every analysis, before saving your notebook, we recommend printing out your session info. This helps make your code more reproducible by recording what versions of software and packages you used to run this.

    -
    # Print session info
-sessioninfo::session_info()
    +
    # Print session info
+sessioninfo::session_info()
    ## ─ Session info ───────────────────────────────────────────────────────────────
 ##  setting  value                       
 ##  version  R version 4.0.2 (2020-06-22)

From a7d22b8ee1389268dd7b5b55825478bfc05c351f Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Thu, 5 Nov 2020 20:04:13 -0500
Subject: [PATCH 08/19] incorporate some of the wording/context suggestions
 from review

---
 .../pathway-analysis_microarray_03_gsea.Rmd   | 101 +++++----
 .../pathway-analysis_microarray_03_gsea.html  | 197 +++++++++---------
 2 files changed, 157 insertions(+), 141 deletions(-)

diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
index f91c217a..1f463839 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
@@ -44,18 +44,14 @@ If you have trouble running this chunk, see our [introduction to using `.Rmd`s](
 if (!dir.exists("data")) {
   dir.create("data")
 }
-
 # Define the file path to the plots directory
 plots_dir <- "plots" # Can replace with path to desired output plots directory
-
 # Create the plots folder if it doesn't exist
 if (!dir.exists(plots_dir)) {
   dir.create(plots_dir)
 }
-
 # Define the file path to the results directory
 results_dir <- "results" # Can replace with path to desired output results directory
-
 # Create the results folder if it doesn't exist
 if (!dir.exists(results_dir)) {
   dir.create(results_dir)
@@ -84,7 +80,7 @@ Your new analysis folder should contain:
 - A folder called `data` (currently empty) 
 - A folder for `plots` (currently empty)  
 - A folder for `results` (currently empty)  
-    
+
 Your example analysis folder should contain your `.Rmd` and three empty folders (which won't be empty for long!).
 
 If the concept of a "file path" is unfamiliar to you; we recommend taking a look at our [section about file paths](https://alexslemonade.github.io/refinebio-examples/01-getting-started/getting-started.html#an-important-note-about-file-paths-and-Rmds). 
@@ -114,12 +110,10 @@ if (!("clusterProfiler" %in% installed.packages())) {
   # Install this package if it isn't installed yet
   BiocManager::install("clusterProfiler", update = FALSE)
 }
-
 if (!("msigdbr" %in% installed.packages())) {
   # Install this package if it isn't installed yet
   BiocManager::install("msigdbr", update = FALSE)
 }
-
 if (!("org.Dr.eg.db" %in% installed.packages())) {
   # Install this package if it isn't installed yet
   BiocManager::install("org.Dr.eg.db", update = FALSE)
@@ -131,29 +125,19 @@ Attach the packages we need for this analysis.
 ```{r}
 # Attach the library
 library(clusterProfiler)
-
 # Package that contains MSigDB gene sets in tidy format
 library(msigdbr)
-
 # Zebrafish annotation package we'll use for gene identifier conversion
 library(org.Dr.eg.db)
-
 # We will need this so we can use the pipe: %>%
 library(magrittr)
 ```
 
-The GSEA algorithm utilizes random sampling so we are going to set the seed to make our results reproducible.
-
-```{r}
-# Set the seed so our results are reproducible:
-set.seed(12345)
-```
-
 ## Import data 
 
 We will read in the differential expression results we will download from online.
 These results are from a zebrafish microarray experiment we used for [differential expression analysis for two groups](https://alexslemonade.github.io/refinebio-examples/02-microarray/differential-expression_microarray_02_2-groups.html) using [`limma`](https://bioconductor.org/packages/release/bioc/html/limma.html) [@Ritchie2015].
-The table contains summary statistics including Ensembl gene IDs, t-statistic values, and adjusted p-values (FDR in this case).
+The table contains summary statistics including Ensembl gene IDs, t-statistic values for each group, and adjusted p-values (FDR in this case).
 We can identify differentially regulated genes by filtering these results and use this list as input to GSEA.
 
 Instead of using the URL below, you can use a file path to a TSV file with your desired gene list results.
@@ -219,10 +203,19 @@ dr_hallmark_df <- msigdbr(
 )
 ```
 
-If you want a less curated dataset, run the chunk above without specifying a `category` to the `msigdbr()` function.
+If you run the chunk above without specifying a `category` to the `msigdbr()` function, it will return all of the MSigDB gene sets for zebrafish.
+
+Let's preview what's in `dr_hallmark_df`.
+
+```{r}
+head(dr_hallmark_df)
+```
+
+Looks like we have a data frame of gene sets with associated gene symbols and Entrez IDs.
 
 In our differential expression results data frame, `dge_df` we have Ensembl gene identifiers. 
 So we will need to convert our Ensembl IDs into either gene symbols or Entrez IDs for GSEA.
+This conversation was marked as resolved by cbethell
 
 ## Gene identifier conversion
 
@@ -259,8 +252,10 @@ dge_mapped_df <- data.frame(
 ) %>%
   # If an Ensembl gene identifier doesn't map to a Entrez gene identifier, drop that
   # from the data frame
-  dplyr::filter(!is.na(entrez_id)) %>% # Make an `Ensembl` column to store the rownames
-  tibble::rownames_to_column("Ensembl") %>% # Now let's join the rest of the expression data
+  dplyr::filter(!is.na(entrez_id)) %>%
+  # Make an `Ensembl` column to store the rownames
+  tibble::rownames_to_column("Ensembl") %>%
+  # Now let's join the rest of the expression data
   dplyr::inner_join(dge_df, by = c("Ensembl" = "Gene"))
 ```
 
@@ -275,7 +270,8 @@ Let's see a preview of `dge_mapped_df`.
 head(dge_mapped_df)
 ```
 
-Now let's check to see if we have any Entrez IDs that mapped to multiple Ensembl IDs.
+We do not want duplicated gene identifiers for the GSEA steps later as the `GSEA()` function will throw a warning that having duplicate gene names may produce unexpected results.
+Let's check to see if we have any Entrez IDs that mapped to multiple Ensembl IDs.
 
 ```{r}
 any(duplicated(dge_mapped_df$entrez_id))
@@ -285,41 +281,46 @@ Looks like we do have duplicated Entrez IDs.
 Let's find out which Entrez IDs have been duplicated.
 
 ```{r}
-dge_mapped_df %>%
+dup_entrez_ids <- dge_mapped_df %>%
   dplyr::filter(duplicated(entrez_id)) %>%
   dplyr::pull(entrez_id)
+dup_entrez_ids
 ```
 
-Now let's take a look at the rows associated with one of the duplicated Entrez IDs.
+Now let's take a look at the rows associated with the duplicated Entrez IDs.
 
 ```{r}
 dge_mapped_df %>%
-  dplyr::filter(entrez_id == "336702")
+  dplyr::filter(entrez_id %in% dup_entrez_ids)
 ```
 
-We can see that the associated values vary for each row although the Entrez ID is the same.
+We can see that the associated values vary for each row.
 
-We do not want duplicated gene identifiers for the GSEA steps later, so let's keep the Entrez IDs associated with the higher t-statistic value.
+As we mentioned earlier, we will want to remove duplicated gene identifiers in preparation for the GSEA steps later, so let's keep the Entrez IDs associated with the higher absolute value of the t-statistic.
+GSEA relies on genes' rankings on the basis of this statistic.
+Retaining the instance of the Entrez ID with the higher absolute value means that we will retain the value that is likely to be more highly- or lowly-ranked or, put another way, less likely to be towards the middle of the ranked gene list.
+We should keep this decision in mind when interpreting our results.
+For example, if the duplicate identifiers tended to be enriched in a particular gene set, we may get an optimistic view of how perturbed that gene set is by preferentially selecting values that have a higher absolute value.
+We are removing values for two genes here, so it is unlikely to have a considerable impact on our results.
 
 ```{r}
 filtered_dge_mapped_df <- dge_mapped_df %>%
-  # Sort so that highest t-statistic values are at the top
-  dplyr::arrange(dplyr::desc(t)) %>%
+  # Sort so that the highest absolute values of the t-statistic are at the top
+  dplyr::arrange(dplyr::desc(abs(t))) %>%
   # Filter out the duplicated rows using `dplyr::distinct()`-- this will keep
   # the first row with the duplicated value thus keeping the row with the
-  # highest t-statistic value
+  # highest absolute value of the t-statistic
   dplyr::distinct(entrez_id, .keep_all = TRUE)
 ```
 
-Let's check to see that we removed the duplicate Entrez IDs and kept the row with the higher t-statistic value.
+Let's check to see that we removed the duplicate Entrez IDs and kept the rows with the higher absolute value of the t-statistic.
 
 ```{r}
 filtered_dge_mapped_df %>%
-  dplyr::filter(entrez_id == "336702")
+  dplyr::filter(entrez_id %in% dup_entrez_ids)
 ```
 
-Looks like we were able to successfully get rid of the duplicate gene identifiers and keep the observation with the higher t-statistic value!
-Note however, that a caveat in using this approach is that the genes that have duplicate identifiers could be enriched in a particular pathway/gene set and we may get an overly optimistic view of how perturbed that pathway truly is.
+Looks like we were able to successfully get rid of the duplicate gene identifiers and keep the observations with the higher absolute value of the t-statistic!
 
 **START REVIEW HERE**
 
@@ -330,15 +331,15 @@ Genes are ranked based on phenotype so we expect that sets related to a distinct
 
 ### Determine our pre-ranked genes list
 
-The `GSEA()` function takes a pre-ranked (sorted) named vector of statistics, where the names in the vector are gene identifiers.
-This is step 1 -- gene-level statistics.
+The `GSEA()` function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers.
+This is step 1 -- create a gene list with statistics that GSEA will rank by.
 
 ```{r}
 # Let's create a named vector ranked based on the t-statistic values -- we
-# already sorted in the t-stastic values in descending order in the previous
-# chunk so we won't need to do that here
+# need to sort the t-statistic values in descending order here
 t_vector <- filtered_dge_mapped_df$t
 names(t_vector) <- filtered_dge_mapped_df$entrez_id
+t_vector <- sort(t_vector, decreasing = TRUE)
 ```
 
 Let's preview our pre-ranked named vector.
@@ -350,11 +351,10 @@ head(t_vector)
 
 ### Run GSEA using the `GSEA()` function
 
-The enrichment score (ES) for a pathway, a pathway-level statistic, is calculated using our gene-level statistics.
+The enrichment score (ES) is a pathway-level statistic, calculated using our gene-level statistics.
 Genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic.
-A running score is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway.
 The ES is the maximum deviation from zero.
-Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing.
+An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway.
 Normalized enrichment scores (NES) are enrichment scores that are scaled to make gene sets that contain different number of genes comparable [@Subramanian2005; @Korotkevich2019].
 
 We can use the `GSEA()` function to perform GSEA with any generic set of gene sets, but there are several functions for using specific, commonly used gene sets (e.g., `gseKEGG()`).
@@ -383,6 +383,7 @@ Let's take a look at the results.
 head(gsea_results@result)
 ```
 
+Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing.
 Looks like we have gene sets returned as significant at FDR of `0.05`.
 If we didn't have any, our visualizations below would show up blank as nothing would have met our `pvalueCutoff` above.
 
@@ -409,17 +410,21 @@ gsea_result_df %>%
   dplyr::arrange(dplyr::desc(NES))
 ```
 
-The gene set `HALLMARK_TNFA_SIGNALING_VIA_NFKB` has the highest NES score.
+The gene set `HALLMARK_TNFA_SIGNALING_VIA_NFKB` has the highest positive NES score.
 
 ```{r}
-enrichplot::gseaplot(gsea_results,
+highly_positive_nes_plot <- enrichplot::gseaplot(
+  gsea_results,
   geneSetID = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
   title = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
   color.line = "#0d76ff"
 )
+
+highly_positive_nes_plot
 ```
 
 Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores.
+The red dashed line indicates the peak ES score (the score with the max deviation from 0).
 
 The plots returned by `enrichplot::gseaplot` are ggplots, so we can use `ggplot2::ggsave()` to save them to file.
 
@@ -427,7 +432,7 @@ Let's save to PNG.
 
 ```{r}
 ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
-  plot = ggplot2::last_plot()
+  plot = highly_positive_nes_plot
 )
 ```
 
@@ -444,20 +449,24 @@ gsea_result_df %>%
 The gene set `HALLMARK_E2F_TARGETS` has a highly negative NES.
 
 ```{r}
-enrichplot::gseaplot(gsea_results,
+highly_negative_nes_plot <- enrichplot::gseaplot(
+  gsea_results,
   geneSetID = "HALLMARK_E2F_TARGETS",
   title = "HALLMARK_E2F_TARGETS",
   color.line = "#0d76ff"
 )
+
+highly_negative_nes_plot
 ```
 
 This gene set shows the opposite pattern -- genes in the pathway tend to be on the right side of the graph.
+Again, the red dashed line here indicates the peak ES score (the score with the max deviation from 0).
 
 Let's save this plot to PNG.
 
 ```{r}
 ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
-  plot = ggplot2::last_plot()
+  plot = highly_negative_nes_plot
 )
 ```
 
diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html
index 25d197d9..8696df37 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.html
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.html
@@ -2931,7 +2931,7 @@
 Eg[h.id]||(h&&"AW"===h.prefix&&!e.adData&&2<=h.C.length?(e.adData={ak:h.C[0],cl:h.C[1]},Eg[h.id]=!0):h&&"UA"===h.prefix&&!e.gaData&&(e.gaData={gaWpid:h.containerId},Eg[h.id]=!0))}(e.gaData||e.adData)&&zg(Dg,d)(d.na,e,d.options)}},Hg=function(){var a=!1;return a},Ig=function(a,b){if(a)if(vg()){}else{if(g(a)){var c=
 sg(a);if(!c)return;a=c}var d=void 0,e=!1,f=b.getWithConfig(C.Yf);if(f&&za(f)){d=[];for(var h=0;hc.indexOf("Safari")||/Chrome|Coast|Opera|Edg|Silk|Android/.test(c)||11>((/Version\/([\d]+)/.exec(c)||[])[1]||"")?!1:!0}a=!b}if(a)return-1;var d=Ga("5");return Ae(1,100)c.indexOf("Safari")||/Chrome|Coast|Opera|Edg|Silk|Android/.test(c)||11>((/Version\/([\d]+)/.exec(c)||[])[1]||"")?!1:!0}a=!b}if(a)return-1;var d=Ga("1");return Ae(1,100)2.2 Set up your analysis folders<
 if (!dir.exists("data")) {
   dir.create("data")
 }
-
-# Define the file path to the plots directory
-plots_dir <- "plots" # Can replace with path to desired output plots directory
-
-# Create the plots folder if it doesn't exist
-if (!dir.exists(plots_dir)) {
-  dir.create(plots_dir)
-}
-
-# Define the file path to the results directory
-results_dir <- "results" # Can replace with path to desired output results directory
-
-# Create the results folder if it doesn't exist
-if (!dir.exists(results_dir)) {
-  dir.create(results_dir)
-}
    +# Define the file path to the plots directory +plots_dir <- "plots" # Can replace with path to desired output plots directory +# Create the plots folder if it doesn't exist +if (!dir.exists(plots_dir)) { + dir.create(plots_dir) +} +# Define the file path to the results directory +results_dir <- "results" # Can replace with path to desired output results directory +# Create the results folder if it doesn't exist +if (!dir.exists(results_dir)) { + dir.create(results_dir) +}

    In the same place you put this .Rmd file, you should now have three new empty folders called data, plots, and results!

    @@ -3847,16 +3843,14 @@

    4.1 Install libraries

    # Install this package if it isn't installed yet BiocManager::install("clusterProfiler", update = FALSE) } - -if (!("msigdbr" %in% installed.packages())) { - # Install this package if it isn't installed yet - BiocManager::install("msigdbr", update = FALSE) -} - -if (!("org.Dr.eg.db" %in% installed.packages())) { - # Install this package if it isn't installed yet - BiocManager::install("org.Dr.eg.db", update = FALSE) -}
    +if (!("msigdbr" %in% installed.packages())) { + # Install this package if it isn't installed yet + BiocManager::install("msigdbr", update = FALSE) +} +if (!("org.Dr.eg.db" %in% installed.packages())) { + # Install this package if it isn't installed yet + BiocManager::install("org.Dr.eg.db", update = FALSE) +}

    Attach the packages we need for this analysis.

    # Attach the library
 library(clusterProfiler)
    @@ -3872,9 +3866,8 @@

    4.1 Install libraries

    ## filter 
    # Package that contains MSigDB gene sets in tidy format
 library(msigdbr)
-
-# Zebrafish annotation package we'll use for gene identifier conversion
-library(org.Dr.eg.db)
    +# Zebrafish annotation package we'll use for gene identifier conversion +library(org.Dr.eg.db) 
    ## Loading required package: AnnotationDbi
    ## Loading required package: stats4
    ## Loading required package: BiocGenerics
    @@ -3926,23 +3919,20 @@

    4.1 Install libraries

    ## 
    # We will need this so we can use the pipe: %>%
 library(magrittr)
    -

    The GSEA algorithm utilizes random sampling so we are going to set the seed to make our results reproducible.

    -
    # Set the seed so our results are reproducible:
-set.seed(12345)

    4.2 Import data

    -

    We will read in the differential expression results we will download from online. These results are from a zebrafish microarray experiment we used for differential expression analysis for two groups using limma (Ritchie et al. 2015). The table contains summary statistics including Ensembl gene IDs, t-statistic values, and adjusted p-values (FDR in this case). We can identify differentially regulated genes by filtering these results and use this list as input to GSEA.

    +

    We will read in the differential expression results we will download from online. These results are from a zebrafish microarray experiment we used for differential expression analysis for two groups using limma (Ritchie et al. 2015). The table contains summary statistics including Ensembl gene IDs, t-statistic values for each group, and adjusted p-values (FDR in this case). We can identify differentially regulated genes by filtering these results and use this list as input to GSEA.

    Instead of using the URL below, you can use a file path to a TSV file with your desired gene list results. First we will assign the URL to its own variable called, dge_url.

    -
    # Define the url to your differential expression results file
-dge_url <- "https://refinebio-examples.s3.us-east-2.amazonaws.com/02-microarray/results/GSE71270/GSE71270_limma_results.tsv"
    +
    # Define the url to your differential expression results file
+dge_url <- "https://refinebio-examples.s3.us-east-2.amazonaws.com/02-microarray/results/GSE71270/GSE71270_limma_results.tsv"

    Read in the file that has differential expression results. Here we are using the URL we set up above, but this can be a local file path instead i.e. you can replace dge_url in the code below with a path to file you have on your computer like: file.path("results", "GSE71270_limma_results.tsv").

    -
    # Read in the contents of your differential expression results file
-# `dge_url` can be replaced with a file path to a TSV file with your
-# desired gene list results
-dge_df <- readr::read_tsv(dge_url)
    +
    # Read in the contents of your differential expression results file
+# `dge_url` can be replaced with a file path to a TSV file with your
+# desired gene list results
+dge_df <- readr::read_tsv(dge_url)
    ## 
-## ── Column specification ────────────────────────────────────────────────────────────────────────────────
+## ── Column specification ───────────────────────────────────────────────────────────────────────────
 ## cols(
 ##   Gene = col_character(),
 ##   logFC = col_double(),
@@ -3953,7 +3943,7 @@ 
    4.2 Import data
    
 ##   B = col_double()
 ## )

    read_tsv() can read TSV files online and doesn’t necessarily require you download the file first. Let’s take a look at what these contrast results from the differential expression analysis look like.

    -
    dge_df
    +
    dge_df
    +
    +

    Looks like we have a data frame of gene sets with associated gene symbols and Entrez IDs.

    +

    In our differential expression results data frame, dge_df we have Ensembl gene identifiers. So we will need to convert our Ensembl IDs into either gene symbols or Entrez IDs for GSEA. This conversation was marked as resolved by cbethell

    4.4 Gene identifier conversion

    @@ -4015,9 +4013,11 @@

    4.4 Gene identifier conversion) %>% # If an Ensembl gene identifier doesn't map to a Entrez gene identifier, drop that # from the data frame - dplyr::filter(!is.na(entrez_id)) %>% # Make an `Ensembl` column to store the rownames - tibble::rownames_to_column("Ensembl") %>% # Now let's join the rest of the expression data - dplyr::inner_join(dge_df, by = c("Ensembl" = "Gene"))

    + dplyr::filter(!is.na(entrez_id)) %>% + # Make an `Ensembl` column to store the rownames + tibble::rownames_to_column("Ensembl") %>% + # Now let's join the rest of the expression data + dplyr::inner_join(dge_df, by = c("Ensembl" = "Gene")) 
    ## 'select()' returned 1:many mapping between keys and columns

    This 1:many mapping between keys and columns message means that some Ensembl gene identifiers map to multiple Entrez IDs. In this case, it’s also possible that a Entrez ID will map to multiple Ensembl IDs. For the purpose of performing GSEA later in this notebook, we keep only the first mapped IDs. For more about how to explore this, take a look at our microarray gene ID conversion example.

    Let’s see a preview of dge_mapped_df.

    @@ -4027,40 +4027,41 @@

    4.4 Gene identifier conversion -

    Now let’s check to see if we have any Entrez IDs that mapped to multiple Ensembl IDs.

    +

    We do not want duplicated gene identifiers for the GSEA steps later as the GSEA() function will throw a warning that having duplicate gene names may produce unexpected results. Let’s check to see if we have any Entrez IDs that mapped to multiple Ensembl IDs.

    any(duplicated(dge_mapped_df$entrez_id))
    ## [1] TRUE

    Looks like we do have duplicated Entrez IDs. Let’s find out which Entrez IDs have been duplicated.

    -
    dge_mapped_df %>%
+
    dup_entrez_ids <- dge_mapped_df %>%
   dplyr::filter(duplicated(entrez_id)) %>%
-  dplyr::pull(entrez_id)
    
+  dplyr::pull(entrez_id)
+dup_entrez_ids
    ## [1] "336702" "57924"
    -

    Now let’s take a look at the rows associated with one of the duplicated Entrez IDs.

    +

    Now let’s take a look at the rows associated with the duplicated Entrez IDs.

    dge_mapped_df %>%
-  dplyr::filter(entrez_id == "336702")
    + dplyr::filter(entrez_id %in% dup_entrez_ids)
    -

    We can see that the associated values vary for each row although the Entrez ID is the same.

    -

    We do not want duplicated gene identifiers for the GSEA steps later, so let’s keep the Entrez IDs associated with the higher t-statistic value.

    +

    We can see that the associated values vary for each row.

    +

    As we mentioned earlier, we will want to remove duplicated gene identifiers in preparation for the GSEA steps later, so let’s keep the Entrez IDs associated with the higher absolute value of the t-statistic. GSEA relies on genes’ rankings on the basis of this statistic. Retaining the instance of the Entrez ID with the higher absolute value means that we will retain the value that is likely to be more highly- or lowly-ranked or, put another way, less likely to be towards the middle of the ranked gene list. We should keep this decision in mind when interpreting our results. For example, if the duplicate identifiers tended to be enriched in a particular gene set, we may get an optimistic view of how perturbed that gene set is by preferentially selecting values that have a higher absolute value. We are removing values for two genes here, so it is unlikely to have a considerable impact on our results.

    filtered_dge_mapped_df <- dge_mapped_df %>%
-  # Sort so that highest t-statistic values are at the top
-  dplyr::arrange(dplyr::desc(t)) %>%
+  # Sort so that the highest absolute values of the t-statistic are at the top
+  dplyr::arrange(dplyr::desc(abs(t))) %>%
   # Filter out the duplicated rows using `dplyr::distinct()`-- this will keep
   # the first row with the duplicated value thus keeping the row with the
-  # highest t-statistic value
+  # highest absolute value of the t-statistic
   dplyr::distinct(entrez_id, .keep_all = TRUE)
    -

    Let’s check to see that we removed the duplicate Entrez IDs and kept the row with the higher t-statistic value.

    +

    Let’s check to see that we removed the duplicate Entrez IDs and kept the rows with the higher absolute value of the t-statistic.

    filtered_dge_mapped_df %>%
-  dplyr::filter(entrez_id == "336702")
    + dplyr::filter(entrez_id %in% dup_entrez_ids)
    -

    Looks like we were able to successfully get rid of the duplicate gene identifiers and keep the observation with the higher t-statistic value! Note however, that a caveat in using this approach is that the genes that have duplicate identifiers could be enriched in a particular pathway/gene set and we may get an overly optimistic view of how perturbed that pathway truly is.

    +

    Looks like we were able to successfully get rid of the duplicate gene identifiers and keep the observations with the higher absolute value of the t-statistic!

    START REVIEW HERE

    @@ -4068,12 +4069,12 @@

    4.5 Perform gene set enrichment a

    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout the pre-ranked gene list or primarily found at the top or bottom of the list. Genes are ranked based on phenotype so we expect that sets related to a distinct phenotype will tend to show the latter distribution (Yu).

    4.5.1 Determine our pre-ranked genes list

    -

    The GSEA() function takes a pre-ranked (sorted) named vector of statistics, where the names in the vector are gene identifiers. This is step 1 – gene-level statistics.

    +

    The GSEA() function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers. This is step 1 – create a gene list with statistics that GSEA will rank by.

    # Let's create a named vector ranked based on the t-statistic values -- we
-# already sorted in the t-stastic values in descending order in the previous
-# chunk so we won't need to do that here
-t_vector <- filtered_dge_mapped_df$t
-names(t_vector) <- filtered_dge_mapped_df$entrez_id
    +# need to sort the t-statistic values in descending order here +t_vector <- filtered_dge_mapped_df$t +names(t_vector) <- filtered_dge_mapped_df$entrez_id +t_vector <- sort(t_vector, decreasing = TRUE)

    Let’s preview our pre-ranked named vector.

    # Look at first entries of the ranked t-statistic vector
 head(t_vector)
    @@ -4082,7 +4083,7 @@

    4.5.1 Determine our pre-ranked ge

    4.5.2 Run GSEA using the GSEA() function

    -

    The enrichment score (ES) for a pathway, a pathway-level statistic, is calculated using our gene-level statistics. Genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic. A running score is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. The ES is the maximum deviation from zero. Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Normalized enrichment scores (NES) are enrichment scores that are scaled to make gene sets that contain different number of genes comparable (Korotkevich et al. 2019; Subramanian et al. 2005).

    +

    The enrichment score (ES) is a pathway-level statistic, calculated using our gene-level statistics. Genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic. The ES is the maximum deviation from zero. An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. Normalized enrichment scores (NES) are enrichment scores that are scaled to make gene sets that contain different number of genes comparable (Korotkevich et al. 2019; Subramanian et al. 2005).

    We can use the GSEA() function to perform GSEA with any generic set of gene sets, but there are several functions for using specific, commonly used gene sets (e.g., gseKEGG()).

    gsea_results <- GSEA(
   geneList = t_vector, # ordered ranked gene list
@@ -4107,10 +4108,10 @@ 
    4.5.2 Run GSEA using the GS
 head(gsea_results@result)
    -

    Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    +

    Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    The information we’re most likely interested in is in the results slot. Let’s convert this into a data frame that we can use for further analysis and write to file.

    gsea_result_df <- data.frame(gsea_results@result)
    @@ -4127,21 +4128,24 @@

    4.6.1 Highly Positive NES

    dplyr::arrange(dplyr::desc(NES))
    -

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest NES score.

    -
    enrichplot::gseaplot(gsea_results,
-  geneSetID = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
-  title = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
-  color.line = "#0d76ff"
-)
    -

    -

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores.

    +

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest positive NES score.

    +
    highly_positive_nes_plot <- enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
+  title = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
+  color.line = "#0d76ff"
+)
+
+highly_positive_nes_plot
    +

    +

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the score with the max deviation from 0).

    The plots returned by enrichplot::gseaplot are ggplots, so we can use ggplot2::ggsave() to save them to file.

    Let’s save to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
-  plot = ggplot2::last_plot()
+  plot = highly_positive_nes_plot
 )
    ## Saving 7 x 5 in image
    @@ -4153,20 +4157,23 @@

    4.6.2 Highly Negative NES

    dplyr::arrange(NES)

    The gene set HALLMARK_E2F_TARGETS has a highly negative NES.

    -
    enrichplot::gseaplot(gsea_results,
-  geneSetID = "HALLMARK_E2F_TARGETS",
-  title = "HALLMARK_E2F_TARGETS",
-  color.line = "#0d76ff"
-)
    -

    -

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph.

    +
    highly_negative_nes_plot <- enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "HALLMARK_E2F_TARGETS",
+  title = "HALLMARK_E2F_TARGETS",
+  color.line = "#0d76ff"
+)
+
+highly_negative_nes_plot
    +

    +

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the peak ES score (the score with the max deviation from 0).

    Let’s save this plot to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
-  plot = ggplot2::last_plot()
+  plot = highly_negative_nes_plot
 )
    ## Saving 7 x 5 in image
    @@ -4206,7 +4213,7 @@

    6 Session info

    ## collate en_US.UTF-8 ## ctype en_US.UTF-8 ## tz Etc/UTC -## date 2020-11-05 +## date 2020-11-06 ## ## ─ Packages ─────────────────────────────────────────────────────────────────── ## package * version date lib source From 0656ac447c4afe0b01c12b854a5ea89f7a6ae26b Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Fri, 6 Nov 2020 08:51:52 -0500 Subject: [PATCH 09/19] rerun Snakefile --- .../pathway-analysis_microarray_03_gsea.html | 130 +++++++++++++++++- 1 file changed, 125 insertions(+), 5 deletions(-) diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html index bbf4c145..890e8695 100644 --- a/02-microarray/pathway-analysis_microarray_03_gsea.html +++ b/02-microarray/pathway-analysis_microarray_03_gsea.html @@ -4074,17 +4074,133 @@

    4.4 Gene identifier conversion

    4.5 Perform gene set enrichment analysis (GSEA)

    -

    Addressed in upcoming PR

    +

    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout the pre-ranked gene list or primarily found at the top or bottom of the list. Genes are ranked based on phenotype so we expect that sets related to a distinct phenotype will tend to show the latter distribution (Yu).

    +
    +

    4.5.1 Determine our pre-ranked genes list

    +

    The GSEA() function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers. This is step 1 – create a gene list with statistics that GSEA will rank by.

    +
    # Let's create a named vector ranked based on the t-statistic values -- we
+# need to sort the t-statistic values in descending order here
+t_vector <- filtered_dge_mapped_df$t
+names(t_vector) <- filtered_dge_mapped_df$entrez_id
+t_vector <- sort(t_vector, decreasing = TRUE)
    +

    Let’s preview our pre-ranked named vector.

    +
    # Look at first entries of the ranked t-statistic vector
+head(t_vector)
    +
    ##   555053   140633   407728   368924   335916   323329 
+## 20.22172 14.48634 13.88657 12.45258 11.24450 10.92140
    +
    +
    +

    4.5.2 Run GSEA using the GSEA() function

    +

    The enrichment score (ES) is a pathway-level statistic, calculated using our gene-level statistics. Genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic. The ES is the maximum deviation from zero. An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. Normalized enrichment scores (NES) are enrichment scores that are scaled to make gene sets that contain different number of genes comparable (Korotkevich et al. 2019; Subramanian et al. 2005).

    +

    We can use the GSEA() function to perform GSEA with any generic set of gene sets, but there are several functions for using specific, commonly used gene sets (e.g., gseKEGG()).

    +
    gsea_results <- GSEA(
+  geneList = t_vector, # ordered ranked gene list
+  minGSSize = 25, # minimum gene set size
+  maxGSSize = 500, # maximum gene set set
+  pvalueCutoff = 0.05, # p value cutoff
+  eps = 0, # boundary for calculating the p value
+  seed = TRUE, # set seed to make results reproducible
+  pAdjustMethod = "BH", # Benjamini-Hochberg correction
+  TERM2GENE = dplyr::select(
+    dr_hallmark_df,
+    gs_name,
+    entrez_gene
+  )
+)
    +
    ## preparing geneSet collections...
    +
    ## GSEA analysis...
    +
    ## leading edge analysis...
    +
    ## done...
    +

    Let’s take a look at the results.

    +
    # We can access the results from our gseaResult object using `@result`
+head(gsea_results@result)
    +
    + +
    +

    Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    +

    The information we’re most likely interested in is in the results slot. Let’s convert this into a data frame that we can use for further analysis and write to file.

    +
    gsea_result_df <- data.frame(gsea_results@result)
    +

    4.6 Visualizing results

    -

    Addressed in upcoming PR

    +

    We can visualize GSEA results for individual pathways or gene sets using enrichplot::gseaplot(). Let’s take a look at 2 different pathways – one with a highly positive NES and one with a highly negative NES – to get more insight into how ES are calculated.

    +
    +

    4.6.1 Highly Positive NES

    +

    Let’s look for the gene set with the highest positive NES.

    +
    gsea_result_df %>%
+  # Combining the `dplyr::desc()` function with `dplyr::arrange()` sorts the
+  # values in descending order
+  dplyr::arrange(dplyr::desc(NES))
    +
    + +
    +

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest positive NES score.

    +
    highly_positive_nes_plot <- enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
+  title = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
+  color.line = "#0d76ff"
+)
+
+highly_positive_nes_plot
    +

    +

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the score with the max deviation from 0).

    +

    The plots returned by enrichplot::gseaplot are ggplots, so we can use ggplot2::ggsave() to save them to file.

    +

    Let’s save to PNG.

    +
    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
+  plot = highly_positive_nes_plot
+)
    +
    ## Saving 7 x 5 in image
    +
    +
    +

    4.6.2 Highly Negative NES

    +

    Let’s look for the gene set with the highest negative NES.

    +
    gsea_result_df %>%
+  # The `dplyr::arrange()` function sorts the values in ascending order
+  dplyr::arrange(NES)
    +
    + +
    +

    The gene set HALLMARK_E2F_TARGETS has a highly negative NES.

    +
    highly_negative_nes_plot <- enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "HALLMARK_E2F_TARGETS",
+  title = "HALLMARK_E2F_TARGETS",
+  color.line = "#0d76ff"
+)
+
+highly_negative_nes_plot
    +

    +

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the peak ES score (the score with the max deviation from 0).

    +

    Let’s save this plot to PNG.

    +
    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
+  plot = highly_negative_nes_plot
+)
    +
    ## Saving 7 x 5 in image
    +
    +
    +
    +

    4.7 Write results to file

    +
    readr::write_tsv(
+  gsea_result_df,
+  file.path(
+    results_dir,
+    "GSE71270_gsea_results.tsv"
+  )
+)

    5 Resources for further learning

      -
    • clusterProfiler paper (Yu et al. 2012)
      • +
    • clusterProfiler paper (Yu et al. 2012).
    • clusterProfiler book (Yu).
    • This handy review which summarizes the different types of pathway analysis and their limitations (Khatri et al. 2012).
    • See this Broad Institute page for more on GSEA and MSigDB (Diego and Team).
      • @@ -4093,8 +4209,8 @@

      5 Resources for further learning<

      6 Session info

      At the end of every analysis, before saving your notebook, we recommend printing out your session info. This helps make your code more reproducible by recording what versions of software and packages you used to run this.

      -
      # Print session info
-sessioninfo::session_info()
      +
      # Print session info
+sessioninfo::session_info()
      ## ─ Session info ───────────────────────────────────────────────────────────────
 ##  setting  value                       
 ##  version  R version 4.0.2 (2020-06-22)
@@ -4162,6 +4278,7 @@ 
      6 Session info
      
 ##  IRanges         * 2.22.2   2020-05-21 [1] Bioconductor  
 ##  jsonlite          1.7.1    2020-09-07 [1] RSPM (R 4.0.2)
 ##  knitr             1.30     2020-09-22 [1] RSPM (R 4.0.2)
+##  labeling          0.3      2014-08-23 [1] RSPM (R 4.0.0)
 ##  lattice           0.20-41  2020-04-02 [2] CRAN (R 4.0.2)
 ##  lifecycle         0.2.0    2020-03-06 [1] RSPM (R 4.0.0)
 ##  magrittr        * 1.5      2014-11-22 [1] RSPM (R 4.0.0)
@@ -4236,6 +4353,9 @@ 
      References
      
 
      
 
      Khatri P., M. Sirota, and A. J. Butte, 2012 Ten years of pathway analysis: Current approaches and outstanding challenges. PLOS Computational Biology 8: e1002375. https://doi.org/10.1371/journal.pcbi.1002375
      
 
      
+
      
+
      Korotkevich G., V. Sukhov, and A. Sergushichev, 2019 Fast gene set enrichment analysis. bioRxiv. https://doi.org/10.1101/060012
      
+
      
 
      
 
      Liberzon A., C. Birger, H. Thorvaldsdóttir, M. Ghandi, and J. P. Mesirov et al., 2015 The molecular signatures database hallmark gene set collection. Cell Systems 1. https://doi.org/10.1016/j.cels.2015.12.004
      
 
      

From 73276a53c65aa82245746b14575d9252676330b4 Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Fri, 6 Nov 2020 14:02:36 -0500
Subject: [PATCH 10/19] incorporate suggested changes re additional
 context/GSEA explanation

---
 .../pathway-analysis_microarray_03_gsea.Rmd    | 18 +++++++++++-------
 .../pathway-analysis_microarray_03_gsea.html   | 15 ++++++++-------
 2 files changed, 19 insertions(+), 14 deletions(-)

diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
index ce58ec6d..f0e98e74 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
@@ -225,6 +225,8 @@ Looks like we have a data frame of gene sets with associated gene symbols and En
 In our differential expression results data frame, `dge_df` we have Ensembl gene identifiers. 
 So we will need to convert our Ensembl IDs into either gene symbols or Entrez IDs for GSEA.
 
+We will also want to keep in mind that GSEA requires that genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic.
+
 ## Gene identifier conversion
 
 We're going to convert our identifiers in `dge_df` to Entrez IDs, but you can, with the change of a single argument, use the same code to convert to many other types of identifiers!
@@ -333,8 +335,8 @@ Looks like we were able to successfully get rid of the duplicate gene identifier
 
 ## Perform gene set enrichment analysis (GSEA)
 
-The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout the pre-ranked gene list or primarily found at the top or bottom of the list.
-Genes are ranked based on phenotype so we expect that sets related to a distinct phenotype will tend to show the latter distribution [@clusterProfiler-book].
+The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list.
+As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes, so we expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list [@clusterProfiler-book; @Subramanian2005].
 
 ### Determine our pre-ranked genes list
 
@@ -358,10 +360,8 @@ head(t_vector)
 
 ### Run GSEA using the `GSEA()` function
 
-The enrichment score (ES) is a pathway-level statistic, calculated using our gene-level statistics.
 Genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic.
-The ES is the maximum deviation from zero.
-An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway.
+The enrichment score (ES) is a pathway-level statistic, calculated using our gene-level statistics.
 Normalized enrichment scores (NES) are enrichment scores that are scaled to make gene sets that contain different number of genes comparable [@Subramanian2005; @Korotkevich2019].
 
 We can use the `GSEA()` function to perform GSEA with any generic set of gene sets, but there are several functions for using specific, commonly used gene sets (e.g., `gseKEGG()`).
@@ -431,7 +431,9 @@ highly_positive_nes_plot
 ```
 
 Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores.
-The red dashed line indicates the peak ES score (the score with the max deviation from 0).
+The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero).
+An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway.
+In this case, the most highly positive enrichment scores are being displayed.
 
 The plots returned by `enrichplot::gseaplot` are ggplots, so we can use `ggplot2::ggsave()` to save them to file.
 
@@ -467,7 +469,9 @@ highly_negative_nes_plot
 ```
 
 This gene set shows the opposite pattern -- genes in the pathway tend to be on the right side of the graph.
-Again, the red dashed line here indicates the peak ES score (the score with the max deviation from 0).
+Again, the red dashed line here indicates the peak ES score (the ES is the maximum deviation from zero).
+An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway.
+In this case, the most highly negative enrichment scores are being displayed.
 
 Let's save this plot to PNG.
 
diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html
index 890e8695..e26c96ca 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.html
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.html
@@ -3993,6 +3993,7 @@ 
      4.3 Getting familiar with c

      Looks like we have a data frame of gene sets with associated gene symbols and Entrez IDs.

      In our differential expression results data frame, dge_df we have Ensembl gene identifiers. So we will need to convert our Ensembl IDs into either gene symbols or Entrez IDs for GSEA.

      +

      We will also want to keep in mind that GSEA requires that genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic.

    4.4 Gene identifier conversion

    @@ -4074,7 +4075,7 @@

    4.4 Gene identifier conversion

    4.5 Perform gene set enrichment analysis (GSEA)

    -

    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout the pre-ranked gene list or primarily found at the top or bottom of the list. Genes are ranked based on phenotype so we expect that sets related to a distinct phenotype will tend to show the latter distribution (Yu).

    +

    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list. As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes, so we expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list (Subramanian et al. 2005; Yu).

    4.5.1 Determine our pre-ranked genes list

    The GSEA() function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers. This is step 1 – create a gene list with statistics that GSEA will rank by.

    @@ -4091,7 +4092,7 @@

    4.5.1 Determine our pre-ranked ge

    4.5.2 Run GSEA using the GSEA() function

    -

    The enrichment score (ES) is a pathway-level statistic, calculated using our gene-level statistics. Genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic. The ES is the maximum deviation from zero. An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. Normalized enrichment scores (NES) are enrichment scores that are scaled to make gene sets that contain different number of genes comparable (Korotkevich et al. 2019; Subramanian et al. 2005).

    +

    Genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic. The enrichment score (ES) is a pathway-level statistic, calculated using our gene-level statistics. Normalized enrichment scores (NES) are enrichment scores that are scaled to make gene sets that contain different number of genes comparable (Korotkevich et al. 2019; Subramanian et al. 2005).

    We can use the GSEA() function to perform GSEA with any generic set of gene sets, but there are several functions for using specific, commonly used gene sets (e.g., gseKEGG()).

    gsea_results <- GSEA(
   geneList = t_vector, # ordered ranked gene list
@@ -4116,7 +4117,7 @@ 
    4.5.2 Run GSEA using the GS
 head(gsea_results@result)

    Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    @@ -4136,7 +4137,7 @@

    4.6.1 Highly Positive NES

    dplyr::arrange(dplyr::desc(NES))

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest positive NES score.

    @@ -4149,7 +4150,7 @@

    4.6.1 Highly Positive NES

    highly_positive_nes_plot

    -

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the score with the max deviation from 0).

    +

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly positive enrichment scores are being displayed.

    The plots returned by enrichplot::gseaplot are ggplots, so we can use ggplot2::ggsave() to save them to file.

    Let’s save to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
@@ -4165,7 +4166,7 @@ 
    4.6.2 Highly Negative NES
    
   dplyr::arrange(NES)

    The gene set HALLMARK_E2F_TARGETS has a highly negative NES.

    @@ -4178,7 +4179,7 @@

    4.6.2 Highly Negative NES

    highly_negative_nes_plot

    -

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the peak ES score (the score with the max deviation from 0).

    +

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly negative enrichment scores are being displayed.

    Let’s save this plot to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
   plot = highly_negative_nes_plot

From bbb565d1508044a755da1e6d8111ec551b87b18f Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Fri, 6 Nov 2020 14:23:11 -0500
Subject: [PATCH 11/19] implement `top_n()`

---
 .../pathway-analysis_microarray_03_gsea.Rmd    | 12 ++++++------
 .../pathway-analysis_microarray_03_gsea.html   | 18 +++++++++---------
 2 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
index f0e98e74..0101fe4f 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
@@ -412,9 +412,8 @@ Let's look for the gene set with the highest positive NES.
 
 ```{r}
 gsea_result_df %>%
-  # Combining the `dplyr::desc()` function with `dplyr::arrange()` sorts the
-  # values in descending order
-  dplyr::arrange(dplyr::desc(NES))
+  # Use `top_n()` to return the top n observation using `NES` as the ordering variable
+  dplyr::top_n(n = 1, wt = NES)
 ```
 
 The gene set `HALLMARK_TNFA_SIGNALING_VIA_NFKB` has the highest positive NES score.
@@ -451,11 +450,12 @@ Let's look for the gene set with the highest negative NES.
 
 ```{r}
 gsea_result_df %>%
-  # The `dplyr::arrange()` function sorts the values in ascending order
-  dplyr::arrange(NES)
+  # Use `top_n()` to return the top n observation using `NES` as the ordering
+  # variable -- note the use of `dplyr::desc()` to sort the values in descending order
+  dplyr::top_n(n = 1, wt = dplyr::desc(NES))
 ```
 
-The gene set `HALLMARK_E2F_TARGETS` has a highly negative NES.
+The gene set `HALLMARK_E2F_TARGETS` has the highest negative NES.
 
 ```{r}
 highly_negative_nes_plot <- enrichplot::gseaplot(
diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html
index e26c96ca..9a041647 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.html
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.html
@@ -4117,7 +4117,7 @@ 
    4.5.2 Run GSEA using the GS
 head(gsea_results@result)

    Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    @@ -4132,12 +4132,11 @@

    4.6 Visualizing results

    4.6.1 Highly Positive NES

    Let’s look for the gene set with the highest positive NES.

    gsea_result_df %>%
-  # Combining the `dplyr::desc()` function with `dplyr::arrange()` sorts the
-  # values in descending order
-  dplyr::arrange(dplyr::desc(NES))
    + # Use `top_n()` to return the top n observation using `NES` as the ordering variable + dplyr::top_n(n = 1, wt = NES)

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest positive NES score.

    @@ -4162,14 +4161,15 @@

    4.6.1 Highly Positive NES

    4.6.2 Highly Negative NES

    Let’s look for the gene set with the highest negative NES.

    gsea_result_df %>%
-  # The `dplyr::arrange()` function sorts the values in ascending order
-  dplyr::arrange(NES)
    + # Use `top_n()` to return the top n observation using `NES` as the ordering + # variable -- note the use of `dplyr::desc()` to sort the values in descending order + dplyr::top_n(n = 1, wt = dplyr::desc(NES))
    -

    The gene set HALLMARK_E2F_TARGETS has a highly negative NES.

    +

    The gene set HALLMARK_E2F_TARGETS has the highest negative NES.

    highly_negative_nes_plot <- enrichplot::gseaplot(
   gsea_results,
   geneSetID = "HALLMARK_E2F_TARGETS",

From 0d52ec405002f277b46a7a3b478f024c5a893704 Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Fri, 6 Nov 2020 14:26:01 -0500
Subject: [PATCH 12/19] add a bit more context for clarification re ES score

---
 02-microarray/pathway-analysis_microarray_03_gsea.Rmd  |  4 ++--
 02-microarray/pathway-analysis_microarray_03_gsea.html | 10 +++++-----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
index 0101fe4f..ca40423f 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
@@ -431,7 +431,7 @@ highly_positive_nes_plot
 
 Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores.
 The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero).
-An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway.
+An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway.
 In this case, the most highly positive enrichment scores are being displayed.
 
 The plots returned by `enrichplot::gseaplot` are ggplots, so we can use `ggplot2::ggsave()` to save them to file.
@@ -470,7 +470,7 @@ highly_negative_nes_plot
 
 This gene set shows the opposite pattern -- genes in the pathway tend to be on the right side of the graph.
 Again, the red dashed line here indicates the peak ES score (the ES is the maximum deviation from zero).
-An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway.
+An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway.
 In this case, the most highly negative enrichment scores are being displayed.
 
 Let's save this plot to PNG.
diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html
index 9a041647..d2e53cec 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.html
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.html
@@ -4117,7 +4117,7 @@ 
    4.5.2 Run GSEA using the GS
 head(gsea_results@result)

    Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    @@ -4136,7 +4136,7 @@

    4.6.1 Highly Positive NES

    dplyr::top_n(n = 1, wt = NES)

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest positive NES score.

    @@ -4149,7 +4149,7 @@

    4.6.1 Highly Positive NES

    highly_positive_nes_plot

    -

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly positive enrichment scores are being displayed.

    +

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly positive enrichment scores are being displayed.

    The plots returned by enrichplot::gseaplot are ggplots, so we can use ggplot2::ggsave() to save them to file.

    Let’s save to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
@@ -4166,7 +4166,7 @@ 
    4.6.2 Highly Negative NES
    
   dplyr::top_n(n = 1, wt = dplyr::desc(NES))

    The gene set HALLMARK_E2F_TARGETS has the highest negative NES.

    @@ -4179,7 +4179,7 @@

    4.6.2 Highly Negative NES

    highly_negative_nes_plot

    -

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly negative enrichment scores are being displayed.

    +

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly negative enrichment scores are being displayed.

    Let’s save this plot to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
   plot = highly_negative_nes_plot

From 968752634b551fac5553fa66b9839e6d185134bf Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Fri, 6 Nov 2020 14:42:35 -0500
Subject: [PATCH 13/19] update GSEA explanation before gene ID conversion
 section

---
 02-microarray/pathway-analysis_microarray_03_gsea.Rmd  |  4 +++-
 02-microarray/pathway-analysis_microarray_03_gsea.html | 10 +++++-----
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
index ca40423f..86233599 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
@@ -226,6 +226,8 @@ In our differential expression results data frame, `dge_df` we have Ensembl gene
 So we will need to convert our Ensembl IDs into either gene symbols or Entrez IDs for GSEA.
 
 We will also want to keep in mind that GSEA requires that genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic.
+GSEA also requires unique gene identifiers to produce the most accurate results.
+This means if any duplicate gene identifiers are found in our dataset, we will want to retain the instance with the higher absolute value as this will be the instance most likely to be ranked most highly negative or most highly positive.
 
 ## Gene identifier conversion
 
@@ -336,7 +338,7 @@ Looks like we were able to successfully get rid of the duplicate gene identifier
 ## Perform gene set enrichment analysis (GSEA)
 
 The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list.
-As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes, so we expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list [@clusterProfiler-book; @Subramanian2005].
+As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes, so we expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list [@clusterProfiler-book].
 
 ### Determine our pre-ranked genes list
 
diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html
index d2e53cec..bec9779b 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.html
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.html
@@ -3993,7 +3993,7 @@ 
    4.3 Getting familiar with c

    Looks like we have a data frame of gene sets with associated gene symbols and Entrez IDs.

    In our differential expression results data frame, dge_df we have Ensembl gene identifiers. So we will need to convert our Ensembl IDs into either gene symbols or Entrez IDs for GSEA.

    -

    We will also want to keep in mind that GSEA requires that genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic.

    +

    We will also want to keep in mind that GSEA requires that genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic. GSEA also requires unique gene identifiers to produce the most accurate results. This means if any duplicate gene identifiers are found in our dataset, we will want to retain the instance with the higher absolute value as this will be the instance most likely to be ranked most highly negative or most highly positive.

    4.4 Gene identifier conversion

    @@ -4075,7 +4075,7 @@

    4.4 Gene identifier conversion

    4.5 Perform gene set enrichment analysis (GSEA)

    -

    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list. As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes, so we expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list (Subramanian et al. 2005; Yu).

    +

    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list. As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes, so we expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list (Yu).

    4.5.1 Determine our pre-ranked genes list

    The GSEA() function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers. This is step 1 – create a gene list with statistics that GSEA will rank by.

    @@ -4117,7 +4117,7 @@

    4.5.2 Run GSEA using the GS head(gsea_results@result)

    Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    @@ -4136,7 +4136,7 @@

    4.6.1 Highly Positive NES

    dplyr::top_n(n = 1, wt = NES)

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest positive NES score.

    @@ -4166,7 +4166,7 @@

    4.6.2 Highly Negative NES

    dplyr::top_n(n = 1, wt = dplyr::desc(NES))

    The gene set HALLMARK_E2F_TARGETS has the highest negative NES.

    From 9267a4d4a6b639ff929ab59f5fb32ffaafb93af0 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Mon, 9 Nov 2020 15:52:26 -0500 Subject: [PATCH 14/19] incorporate @cansavvy's wording suggestions --- .../pathway-analysis_microarray_03_gsea.Rmd | 33 ++++++++--------- .../pathway-analysis_microarray_03_gsea.html | 35 ++++++++++--------- 2 files changed, 35 insertions(+), 33 deletions(-) diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd index 86233599..4ad34d15 100644 --- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd +++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd @@ -146,7 +146,7 @@ library(magrittr) We will read in the differential expression results we will download from online. These results are from a zebrafish microarray experiment we used for [differential expression analysis for two groups](https://alexslemonade.github.io/refinebio-examples/02-microarray/differential-expression_microarray_02_2-groups.html) using [`limma`](https://bioconductor.org/packages/release/bioc/html/limma.html) [@Ritchie2015]. -The table contains summary statistics including Ensembl gene IDs, t-statistic values for each group, and adjusted p-values (FDR in this case). +The table contains summary statistics including Ensembl gene IDs, t-statistic values, and adjusted p-values (FDR in this case). We can identify differentially regulated genes by filtering these results and use this list as input to GSEA. Instead of using the URL below, you can use a file path to a TSV file with your desired gene list results. @@ -225,10 +225,6 @@ Looks like we have a data frame of gene sets with associated gene symbols and En In our differential expression results data frame, `dge_df` we have Ensembl gene identifiers. So we will need to convert our Ensembl IDs into either gene symbols or Entrez IDs for GSEA. -We will also want to keep in mind that GSEA requires that genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic. -GSEA also requires unique gene identifiers to produce the most accurate results. -This means if any duplicate gene identifiers are found in our dataset, we will want to retain the instance with the higher absolute value as this will be the instance most likely to be ranked most highly negative or most highly positive. - ## Gene identifier conversion We're going to convert our identifiers in `dge_df` to Entrez IDs, but you can, with the change of a single argument, use the same code to convert to many other types of identifiers! @@ -281,8 +277,11 @@ Let's see a preview of `dge_mapped_df`. ```{r} head(dge_mapped_df) ``` +We will want to keep in mind that GSEA requires that genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic. +GSEA also requires unique gene identifiers to produce the most accurate results. +This means if any duplicate gene identifiers are found in our dataset, we will want to retain the instance with the higher absolute value as this will be the instance most likely to be ranked most highly negative or most highly positive. +Otherwise the enrichment score results may be skewed and the `GSEA()` function will throw a warning. -We do not want duplicated gene identifiers for the GSEA steps later as the `GSEA()` function will throw a warning that having duplicate gene names may produce unexpected results. Let's check to see if we have any Entrez IDs that mapped to multiple Ensembl IDs. ```{r} @@ -311,7 +310,7 @@ We can see that the associated values vary for each row. As we mentioned earlier, we will want to remove duplicated gene identifiers in preparation for the GSEA steps later, so let's keep the Entrez IDs associated with the higher absolute value of the t-statistic. GSEA relies on genes' rankings on the basis of this statistic. -Retaining the instance of the Entrez ID with the higher absolute value means that we will retain the value that is likely to be more highly- or lowly-ranked or, put another way, less likely to be towards the middle of the ranked gene list. +Retaining the instance of the Entrez ID with the higher absolute value means that we will retain the value that is likely to be more highly- or lowly-ranked or, put another way, the values less likely to be towards the middle of the ranked gene list. We should keep this decision in mind when interpreting our results. For example, if the duplicate identifiers tended to be enriched in a particular gene set, we may get an optimistic view of how perturbed that gene set is by preferentially selecting values that have a higher absolute value. We are removing values for two genes here, so it is unlikely to have a considerable impact on our results. @@ -338,7 +337,8 @@ Looks like we were able to successfully get rid of the duplicate gene identifier ## Perform gene set enrichment analysis (GSEA) The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list. -As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes, so we expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list [@clusterProfiler-book]. +As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes. +We therefore expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list [@clusterProfiler-book]. ### Determine our pre-ranked genes list @@ -415,7 +415,7 @@ Let's look for the gene set with the highest positive NES. ```{r} gsea_result_df %>% # Use `top_n()` to return the top n observation using `NES` as the ordering variable - dplyr::top_n(n = 1, wt = NES) + dplyr::top_n(n = 3, wt = NES) ``` The gene set `HALLMARK_TNFA_SIGNALING_VIA_NFKB` has the highest positive NES score. @@ -434,7 +434,7 @@ highly_positive_nes_plot Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. -In this case, the most highly positive enrichment scores are being displayed. +In this case, the most highly positive enrichment score's data are being displayed. The plots returned by `enrichplot::gseaplot` are ggplots, so we can use `ggplot2::ggsave()` to save them to file. @@ -448,16 +448,17 @@ ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"), ### Highly Negative NES -Let's look for the gene set with the highest negative NES. +Let's look for the gene set with the most negative NES. ```{r} gsea_result_df %>% # Use `top_n()` to return the top n observation using `NES` as the ordering - # variable -- note the use of `dplyr::desc()` to sort the values in descending order - dplyr::top_n(n = 1, wt = dplyr::desc(NES)) + # variable -- note the use of `-n` to display the top n values when sorted + # in descending order + dplyr::top_n(n = -3, wt = NES) ``` -The gene set `HALLMARK_E2F_TARGETS` has the highest negative NES. +The gene set `HALLMARK_E2F_TARGETS` has the most negative NES. ```{r} highly_negative_nes_plot <- enrichplot::gseaplot( @@ -471,9 +472,9 @@ highly_negative_nes_plot ``` This gene set shows the opposite pattern -- genes in the pathway tend to be on the right side of the graph. -Again, the red dashed line here indicates the peak ES score (the ES is the maximum deviation from zero). +Again, the red dashed line here indicates the maximum deviation from zero, in other words, the most negative ES score. An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. -In this case, the most highly negative enrichment scores are being displayed. +In this case, the most highly negative enrichment score's data are being displayed. Let's save this plot to PNG. diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html index bec9779b..f2f88fca 100644 --- a/02-microarray/pathway-analysis_microarray_03_gsea.html +++ b/02-microarray/pathway-analysis_microarray_03_gsea.html @@ -3930,7 +3930,7 @@

    4.1 Install libraries

    4.2 Import data

    -

    We will read in the differential expression results we will download from online. These results are from a zebrafish microarray experiment we used for differential expression analysis for two groups using limma (Ritchie et al. 2015). The table contains summary statistics including Ensembl gene IDs, t-statistic values for each group, and adjusted p-values (FDR in this case). We can identify differentially regulated genes by filtering these results and use this list as input to GSEA.

    +

    We will read in the differential expression results we will download from online. These results are from a zebrafish microarray experiment we used for differential expression analysis for two groups using limma (Ritchie et al. 2015). The table contains summary statistics including Ensembl gene IDs, t-statistic values, and adjusted p-values (FDR in this case). We can identify differentially regulated genes by filtering these results and use this list as input to GSEA.

    Instead of using the URL below, you can use a file path to a TSV file with your desired gene list results. First we will assign the URL to its own variable called, dge_url.

    # Define the url to your differential expression results file
 dge_url <- "https://refinebio-examples.s3.us-east-2.amazonaws.com/02-microarray/results/GSE71270/GSE71270_limma_results.tsv"
    @@ -3940,7 +3940,7 @@

    4.2 Import data

    # desired gene list results dge_df <- readr::read_tsv(dge_url)
    ## 
-## ── Column specification ───────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────────────────────────
 ## cols(
 ##   Gene = col_character(),
 ##   logFC = col_double(),
@@ -3993,7 +3993,6 @@ 
    4.3 Getting familiar with c
 
 
    Looks like we have a data frame of gene sets with associated gene symbols and Entrez IDs.
    
 
    In our differential expression results data frame, dge_df we have Ensembl gene identifiers. So we will need to convert our Ensembl IDs into either gene symbols or Entrez IDs for GSEA.
    
-
    We will also want to keep in mind that GSEA requires that genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic. GSEA also requires unique gene identifiers to produce the most accurate results. This means if any duplicate gene identifiers are found in our dataset, we will want to retain the instance with the higher absolute value as this will be the instance most likely to be ranked most highly negative or most highly positive.
    
 
 
    
 
    4.4 Gene identifier conversion
    
@@ -4036,7 +4035,8 @@ 
    4.4 Gene identifier conversion
 
    
-
    We do not want duplicated gene identifiers for the GSEA steps later as the GSEA() function will throw a warning that having duplicate gene names may produce unexpected results. Let’s check to see if we have any Entrez IDs that mapped to multiple Ensembl IDs.
    
+
    We will want to keep in mind that GSEA requires that genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic. GSEA also requires unique gene identifiers to produce the most accurate results. This means if any duplicate gene identifiers are found in our dataset, we will want to retain the instance with the higher absolute value as this will be the instance most likely to be ranked most highly negative or most highly positive. Otherwise the enrichment score results may be skewed and the GSEA() function will throw a warning.
    
+
    Let’s check to see if we have any Entrez IDs that mapped to multiple Ensembl IDs.
    
 
    any(duplicated(dge_mapped_df$entrez_id))
    
 
    ## [1] TRUE
    
 
    Looks like we do have duplicated Entrez IDs. Let’s find out which Entrez IDs have been duplicated.
    
@@ -4055,7 +4055,7 @@ 
    4.4 Gene identifier conversion
 
 
    We can see that the associated values vary for each row.
    
-
    As we mentioned earlier, we will want to remove duplicated gene identifiers in preparation for the GSEA steps later, so let’s keep the Entrez IDs associated with the higher absolute value of the t-statistic. GSEA relies on genes’ rankings on the basis of this statistic. Retaining the instance of the Entrez ID with the higher absolute value means that we will retain the value that is likely to be more highly- or lowly-ranked or, put another way, less likely to be towards the middle of the ranked gene list. We should keep this decision in mind when interpreting our results. For example, if the duplicate identifiers tended to be enriched in a particular gene set, we may get an optimistic view of how perturbed that gene set is by preferentially selecting values that have a higher absolute value. We are removing values for two genes here, so it is unlikely to have a considerable impact on our results.
    
+
    As we mentioned earlier, we will want to remove duplicated gene identifiers in preparation for the GSEA steps later, so let’s keep the Entrez IDs associated with the higher absolute value of the t-statistic. GSEA relies on genes’ rankings on the basis of this statistic. Retaining the instance of the Entrez ID with the higher absolute value means that we will retain the value that is likely to be more highly- or lowly-ranked or, put another way, the values less likely to be towards the middle of the ranked gene list. We should keep this decision in mind when interpreting our results. For example, if the duplicate identifiers tended to be enriched in a particular gene set, we may get an optimistic view of how perturbed that gene set is by preferentially selecting values that have a higher absolute value. We are removing values for two genes here, so it is unlikely to have a considerable impact on our results.
    
 
    filtered_dge_mapped_df <- dge_mapped_df %>%
   # Sort so that the highest absolute values of the t-statistic are at the top
   dplyr::arrange(dplyr::desc(abs(t))) %>%
@@ -4075,7 +4075,7 @@ 
    4.4 Gene identifier conversion
 
    
 
    4.5 Perform gene set enrichment analysis (GSEA)
    
-
    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list. As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes, so we expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list (Yu).
    
+
    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list. As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes. We therefore expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list (Yu).
    
 
    
 
    4.5.1 Determine our pre-ranked genes list
    
 
    The GSEA() function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers. This is step 1 – create a gene list with statistics that GSEA will rank by.
    
@@ -4117,7 +4117,7 @@ 
    4.5.2 Run GSEA using the GS
 head(gsea_results@result)
    
 
    
 
 
    
 
    Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.
    
@@ -4133,10 +4133,10 @@ 
    4.6.1 Highly Positive NES
    
 
    Let’s look for the gene set with the highest positive NES.
    
 
    gsea_result_df %>%
   # Use `top_n()` to return the top n observation using `NES` as the ordering variable
-  dplyr::top_n(n = 1, wt = NES)
    
+  dplyr::top_n(n = 3, wt = NES)

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest positive NES score.

    @@ -4149,7 +4149,7 @@

    4.6.1 Highly Positive NES

    highly_positive_nes_plot

    -

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly positive enrichment scores are being displayed.

    +

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly positive enrichment score’s data are being displayed.

    The plots returned by enrichplot::gseaplot are ggplots, so we can use ggplot2::ggsave() to save them to file.

    Let’s save to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
@@ -4159,17 +4159,18 @@ 
    4.6.1 Highly Positive NES

    4.6.2 Highly Negative NES

    -

    Let’s look for the gene set with the highest negative NES.

    +

    Let’s look for the gene set with the most negative NES.

    gsea_result_df %>%
   # Use `top_n()` to return the top n observation using `NES` as the ordering
-  # variable -- note the use of `dplyr::desc()` to sort the values in descending order
-  dplyr::top_n(n = 1, wt = dplyr::desc(NES))
    + # variable -- note the use of `-n` to display the top n values when sorted + # in descending order + dplyr::top_n(n = -3, wt = NES)
    -

    The gene set HALLMARK_E2F_TARGETS has the highest negative NES.

    +

    The gene set HALLMARK_E2F_TARGETS has the most negative NES.

    highly_negative_nes_plot <- enrichplot::gseaplot(
   gsea_results,
   geneSetID = "HALLMARK_E2F_TARGETS",
@@ -4179,7 +4180,7 @@ 
    4.6.2 Highly Negative NES
    
 
 highly_negative_nes_plot

    -

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly negative enrichment scores are being displayed.

    +

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the maximum deviation from zero, in other words, the most negative ES score. An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly negative enrichment score’s data are being displayed.

    Let’s save this plot to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
   plot = highly_negative_nes_plot
@@ -4222,7 +4223,7 @@ 
    6 Session info
    
 ##  collate  en_US.UTF-8                 
 ##  ctype    en_US.UTF-8                 
 ##  tz       Etc/UTC                     
-##  date     2020-11-06                  
+##  date     2020-11-09                  
 ## 
 ## ─ Packages ───────────────────────────────────────────────────────────────────
 ##  package         * version  date       lib source        

From dfc490729034b53894851f1e8f9dfceac85b5bef Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Mon, 9 Nov 2020 15:58:27 -0500
Subject: [PATCH 15/19] mimic "highly" -> "most" language

---
 .../pathway-analysis_microarray_03_gsea.Rmd   | 22 ++++++-------
 .../pathway-analysis_microarray_03_gsea.html  | 32 +++++++++----------
 2 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
index 4ad34d15..ed949d4a 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
@@ -408,9 +408,9 @@ gsea_result_df <- data.frame(gsea_results@result)
 We can visualize GSEA results for individual pathways or gene sets using `enrichplot::gseaplot()`.
 Let's take a look at 2 different pathways -- one with a highly positive NES and one with a highly negative NES -- to get more insight into how ES are calculated.
 
-### Highly Positive NES
+### Most Positive NES
 
-Let's look for the gene set with the highest positive NES.
+Let's look for the gene set with the most positive NES.
 
 ```{r}
 gsea_result_df %>%
@@ -418,17 +418,17 @@ gsea_result_df %>%
   dplyr::top_n(n = 3, wt = NES)
 ```
 
-The gene set `HALLMARK_TNFA_SIGNALING_VIA_NFKB` has the highest positive NES score.
+The gene set `HALLMARK_TNFA_SIGNALING_VIA_NFKB` has the most positive NES score.
 
 ```{r}
-highly_positive_nes_plot <- enrichplot::gseaplot(
+most_positive_nes_plot <- enrichplot::gseaplot(
   gsea_results,
   geneSetID = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
   title = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
   color.line = "#0d76ff"
 )
 
-highly_positive_nes_plot
+most_positive_nes_plot
 ```
 
 Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores.
@@ -442,11 +442,11 @@ Let's save to PNG.
 
 ```{r}
 ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
-  plot = highly_positive_nes_plot
+  plot = most_positive_nes_plot
 )
 ```
 
-### Highly Negative NES
+### Most Negative NES
 
 Let's look for the gene set with the most negative NES.
 
@@ -461,26 +461,26 @@ gsea_result_df %>%
 The gene set `HALLMARK_E2F_TARGETS` has the most negative NES.
 
 ```{r}
-highly_negative_nes_plot <- enrichplot::gseaplot(
+most_negative_nes_plot <- enrichplot::gseaplot(
   gsea_results,
   geneSetID = "HALLMARK_E2F_TARGETS",
   title = "HALLMARK_E2F_TARGETS",
   color.line = "#0d76ff"
 )
 
-highly_negative_nes_plot
+most_negative_nes_plot
 ```
 
 This gene set shows the opposite pattern -- genes in the pathway tend to be on the right side of the graph.
 Again, the red dashed line here indicates the maximum deviation from zero, in other words, the most negative ES score.
 An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway.
-In this case, the most highly negative enrichment score's data are being displayed.
+In this case, the most negative enrichment score's data are being displayed.
 
 Let's save this plot to PNG.
 
 ```{r}
 ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
-  plot = highly_negative_nes_plot
+  plot = most_negative_nes_plot
 )
 ```
 
diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html
index f2f88fca..99113ef3 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.html
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.html
@@ -4117,7 +4117,7 @@ 
    4.5.2 Run GSEA using the GS
 head(gsea_results@result)

    Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    @@ -4128,37 +4128,37 @@

    4.5.2 Run GSEA using the GS

    4.6 Visualizing results

    We can visualize GSEA results for individual pathways or gene sets using enrichplot::gseaplot(). Let’s take a look at 2 different pathways – one with a highly positive NES and one with a highly negative NES – to get more insight into how ES are calculated.

    -
    -

    4.6.1 Highly Positive NES

    -

    Let’s look for the gene set with the highest positive NES.

    +
    +

    4.6.1 Most Positive NES

    +

    Let’s look for the gene set with the most positive NES.

    gsea_result_df %>%
   # Use `top_n()` to return the top n observation using `NES` as the ordering variable
   dplyr::top_n(n = 3, wt = NES)
    -

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the highest positive NES score.

    -
    highly_positive_nes_plot <- enrichplot::gseaplot(
+
    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the most positive NES score.
    
+
    most_positive_nes_plot <- enrichplot::gseaplot(
   gsea_results,
   geneSetID = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
   title = "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
   color.line = "#0d76ff"
 )
 
-highly_positive_nes_plot
    
+most_positive_nes_plot

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly positive enrichment score’s data are being displayed.

    The plots returned by enrichplot::gseaplot are ggplots, so we can use ggplot2::ggsave() to save them to file.

    Let’s save to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
-  plot = highly_positive_nes_plot
+  plot = most_positive_nes_plot
 )
    ## Saving 7 x 5 in image
    -
    -

    4.6.2 Highly Negative NES

    +
    +

    4.6.2 Most Negative NES

    Let’s look for the gene set with the most negative NES.

    gsea_result_df %>%
   # Use `top_n()` to return the top n observation using `NES` as the ordering
@@ -4167,23 +4167,23 @@ 
    4.6.2 Highly Negative NES
    
   dplyr::top_n(n = -3, wt = NES)

    The gene set HALLMARK_E2F_TARGETS has the most negative NES.

    -
    highly_negative_nes_plot <- enrichplot::gseaplot(
+
    most_negative_nes_plot <- enrichplot::gseaplot(
   gsea_results,
   geneSetID = "HALLMARK_E2F_TARGETS",
   title = "HALLMARK_E2F_TARGETS",
   color.line = "#0d76ff"
 )
 
-highly_negative_nes_plot
    
+most_negative_nes_plot

    -

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the maximum deviation from zero, in other words, the most negative ES score. An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly negative enrichment score’s data are being displayed.

    +

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the maximum deviation from zero, in other words, the most negative ES score. An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most negative enrichment score’s data are being displayed.

    Let’s save this plot to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
-  plot = highly_negative_nes_plot
+  plot = most_negative_nes_plot
 )
    ## Saving 7 x 5 in image
    From 7576ca5fb5c4e2a0a05e6d404f118b228e35d507 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Wed, 11 Nov 2020 08:43:33 -0500 Subject: [PATCH 16/19] incorporate wording suggestions from code review --- .../pathway-analysis_microarray_03_gsea.Rmd | 12 ++++----- .../pathway-analysis_microarray_03_gsea.html | 25 ++++++++++--------- 2 files changed, 19 insertions(+), 18 deletions(-) diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd index ed949d4a..e9e6b170 100644 --- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd +++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd @@ -336,9 +336,8 @@ Looks like we were able to successfully get rid of the duplicate gene identifier ## Perform gene set enrichment analysis (GSEA) -The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list. -As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes. -We therefore expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list [@clusterProfiler-book]. +The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list meaning that they are highly- or lowly-ranked. +As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes so we would expect the latter distribution. [@clusterProfiler-book]. ### Determine our pre-ranked genes list @@ -346,10 +345,11 @@ The `GSEA()` function takes a pre-ranked and sorted named vector of statistics, This is step 1 -- create a gene list with statistics that GSEA will rank by. ```{r} -# Let's create a named vector ranked based on the t-statistic values -- we -# need to sort the t-statistic values in descending order here +# Let's create a named vector ranked based on the t-statistic values t_vector <- filtered_dge_mapped_df$t names(t_vector) <- filtered_dge_mapped_df$entrez_id + +# We need to sort the t-statistic values in descending order here t_vector <- sort(t_vector, decreasing = TRUE) ``` @@ -473,7 +473,7 @@ most_negative_nes_plot This gene set shows the opposite pattern -- genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the maximum deviation from zero, in other words, the most negative ES score. -An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. +Here the ES is calculated by starting with the most lowly ranked genes (according to the gene-level t-statistic values) and making the score more negative when a gene is in the pathway. In this case, the most negative enrichment score's data are being displayed. Let's save this plot to PNG. diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html index 99113ef3..33a0a33f 100644 --- a/02-microarray/pathway-analysis_microarray_03_gsea.html +++ b/02-microarray/pathway-analysis_microarray_03_gsea.html @@ -3940,7 +3940,7 @@

    4.2 Import data

    # desired gene list results dge_df <- readr::read_tsv(dge_url)
    ## 
-## ── Column specification ────────────────────────────────────────────────────────────────────────────
+## ── Column specification ──────────────────────────────────────────────────────────────────────
 ## cols(
 ##   Gene = col_character(),
 ##   logFC = col_double(),
@@ -4075,15 +4075,16 @@ 
    4.4 Gene identifier conversion
 
    
 
    4.5 Perform gene set enrichment analysis (GSEA)
    
-
    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list. As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes. We therefore expect that sets will tend to be highly- or lowly-ranked or, put another way, found at the top or bottom of the pre-ranked gene list (Yu).
    
+
    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list meaning that they are highly- or lowly-ranked. As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes so we would expect the latter distribution. (Yu).
    
 
    
 
    4.5.1 Determine our pre-ranked genes list
    
 
    The GSEA() function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers. This is step 1 – create a gene list with statistics that GSEA will rank by.
    
-
    # Let's create a named vector ranked based on the t-statistic values -- we
-# need to sort the t-statistic values in descending order here
-t_vector <- filtered_dge_mapped_df$t
-names(t_vector) <- filtered_dge_mapped_df$entrez_id
-t_vector <- sort(t_vector, decreasing = TRUE)
    
+
    # Let's create a named vector ranked based on the t-statistic values
+t_vector <- filtered_dge_mapped_df$t
+names(t_vector) <- filtered_dge_mapped_df$entrez_id
+
+# We need to sort the t-statistic values in descending order here
+t_vector <- sort(t_vector, decreasing = TRUE)
    
 
    Let’s preview our pre-ranked named vector.
    
 
    # Look at first entries of the ranked t-statistic vector
 head(t_vector)
    
@@ -4117,7 +4118,7 @@ 
    4.5.2 Run GSEA using the GS
 head(gsea_results@result)

    Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    @@ -4136,7 +4137,7 @@

    4.6.1 Most Positive NES

    dplyr::top_n(n = 3, wt = NES)

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the most positive NES score.

    @@ -4167,7 +4168,7 @@

    4.6.2 Most Negative NES

    dplyr::top_n(n = -3, wt = NES)

    The gene set HALLMARK_E2F_TARGETS has the most negative NES.

    @@ -4180,7 +4181,7 @@

    4.6.2 Most Negative NES

    most_negative_nes_plot

    -

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the maximum deviation from zero, in other words, the most negative ES score. An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most negative enrichment score’s data are being displayed.

    +

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the maximum deviation from zero, in other words, the most negative ES score. Here the ES is calculated by starting with the most lowly ranked genes (according to the gene-level t-statistic values) and making the score more negative when a gene is in the pathway. In this case, the most negative enrichment score’s data are being displayed.

    Let’s save this plot to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
   plot = most_negative_nes_plot
@@ -4223,7 +4224,7 @@ 
    6 Session info
    
 ##  collate  en_US.UTF-8                 
 ##  ctype    en_US.UTF-8                 
 ##  tz       Etc/UTC                     
-##  date     2020-11-09                  
+##  date     2020-11-11                  
 ## 
 ## ─ Packages ───────────────────────────────────────────────────────────────────
 ##  package         * version  date       lib source        

From ae5d2104afe58d3287c711b082c9ffcc16d5ad9a Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Thu, 12 Nov 2020 19:49:22 -0500
Subject: [PATCH 17/19] some re-structuring/re-wording based on review
 suggestions

---
 .../pathway-analysis_microarray_03_gsea.Rmd   |  16 +-
 .../pathway-analysis_microarray_03_gsea.html  | 554 +++++++++---------
 2 files changed, 288 insertions(+), 282 deletions(-)

diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
index e9e6b170..8d845b8b 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd
@@ -13,7 +13,9 @@ output:
 
 This example is one of pathway analysis module set, we recommend looking at the [pathway analysis introduction](https://alexslemonade.github.io/refinebio-examples/02-microarray/pathway-analysis_microarray_00_intro.html) to help you determine which pathway analysis method is best suited for your purposes.
 
-This particular example analysis shows how you can use gene set enrichment analysis (GSEA) to detect situations where all genes in a predefined set change in a coordinated way, detecting even small statistical but coordinated changes between two biological states.
+This particular example analysis shows how you can use gene set enrichment analysis (GSEA) to detect situations where all genes in a predefined gene set change in a coordinated way, detecting even small statistical but coordinated changes between two biological states.
+Genes are ranked from most highly positive to most highly negative, weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic, for each gene set.
+Normalized enrichment scores (NES) are enrichment scores that are scaled to account for gene sets of different sizes [@Subramanian2005; @Korotkevich2019].
 
 ⬇️ [**Jump to the analysis code**](#analysis) ⬇️
 
@@ -336,8 +338,8 @@ Looks like we were able to successfully get rid of the duplicate gene identifier
 
 ## Perform gene set enrichment analysis (GSEA)
 
-The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list meaning that they are highly- or lowly-ranked.
-As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes so we would expect the latter distribution. [@clusterProfiler-book].
+The goal of GSEA is to detect situations where all genes in a gene set change in a coordinated way, detecting even small statistical but coordinated changes.
+This is done by ranking genes within a gene set from most highly positive to most highly negative, weighting them according to their gene-level statistic, to calculate the enrichment score (ES), which is a pathway-level statistic [@clusterProfiler-book].
 
 ### Determine our pre-ranked genes list
 
@@ -362,9 +364,8 @@ head(t_vector)
 
 ### Run GSEA using the `GSEA()` function
 
-Genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic.
-The enrichment score (ES) is a pathway-level statistic, calculated using our gene-level statistics.
-Normalized enrichment scores (NES) are enrichment scores that are scaled to make gene sets that contain different number of genes comparable [@Subramanian2005; @Korotkevich2019].
+Genes were ranked from most highly positive to most highly negative, weighted according to their gene-level statistic, in the previous section.
+In this section, we will implement GSEA to calculate the enrichment score (ES) for each gene set using our pre-ranked gene list.
 
 We can use the `GSEA()` function to perform GSEA with any generic set of gene sets, but there are several functions for using specific, commonly used gene sets (e.g., `gseKEGG()`).
 
@@ -392,7 +393,8 @@ Let's take a look at the results.
 head(gsea_results@result)
 ```
 
-Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing.
+Significance is assessed by permutating the gene labels of the pre-ranked gene list and recomputing the ES of the gene set for the permutated data, which generates a null distribution for the ES.
+The ES for each gene set is then normalized to account for the size of the set, resulting in a normalized enrichment score (NES), and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing.
 Looks like we have gene sets returned as significant at FDR of `0.05`.
 If we didn't have any, our visualizations below would show up blank as nothing would have met our `pvalueCutoff` above.
 
diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html
index 33a0a33f..9bc53453 100644
--- a/02-microarray/pathway-analysis_microarray_03_gsea.html
+++ b/02-microarray/pathway-analysis_microarray_03_gsea.html
@@ -2850,282 +2850,286 @@
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@@ -3768,7 +3772,7 @@ 
    October 2020
    
 
    
 
    1 Purpose of this analysis
    
 
    This example is one of pathway analysis module set, we recommend looking at the pathway analysis introduction to help you determine which pathway analysis method is best suited for your purposes.
    
-
    This particular example analysis shows how you can use gene set enrichment analysis (GSEA) to detect situations where all genes in a predefined set change in a coordinated way, detecting even small statistical but coordinated changes between two biological states.
    
+
    This particular example analysis shows how you can use gene set enrichment analysis (GSEA) to detect situations where all genes in a predefined gene set change in a coordinated way, detecting even small statistical but coordinated changes between two biological states. Genes are ranked from most highly positive to most highly negative, weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic, for each gene set. Normalized enrichment scores (NES) are enrichment scores that are scaled to account for gene sets of different sizes (Korotkevich et al. 2019; Subramanian et al. 2005).
    
 
    ⬇️ Jump to the analysis code ⬇️
    
 
    
 
    
@@ -3940,7 +3944,7 @@ 
    4.2 Import data
    
 # desired gene list results
 dge_df <- readr::read_tsv(dge_url)
    ## 
-## ── Column specification ──────────────────────────────────────────────────────────────────────
+## ── Column specification ───────────────────────────────────────────────────────────────────
 ## cols(
 ##   Gene = col_character(),
 ##   logFC = col_double(),
@@ -4075,7 +4079,7 @@ 
    4.4 Gene identifier conversion
 
    
 
    4.5 Perform gene set enrichment analysis (GSEA)
    
-
    The goal of GSEA is to determine whether the members of a set of genes are randomly distributed throughout a pre-ranked gene list or primarily found at the top or bottom of the list meaning that they are highly- or lowly-ranked. As mentioned earlier, hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression of genes so we would expect the latter distribution. (Yu).
    
+
    The goal of GSEA is to detect situations where all genes in a gene set change in a coordinated way, detecting even small statistical but coordinated changes. This is done by ranking genes within a gene set from most highly positive to most highly negative, weighting them according to their gene-level statistic, to calculate the enrichment score (ES), which is a pathway-level statistic (Yu).
    
 
    
 
    4.5.1 Determine our pre-ranked genes list
    
 
    The GSEA() function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers. This is step 1 – create a gene list with statistics that GSEA will rank by.
    
@@ -4093,7 +4097,7 @@ 
    4.5.1 Determine our pre-ranked ge
 
    
 
    
 
    4.5.2 Run GSEA using the GSEA() function
    
-
    Genes are ranked from most highly positive to most highly negative and weighted according to their gene-level statistic. The enrichment score (ES) is a pathway-level statistic, calculated using our gene-level statistics. Normalized enrichment scores (NES) are enrichment scores that are scaled to make gene sets that contain different number of genes comparable (Korotkevich et al. 2019; Subramanian et al. 2005).
    
+
    Genes were ranked from most highly positive to most highly negative, weighted according to their gene-level statistic, in the previous section. In this section, we will implement GSEA to calculate the enrichment score (ES) for each gene set using our pre-ranked gene list.
    
 
    We can use the GSEA() function to perform GSEA with any generic set of gene sets, but there are several functions for using specific, commonly used gene sets (e.g., gseKEGG()).
    
 
    gsea_results <- GSEA(
   geneList = t_vector, # ordered ranked gene list
@@ -4118,10 +4122,10 @@ 
    4.5.2 Run GSEA using the GS
 head(gsea_results@result)
    
 
    
 
 
    
-
    Significance is assessed by generating a null distribution by sampling random gene sets of the same size and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.
    
+
    Significance is assessed by permutating the gene labels of the pre-ranked gene list and recomputing the ES of the gene set for the permutated data, which generates a null distribution for the ES. The ES for each gene set is then normalized to account for the size of the set, resulting in a normalized enrichment score (NES), and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.
    
 
    The information we’re most likely interested in is in the results slot. Let’s convert this into a data frame that we can use for further analysis and write to file.
    
 
    gsea_result_df <- data.frame(gsea_results@result)
    
 
    
@@ -4137,7 +4141,7 @@ 
    4.6.1 Most Positive NES
    
   dplyr::top_n(n = 3, wt = NES)

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the most positive NES score.

    @@ -4168,7 +4172,7 @@

    4.6.2 Most Negative NES

    dplyr::top_n(n = -3, wt = NES)

    The gene set HALLMARK_E2F_TARGETS has the most negative NES.

    @@ -4224,7 +4228,7 @@

    6 Session info

    ## collate en_US.UTF-8 ## ctype en_US.UTF-8 ## tz Etc/UTC -## date 2020-11-11 +## date 2020-11-13 ## ## ─ Packages ─────────────────────────────────────────────────────────────────── ## package * version date lib source From a810360722814b3415fe9cda5bf7041df446ed48 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Thu, 12 Nov 2020 20:05:47 -0500 Subject: [PATCH 18/19] update `dictionary.txt` file --- components/dictionary.txt | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/components/dictionary.txt b/components/dictionary.txt index 14385da0..ca8a3485 100644 --- a/components/dictionary.txt +++ b/components/dictionary.txt @@ -16,6 +16,7 @@ Brainarray’s Brems CCDL CCDL's +cDNA cheatsheet cheatsheets ChIP @@ -85,6 +86,7 @@ molecularly musculus MSigDB myeloid +NES Northcott nb ortholog @@ -92,6 +94,8 @@ orthologs orthology overexpressing overexpression +permutated +permutating pheatmap PLOS PLX From 82a97d882c72cff2d3ed67ee90e132aa69a48cd6 Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Fri, 13 Nov 2020 21:38:23 -0500 Subject: [PATCH 19/19] incorporate @jaclyn-taroni's review suggestions --- .../pathway-analysis_microarray_03_gsea.Rmd | 10 +++++++--- .../pathway-analysis_microarray_03_gsea.html | 19 ++++++++++--------- 2 files changed, 17 insertions(+), 12 deletions(-) diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd index 8d845b8b..0c414a56 100644 --- a/02-microarray/pathway-analysis_microarray_03_gsea.Rmd +++ b/02-microarray/pathway-analysis_microarray_03_gsea.Rmd @@ -15,6 +15,7 @@ This example is one of pathway analysis module set, we recommend looking at the This particular example analysis shows how you can use gene set enrichment analysis (GSEA) to detect situations where all genes in a predefined gene set change in a coordinated way, detecting even small statistical but coordinated changes between two biological states. Genes are ranked from most highly positive to most highly negative, weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic, for each gene set. +More specifically, an ES is calculated by starting with the most highly ranked genes (based on the gene-level statistic selected for ranking) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. Normalized enrichment scores (NES) are enrichment scores that are scaled to account for gene sets of different sizes [@Subramanian2005; @Korotkevich2019]. ⬇️ [**Jump to the analysis code**](#analysis) ⬇️ @@ -344,7 +345,9 @@ This is done by ranking genes within a gene set from most highly positive to mos ### Determine our pre-ranked genes list The `GSEA()` function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers. -This is step 1 -- create a gene list with statistics that GSEA will rank by. +We will therefore need to create a gene list with statistics that GSEA will rank by. + +In the next chunk, we will create a named vector ranked based on the gene-level t-statistic values. ```{r} # Let's create a named vector ranked based on the t-statistic values @@ -435,7 +438,7 @@ most_positive_nes_plot Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero). -An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. +As mentioned earlier, an ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly positive enrichment score's data are being displayed. The plots returned by `enrichplot::gseaplot` are ggplots, so we can use `ggplot2::ggsave()` to save them to file. @@ -475,7 +478,8 @@ most_negative_nes_plot This gene set shows the opposite pattern -- genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the maximum deviation from zero, in other words, the most negative ES score. -Here the ES is calculated by starting with the most lowly ranked genes (according to the gene-level t-statistic values) and making the score more negative when a gene is in the pathway. +As we know, the ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. +A negative enrichment score will be returned when not many of the genes are found at the top of the list, as this would mean decreasing the score a great deal thus producing a negative value. In this case, the most negative enrichment score's data are being displayed. Let's save this plot to PNG. diff --git a/02-microarray/pathway-analysis_microarray_03_gsea.html b/02-microarray/pathway-analysis_microarray_03_gsea.html index 9bc53453..d3fa10fb 100644 --- a/02-microarray/pathway-analysis_microarray_03_gsea.html +++ b/02-microarray/pathway-analysis_microarray_03_gsea.html @@ -3772,7 +3772,7 @@

    October 2020

    1 Purpose of this analysis

    This example is one of pathway analysis module set, we recommend looking at the pathway analysis introduction to help you determine which pathway analysis method is best suited for your purposes.

    -

    This particular example analysis shows how you can use gene set enrichment analysis (GSEA) to detect situations where all genes in a predefined gene set change in a coordinated way, detecting even small statistical but coordinated changes between two biological states. Genes are ranked from most highly positive to most highly negative, weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic, for each gene set. Normalized enrichment scores (NES) are enrichment scores that are scaled to account for gene sets of different sizes (Korotkevich et al. 2019; Subramanian et al. 2005).

    +

    This particular example analysis shows how you can use gene set enrichment analysis (GSEA) to detect situations where all genes in a predefined gene set change in a coordinated way, detecting even small statistical but coordinated changes between two biological states. Genes are ranked from most highly positive to most highly negative, weighted according to their gene-level statistic, which is essential to the calculation of the enrichment score (ES), a pathway-level statistic, for each gene set. More specifically, an ES is calculated by starting with the most highly ranked genes (based on the gene-level statistic selected for ranking) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. Normalized enrichment scores (NES) are enrichment scores that are scaled to account for gene sets of different sizes (Korotkevich et al. 2019; Subramanian et al. 2005).

    ⬇️ Jump to the analysis code ⬇️

    @@ -3944,7 +3944,7 @@

    4.2 Import data

    # desired gene list results dge_df <- readr::read_tsv(dge_url)
    ## 
-## ── Column specification ───────────────────────────────────────────────────────────────────
+## ── Column specification ──────────────────────────────────────────────────────────────
 ## cols(
 ##   Gene = col_character(),
 ##   logFC = col_double(),
@@ -4082,7 +4082,8 @@ 
    4.5 Perform gene set enrichment a
 
    The goal of GSEA is to detect situations where all genes in a gene set change in a coordinated way, detecting even small statistical but coordinated changes. This is done by ranking genes within a gene set from most highly positive to most highly negative, weighting them according to their gene-level statistic, to calculate the enrichment score (ES), which is a pathway-level statistic (Yu).
    
 
    
 
    4.5.1 Determine our pre-ranked genes list
    
-
    The GSEA() function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers. This is step 1 – create a gene list with statistics that GSEA will rank by.
    
+
    The GSEA() function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers. We will therefore need to create a gene list with statistics that GSEA will rank by.
    
+
    In the next chunk, we will create a named vector ranked based on the gene-level t-statistic values.
    
 
    # Let's create a named vector ranked based on the t-statistic values
 t_vector <- filtered_dge_mapped_df$t
 names(t_vector) <- filtered_dge_mapped_df$entrez_id
@@ -4122,7 +4123,7 @@ 
    4.5.2 Run GSEA using the GS
 head(gsea_results@result)
    
 
    
 
 
    
 
    Significance is assessed by permutating the gene labels of the pre-ranked gene list and recomputing the ES of the gene set for the permutated data, which generates a null distribution for the ES. The ES for each gene set is then normalized to account for the size of the set, resulting in a normalized enrichment score (NES), and an FDR (false discovery rate) value is calculated to account for multiple hypothesis testing. Looks like we have gene sets returned as significant at FDR of 0.05. If we didn’t have any, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.
    
@@ -4141,7 +4142,7 @@ 
    4.6.1 Most Positive NES
    
   dplyr::top_n(n = 3, wt = NES)

    The gene set HALLMARK_TNFA_SIGNALING_VIA_NFKB has the most positive NES score.

    @@ -4154,7 +4155,7 @@

    4.6.1 Most Positive NES

    most_positive_nes_plot

    -

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero). An ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly positive enrichment score’s data are being displayed.

    +

    Notice how the genes that are in the gene set, indicated by the black bars, tend to be on the left side of the graph indicating that they have positive gene-level scores. The red dashed line indicates the peak ES score (the ES is the maximum deviation from zero). As mentioned earlier, an ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. In this case, the most highly positive enrichment score’s data are being displayed.

    The plots returned by enrichplot::gseaplot are ggplots, so we can use ggplot2::ggsave() to save them to file.

    Let’s save to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_positive_plot.png"),
@@ -4172,7 +4173,7 @@ 
    4.6.2 Most Negative NES
    
   dplyr::top_n(n = -3, wt = NES)

    The gene set HALLMARK_E2F_TARGETS has the most negative NES.

    @@ -4185,7 +4186,7 @@

    4.6.2 Most Negative NES

    most_negative_nes_plot

    -

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the maximum deviation from zero, in other words, the most negative ES score. Here the ES is calculated by starting with the most lowly ranked genes (according to the gene-level t-statistic values) and making the score more negative when a gene is in the pathway. In this case, the most negative enrichment score’s data are being displayed.

    +

    This gene set shows the opposite pattern – genes in the pathway tend to be on the right side of the graph. Again, the red dashed line here indicates the maximum deviation from zero, in other words, the most negative ES score. As we know, the ES is calculated by starting with the most highly ranked genes (according to the gene-level t-statistic values) and increasing the score when a gene is in the pathway and decreasing the score when a gene is not in the pathway. A negative enrichment score will be returned when not many of the genes are found at the top of the list, as this would mean decreasing the score a great deal thus producing a negative value. In this case, the most negative enrichment score’s data are being displayed.

    Let’s save this plot to PNG.

    ggplot2::ggsave(file.path(plots_dir, "GSE71270_gsea_enrich_negative_plot.png"),
   plot = most_negative_nes_plot
@@ -4228,7 +4229,7 @@ 
    6 Session info
    
 ##  collate  en_US.UTF-8                 
 ##  ctype    en_US.UTF-8                 
 ##  tz       Etc/UTC                     
-##  date     2020-11-13                  
+##  date     2020-11-14                  
 ## 
 ## ─ Packages ───────────────────────────────────────────────────────────────────
 ##  package         * version  date       lib source