From 8b81fdd96eeecf1d0e479d7908376b8e57dc356d Mon Sep 17 00:00:00 2001 From: Candace Savonen Date: Thu, 22 Oct 2020 11:04:43 -0400 Subject: [PATCH 1/7] 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 2/7] 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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    1 Purpose of this analysis

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

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    IDENTICAL TO MICROARRAY EXAMPLE

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    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).

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    ⬇️ Jump to the analysis code ⬇️

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    IDENTICAL TO OTHER PATHWAY ANALYSIS EXAMPLES

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    1.0.1 What is pathway analysis?

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    Pathway analysis refers to any one of many techniques that uses predetermined sets of genes that are related or coordinated in their expression in some way (e.g., participate in the same molecular process, are regulated by the same transcription factor) to interpret a high-throughput experiment. In the context of refine.bio, we use these techniques to analyze and interpret genome-wide gene expression experiments. The rationale for performing pathway analysis is that looking at the pathway-level may be more biologically meaningful than considering individual genes, especially if a large number of genes are differentially expressed between conditions of interest. In addition, many relatively small changes in the expression values of genes in the same pathway could lead to a phenotypic outcome and these small changes may go undetected in differential gene expression analysis.

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    We highly recommend taking a look at Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges from Khatri et al. (2012) for a more comprehensive overview. We have provided primary publications and documentation of the methods we will introduce below as well as some recommended reading in the Resources for further learning section.

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    1.0.2 How to choose a pathway analysis?

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    This table summarizes the pathway analyses examples in this module.

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    AnalysisWhat is required for inputWhat output looks like✅ Pros⚠️ Cons
    ORA (Over-representation Analysis)A list of gene IDs (no stats needed)A per-pathway hypergeometric test result- Simple

    - Inexpensive computationally to calculate p-values    		- Requires arbitrary thresholds and ignores any statistics associated with a gene

    - Assumes independence of genes and pathways
    GSEA (Gene Set Enrichment Analysis)A list of genes IDs with gene-level summary statisticsA per-pathway enrichment score- Includes all genes (no arbitrary threshold!)

    - Attempts to measure coordination of genes    		- Permutations can be expensive

    - Does not account for pathway overlap

    - Two-group comparisons not always appropriate/feasible
    GSVA (Gene Set Variation Analysis)A gene expression matrix (like what you get from refine.bio directly)Pathway-level scores on a per-sample basis- Does not require two groups to compare upfront

    - Normally distributed scores    		- Scores are not a good fit for gene sets that contain genes that go up AND down

    - Method doesn’t assign statistical significance itself

    - Recommended sample size n > 10
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    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

    +

    DRAFT

    +

    In this example, we are using differential expression results table we obtained from an example analysis of an acute myeloid leukemia (AML) dataset using the DESeq2 package (Love et al. 2014). The table contains summary statistics including Ensembl gene IDs, log fold change 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 acute myeloid leukemia (AML) dataset (Micol et al. 2017).

    +

    Micol et al. (2017) performed RNA-seq on primary peripheral blood and bone marrow samples from AML patients with and without ASXL1/2 mutations.

    +
    +
    +

    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 - RNA-seq

    +
    +

    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.Hs.eg.db package to perform gene identifier conversion (Carlson 2020).

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

    Attach the packages we need for this analysis.

    +
    # Attach the library
+library(clusterProfiler)
    +
    ##
    +
    ## clusterProfiler v3.18.0  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)
+
+# Human annotation package we'll use for gene identifier conversion
+library(org.Hs.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.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

    +

    DRAFT

    +

    We will read in the differential expression results we will download from online. These results are from a acute myeloid leukemia (AML) RNA-seq dataset we used for differential expression analysis using DESeq2 (Love et al. 2014). The table contains summary statistics including Ensembl gene IDs, log fold change 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.

    +

    REVIEW

    +

    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/03-rnaseq/results/SRP078441/SRP078441_differential_expression_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", "SRP078441_differential_expression_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(),
+##   baseMean = col_double(),
+##   log2FoldChange = col_double(),
+##   lfcSE = col_double(),
+##   pvalue = col_double(),
+##   padj = col_double(),
+##   threshold = col_logical()
+## )
    +

    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.

    +

    REVIEW

    +

    The data we’re interested in here comes from human samples, so we can obtain just the gene sets relevant to Homo sapiens by specifying species = "Homo sapiens"

    +
    hs_gene_sets <- msigdbr(
+  species = "Homo sapiens" # Replace with species name relevant to your data
+)
    +

    If you run the chunk above with specifying category = "H" to the msigdbr() function, it will return only the Hallmark gene sets for Homo sapiens.

    +

    Let’s preview what’s in hs_gene_sets.

    +
    head(hs_gene_sets)
    +
    + +
    +

    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.

    +

    IDENTICAL TO MICROARRAY EXAMPLE (except “ENTREZID” –> “SYMBOL” and “Dr” –> “Hs”) ## Gene identifier conversion

    +

    We’re going to convert our identifiers in dge_df to gene symbols, 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.Hs.eg.db contains information for different identifiers (Carlson 2020). org.Hs.eg.db is specific to Homo sapiens – this is what the Hs 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.Hs.eg.db)
    +
    ##  [1] "ACCNUM"       "ALIAS"        "ENSEMBL"      "ENSEMBLPROT"  "ENSEMBLTRANS"
+##  [6] "ENTREZID"     "ENZYME"       "EVIDENCE"     "EVIDENCEALL"  "GENENAME"    
+## [11] "GO"           "GOALL"        "IPI"          "MAP"          "OMIM"        
+## [16] "ONTOLOGY"     "ONTOLOGYALL"  "PATH"         "PFAM"         "PMID"        
+## [21] "PROSITE"      "REFSEQ"       "SYMBOL"       "UCSCKG"       "UNIGENE"     
+## [26] "UNIPROT"
    +

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

    +

    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 gene symbols is called mapIds().

    +

    Let’s create a data frame that shows the mapped gene symbols 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(
+  "gene_symbol" = mapIds(
+    org.Hs.eg.db, # Replace with annotation package for the organism relevant to your data
+    keys = dge_df$Gene,
+    column = "SYMBOL", # 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 gene symbol, drop that
+  # from the data frame
+  dplyr::filter(!is.na(gene_symbol)) %>%
+  # 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 gene symbols. In this case, it’s also possible that a gene symbol 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)
    +
    + +
    +

    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 gene symbols that mapped to multiple Ensembl IDs.

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

    Looks like we do have duplicated gene symbols. Let’s find out which gene symbols have been duplicated.

    +
    dup_gene_symbols <- dge_mapped_df %>%
+  dplyr::filter(duplicated(gene_symbol)) %>%
+  dplyr::pull(gene_symbol)
+
+dup_gene_symbols
    +
    ##  [1] "IFNAR2"       "NDST2"        "CLEC4A"       "PTP4A1"       "BAZ2B"       
+##  [6] "ZNF280D"      "ARHGAP11B"    "DNAJC9-AS1"   "ZNF365"       "CYB561D2"    
+## [11] "ZCWPW2"       "PLA2G2C"      "SLFN12L"      "ARHGEF18"     "C2-AS1"      
+## [16] "GGT1"         "LINC02203"    "SPDYE1"       "LOC440896"    "RGS5"        
+## [21] "LRTOMT"       "RLN1"         "ITFG2-AS1"    "TBC1D3"       "HSPA14"      
+## [26] "MPV17L"       "DET1"         "KBTBD11-OT1"  "FBXO10"       "SCHIP1"      
+## [31] "ZNF365"       "GOLGA8M"      "CHN2"         "SLC35D2"      "SPATA13"     
+## [36] "RN7SK"        "POLA2"        "CLN3"         "BOLA2"        "ZNF724"      
+## [41] "HLA-DQA1"     "FAM50B"       "LRRC57"       "TMEM217"      "ST6GALNAC6"  
+## [46] "LOC101927815" "C4orf36"      "GOLGA6L4"     "TAB2"         "RBM27"       
+## [51] "KYAT1"        "OR2C3"        "COMMD7"       "SEC16B"       "PRH1"        
+## [56] "PTGR2"        "PRORP"        "ELFN2"        "ARL17A"       "TUT1"        
+## [61] "TBCE"         "ZNF844"       "TMSB15B"      "RAET1E"       "SMN1"        
+## [66] "LINC01238"    "RNF43"        "POLR2J3"      "POLR2J2"      "SERF1A"      
+## [71] "PARL"         "ZNF761"       "SPICE1"       "SPATA13"      "MATR3"
    +

    Now let’s take a look at the rows associated with the duplicated gene symbols.

    +
    dge_mapped_df %>%
+  dplyr::filter(gene_symbol %in% dup_gene_symbols) %>%
+  dplyr::arrange(gene_symbol)
    +
    + +
    +

    IDENTICAL TO MICROARRAY EXAMPLE (except using absolute log2 fold change value instead of 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 gene symbols associated with the higher absolute value of the log fold change. GSEA relies on genes’ rankings on the basis of this statistic. Retaining the instance of the gene symbol 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.

    +
    filtered_dge_mapped_df <- dge_mapped_df %>%
+  # Sort so that the highest absolute values of the log fold change are at the top
+  dplyr::arrange(dplyr::desc(abs(log2FoldChange))) %>%
+  # 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 absolute value of the log fold change
+  dplyr::distinct(gene_symbol, .keep_all = TRUE)
    +

    Let’s check to see that we removed the duplicate gene symbols and kept the rows with the higher absolute value of the log fold change.

    +
    filtered_dge_mapped_df %>%
+  dplyr::filter(gene_symbol %in% dup_gene_symbols) %>%
+  dplyr::arrange(gene_symbol)
    +
    + +
    +

    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 log fold change!

    +
    +
    +

    4.4 Perform gene set enrichment analysis (GSEA)

    +

    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).

    +
    +

    4.4.1 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. 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 log fold change values.

    +
    # Let's create a named vector ranked based on the log fold change values
+lfc_vector <- filtered_dge_mapped_df$log2FoldChange
+names(lfc_vector) <- filtered_dge_mapped_df$gene_symbol
+
+# We need to sort the log fold change values in descending order here
+lfc_vector <- sort(lfc_vector, decreasing = TRUE)
    +

    Let’s preview our pre-ranked named vector.

    +
    # Look at first entries of the ranked log fold change vector
+head(lfc_vector)
    +
    ##     UTS2     ALPL   GRIN2B      ADM    KRT23    IL1R2 
+## 6.174579 5.824919 4.172007 3.952953 3.731020 3.717043
    +
    +
    +

    4.4.2 Run GSEA using the GSEA() function

    +

    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 = lfc_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(
+    hs_gene_sets,
+    gs_name,
+    gene_symbol
+  )
+)
    +
    ## preparing geneSet collections...
    +
    ## GSEA analysis...
    +
    ## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
+## The order of those tied genes will be arbitrary, which may produce unexpected results.
    +
    ## no term enriched under specific pvalueCutoff...
    +

    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 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)
    +

    IDENTICAL TO MICROARRAY EXAMPLE

    +
    +
    +
    +

    4.5 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.

    +
    +

    4.5.1 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 HP_VERTEBRAL_CLEFTING has the most positive NES score.

    +
    most_positive_nes_plot <- enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "HP_VERTEBRAL_CLEFTING",
+  title = "HP_VERTEBRAL_CLEFTING",
+  color.line = "#0d76ff"
+)
+
+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). As mentioned earlier, an ES is calculated by starting with the most highly ranked genes (according to the gene-level log fold change 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, "SRP078441_gsea_enrich_positive_plot.png"),
+  plot = most_positive_nes_plot
+)
    +
    ## Saving 7 x 5 in image
    +
    +
    +

    4.5.2 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
+  # 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 HOLLERN_EMT_BREAST_TUMOR_DN has the most negative NES.

    +
    most_negative_nes_plot <- enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "HOLLERN_EMT_BREAST_TUMOR_DN",
+  title = "HOLLERN_EMT_BREAST_TUMOR_DN",
+  color.line = "#0d76ff"
+)
+
+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. As we know, the ES is calculated by starting with the most highly ranked genes (according to the gene-level log fold change 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, "SRP078441_gsea_enrich_negative_plot.png"),
+  plot = most_negative_nes_plot
+)
    +
    ## Saving 7 x 5 in image
    +
    +
    +
    +

    4.6 Write results to file

    +
    readr::write_tsv(
+  gsea_result_df,
+  file.path(
+    results_dir,
+    "SRP078441_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     2020-12-02                  
+## 
+## ─ Packages ───────────────────────────────────────────────────────────────────
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    +
    +
    +

    References

    +
    +
    +

    Carlson M., 2020 org.Hs.eg.db: Genome wide annotation for human. http://bioconductor.org/packages/release/data/annotation/html/org.Hs.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

    +
    +
    +

    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

    +
    +
    +

    Micol J. B., A. Pastore, D. Inoue, N. Duployez, and E. Kim et al., 2017 ASXL2 is essential for haematopoiesis and acts as a haploinsufficient tumour suppressor in leukemia. Nature Communications 8: 15429. https://doi.org/10.1038/ncomms15429

    +
    +
    +

    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

    +
    +
    +

    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/Snakefile b/Snakefile index e9c26d36..47184ca4 100644 --- a/Snakefile +++ b/Snakefile @@ -18,6 +18,7 @@ rule target: "03-rnaseq/dimension-reduction_rnaseq_01_pca.html", "03-rnaseq/dimension-reduction_rnaseq_02_umap.html", "03-rnaseq/gene-id-annotation_rnaseq_01_ensembl.html", + "03-rnaseq/pathway-analysis_rnaseq_02_gsea.html", "03-rnaseq/ortholog-mapping_rnaseq_01_ensembl.html", "04-advanced-topics/00-intro-to-advanced-topics.html", "04-advanced-topics/network-analysis_rnaseq_01_wgcna.html" From f9b46d2b8b9f240981e22c8df44bdecafbddb5ba Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Wed, 2 Dec 2020 09:29:32 -0500 Subject: [PATCH 4/7] add to `dictionary.txt` --- components/dictionary.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/components/dictionary.txt b/components/dictionary.txt index 8fe5cca5..61ccfcec 100644 --- a/components/dictionary.txt +++ b/components/dictionary.txt @@ -56,6 +56,7 @@ ENSDARG Ensembl ENSG Entrez +ENTREZID et FACS frac @@ -77,6 +78,7 @@ hexamer HGNC histological Hochberg +Hs hypomethylating IDH Illumina @@ -126,6 +128,7 @@ ribosomes RMA RPKMs RStudio +sina ssGSEA StatQuest Subramanian From 20929fa662eb1c05a721b2a4a6eaf0d14050a4dc Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Fri, 4 Dec 2020 09:30:30 -0500 Subject: [PATCH 5/7] use KEGG gene sets and rank log fold change values --- 03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd | 143 +++--- .../pathway-analysis_rnaseq_02_gsea.html | 432 +++++++----------- 2 files changed, 239 insertions(+), 336 deletions(-) diff --git a/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd index b4adbe0e..ac423381 100644 --- a/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd +++ b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd @@ -13,8 +13,6 @@ output: This example is one of pathway analysis module set, we recommend looking at the [pathway analysis table below](#how-to-choose-a-pathway-analysis) to help you determine which pathway analysis method is best suited for your purposes. -**IDENTICAL TO MICROARRAY EXAMPLE** - 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. @@ -22,8 +20,6 @@ Normalized enrichment scores (NES) are enrichment scores that are scaled to acco ⬇️ [**Jump to the analysis code**](#analysis) ⬇️ -**IDENTICAL TO OTHER PATHWAY ANALYSIS EXAMPLES** - ### What is pathway analysis? Pathway analysis refers to any one of many techniques that uses predetermined sets of genes that are related or coordinated in their expression in some way (e.g., participate in the same molecular process, are regulated by the same transcription factor) to interpret a high-throughput experiment. @@ -92,10 +88,8 @@ In the same place you put this `.Rmd` file, you should now have three new empty ## Obtain the gene set for this example -**DRAFT** - -In this example, we are using differential expression results table we obtained from an [example analysis of an acute myeloid leukemia (AML) dataset](https://alexslemonade.github.io/refinebio-examples/03-rnaseq/differential-expression_rnaseq_01.html) using the `DESeq2` package [@Love2014]. -The table contains summary statistics including Ensembl gene IDs, log fold change values, and adjusted p-values (FDR in this case). +In this example, we are using the differential expression results table we obtained from an [example analysis of an acute myeloid leukemia (AML) dataset](https://alexslemonade.github.io/refinebio-examples/03-rnaseq/differential-expression_rnaseq_01.html) using the `DESeq2` package [@Love2014]. +The table contains summary statistics including Ensembl gene IDs, log2 fold change 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/03-rnaseq/differential-expression_rnaseq_01.html). @@ -113,7 +107,7 @@ 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). @@ -157,7 +151,7 @@ if (!("org.Hs.eg.db" %in% installed.packages())) { Attach the packages we need for this analysis. -```{r} +```{r message=FALSE} # Attach the library library(clusterProfiler) @@ -171,17 +165,20 @@ library(org.Hs.eg.db) library(magrittr) ``` -## Import data +The GSEA algorithm utilizes random sampling so we are going to set the seed to make our results reproducible. -**DRAFT** +```{r} +# Set the seed so our results are reproducible: +set.seed(2020) +``` + +## Import data We will read in the differential expression results we will download from online. -These results are from a acute myeloid leukemia (AML) RNA-seq dataset we used for [differential expression analysis](https://alexslemonade.github.io/refinebio-examples/03-rnaseq/differential-expression_rnaseq_01.html) using `DESeq2` [@Love2014]. -The table contains summary statistics including Ensembl gene IDs, log fold change values, and adjusted p-values (FDR in this case). +These results are from an acute myeloid leukemia (AML) RNA-seq dataset we used for [differential expression analysis](https://alexslemonade.github.io/refinebio-examples/03-rnaseq/differential-expression_rnaseq_01.html) using `DESeq2` [@Love2014]. +The table contains summary statistics including Ensembl gene IDs, log2 fold change 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. -**REVIEW** - 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`. @@ -201,7 +198,7 @@ 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. +Let's take a look at what the contrast results from the differential expression analysis looks like. ```{r} dge_df @@ -226,24 +223,18 @@ MSigDB contains 8 different gene set collections [@Subramanian2005]. 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. - -**REVIEW** - -The data we're interested in here comes from human samples, so we can obtain just the gene sets relevant to _Homo sapiens_ by specifying `species = "Homo sapiens"` +The data we're interested in here comes from human samples, so we can obtain just the gene sets (from all of the gene set collections listed above) relevant to _Homo sapiens_ by specifying `species = "Homo sapiens"`. ```{r} hs_gene_sets <- msigdbr( - species = "Homo sapiens" # Replace with species name relevant to your data + species = "Homo sapiens", # Replace with species name relevant to your data, + category = "C2", + subcategory = "KEGG" ) ``` If you run the chunk above with specifying `category = "H"` to the `msigdbr()` function, it will return only the Hallmark gene sets for _Homo sapiens_. +See `?msigdbr` for more options. Let's preview what's in `hs_gene_sets`. @@ -256,7 +247,6 @@ 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. -**IDENTICAL TO MICROARRAY EXAMPLE** (except "ENTREZID" --> "SYMBOL" and "Dr" --> "Hs") ## Gene identifier conversion We're going to convert our identifiers in `dge_df` to gene symbols, but you can, with the change of a single argument, use the same code to convert to many other types of identifiers! @@ -270,10 +260,7 @@ We can see what types of IDs are available to us in an annotation package with ` keytypes(org.Hs.eg.db) ``` -Even though we'll use this package to convert from Ensembl gene IDs (`ENSEMBL`) to gene symbols (`SYMBOL`), we could just as easily use it to convert from an Ensembl transcript ID (`ENSEMBLTRANS`) to Entrez IDs (`ENTREZID`). - -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). +Even though we'll use this package to convert from Ensembl gene IDs (`ENSEMBL`) to gene symbols (`SYMBOL`), we could just as easily use it to convert to and from any of these `keytypes()` listed above. The function we will use to map from Ensembl gene IDs to gene symbols is called `mapIds()`. @@ -302,7 +289,8 @@ dge_mapped_df <- data.frame( This `1:many mapping between keys and columns` message means that some Ensembl gene identifiers map to multiple gene symbols. In this case, it's also possible that a gene symbol 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). +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). Let's see a preview of `dge_mapped_df`. @@ -328,8 +316,6 @@ Let's find out which gene symbols have been duplicated. dup_gene_symbols <- dge_mapped_df %>% dplyr::filter(duplicated(gene_symbol)) %>% dplyr::pull(gene_symbol) - -dup_gene_symbols ``` Now let's take a look at the rows associated with the duplicated gene symbols. @@ -339,35 +325,39 @@ dge_mapped_df %>% dplyr::filter(gene_symbol %in% dup_gene_symbols) %>% dplyr::arrange(gene_symbol) ``` -**IDENTICAL TO MICROARRAY EXAMPLE** (except using absolute log2 fold change value instead of 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 gene symbols associated with the higher absolute value of the log fold change. +As we mentioned earlier, we will want to remove duplicated gene identifiers in preparation for the GSEA steps later, so let's keep the gene symbols associated with the higher ranked absolute value of the log2 fold change. GSEA relies on genes' rankings on the basis of this statistic. Retaining the instance of the gene symbol 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. +In the next chunk, we are going to filter out the duplicated row using the `dplyr::distinct()` function. +This will keep the first row with the duplicated value thus keeping the row with the highest ranked absolute value of the log2 fold change. + ```{r} filtered_dge_mapped_df <- dge_mapped_df %>% - # Sort so that the highest absolute values of the log fold change are at the top - dplyr::arrange(dplyr::desc(abs(log2FoldChange))) %>% - # 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 absolute value of the log fold change + # First let's create a column to hold the rankings of our absolute log2 fold + # change values -- this handles duplicate log2 fold change values + dplyr::mutate(rank = rank(abs(log2FoldChange), ties.method = "random")) %>% + # Sort so that the highest ranked absolute values of the log2 fold change are at the top + dplyr::arrange(dplyr::desc(rank)) %>% + # Filter out the duplicated rows using `dplyr::distinct()` dplyr::distinct(gene_symbol, .keep_all = TRUE) ``` -Let's check to see that we removed the duplicate gene symbols and kept the rows with the higher absolute value of the log fold change. +Note that the log2 fold change values we use here for ranking have been transformed by `DESeq2` to account for genes with low counts or highly variable counts. +See the [`DESeq2` package vignette](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) for more information on how `DESeq2` handles the log2 fold change values with the `lfcShrink()` function. + +Let's check to see that we removed the duplicate gene symbols and kept the rows with the higher ranked absolute value of the log2 fold change. ```{r} -filtered_dge_mapped_df %>% - dplyr::filter(gene_symbol %in% dup_gene_symbols) %>% - dplyr::arrange(gene_symbol) +any(duplicated(filtered_dge_mapped_df$gene_symbol)) ``` -Looks like we were able to successfully get rid of the duplicate gene identifiers and keep the observations with the higher absolute value of the log fold change! +Looks like we were able to successfully get rid of the duplicate gene identifiers and keep the observations with the higher ranked absolute value of the log2 fold change! ## Perform gene set enrichment analysis (GSEA) @@ -379,22 +369,22 @@ This is done by ranking genes within a gene set from most highly positive to mos The `GSEA()` function takes a pre-ranked and sorted named vector of statistics, where the names in the vector are gene identifiers. We will therefore need to create a gene list with statistics that GSEA will rank by. -In the next chunk, we will create a named vector ranked based on the gene-level log fold change values. +In the next chunk, we will create a named vector ranked based on the rankings of the gene-level log2 fold change values. ```{r} -# Let's create a named vector ranked based on the log fold change values -lfc_vector <- filtered_dge_mapped_df$log2FoldChange -names(lfc_vector) <- filtered_dge_mapped_df$gene_symbol +# Let's create a named vector of the rankings based on the log2 fold change values +ranked_vector <- filtered_dge_mapped_df$rank +names(ranked_vector) <- filtered_dge_mapped_df$gene_symbol -# We need to sort the log fold change values in descending order here -lfc_vector <- sort(lfc_vector, decreasing = TRUE) +# We need to sort the ranked vector in descending order here +ranked_vector <- sort(ranked_vector, decreasing = TRUE) ``` Let's preview our pre-ranked named vector. ```{r} -# Look at first entries of the ranked log fold change vector -head(lfc_vector) +# Look at first entries of the ranked vector +head(ranked_vector) ``` ### Run GSEA using the `GSEA()` function @@ -406,13 +396,14 @@ We can use the `GSEA()` function to perform GSEA with any generic set of gene se ```{r} gsea_results <- GSEA( - geneList = lfc_vector, # ordered ranked gene list + geneList = ranked_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 + scoreType = "pos", TERM2GENE = dplyr::select( hs_gene_sets, gs_name, @@ -424,14 +415,14 @@ gsea_results <- GSEA( Let's take a look at the results. ```{r} -# We can access the results from our gseaResult object using `@result` +# We can access the results from our `gsea_results` object using `@result` 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. + +Looks like we have gene sets returned as significant at FDR of `0.05`, according to the `p.adjust` column 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. @@ -440,12 +431,11 @@ Let's convert this into a data frame that we can use for further analysis and wr gsea_result_df <- data.frame(gsea_results@result) ``` -**IDENTICAL TO MICROARRAY EXAMPLE** - ## 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. +Note that if we didn't have any significant pathways, our visualizations below would show up blank as nothing would have met our `pvalueCutoff` above. ### Most Positive NES @@ -453,26 +443,25 @@ Let's look for the gene set with the most 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 = 3, wt = NES) + # Use `slice_max()` to return the top n observation using `NES` as the ordering variable + dplyr::slice_max(NES, n = 3) ``` -The gene set `HP_VERTEBRAL_CLEFTING` has the most positive NES score. +The gene set `KEGG_SELENOAMINO_ACID_METABOLISM` has the most positive NES score. ```{r} most_positive_nes_plot <- enrichplot::gseaplot( gsea_results, - geneSetID = "HP_VERTEBRAL_CLEFTING", - title = "HP_VERTEBRAL_CLEFTING", + geneSetID = "KEGG_SELENOAMINO_ACID_METABOLISM", + title = "KEGG_SELENOAMINO_ACID_METABOLISM", color.line = "#0d76ff" ) 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). -As mentioned earlier, an ES is calculated by starting with the most highly ranked genes (according to the gene-level log fold change 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 log2 fold change 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. @@ -491,28 +480,26 @@ 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 `-n` to display the top n values when sorted - # in descending order - dplyr::top_n(n = -3, wt = NES) + # Use `slice_min()` to return the bottom n observation using `NES` as the ordering + # variable + dplyr::slice_min(NES, n = 3) ``` -The gene set `HOLLERN_EMT_BREAST_TUMOR_DN` has the most negative NES. +The gene set `KEGG_TIGHT_JUNCTION` has the most negative NES. ```{r} most_negative_nes_plot <- enrichplot::gseaplot( gsea_results, - geneSetID = "HOLLERN_EMT_BREAST_TUMOR_DN", - title = "HOLLERN_EMT_BREAST_TUMOR_DN", + geneSetID = "KEGG_TIGHT_JUNCTION", + title = "KEGG_TIGHT_JUNCTION", color.line = "#0d76ff" ) 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. -As we know, the ES is calculated by starting with the most highly ranked genes (according to the gene-level log fold change 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 we know, the ES is calculated by starting with the most highly ranked genes (according to the gene-level log2 fold change 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. 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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 f06c15fab4309a4647e10cc002bb68c465da8a1e Mon Sep 17 00:00:00 2001 From: Chante Bethell Date: Wed, 2 Dec 2020 09:09:14 -0500 Subject: [PATCH 3/7] Add draft for GSEA RNA-seq example notebook --- 03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd | 556 ++ .../pathway-analysis_rnaseq_02_gsea.html | 4533 +++++++++++++++++ Snakefile | 1 + 3 files changed, 5090 insertions(+) create mode 100644 03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd create mode 100644 03-rnaseq/pathway-analysis_rnaseq_02_gsea.html diff --git a/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd new file mode 100644 index 00000000..b4adbe0e --- /dev/null +++ b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd @@ -0,0 +1,556 @@ +--- +title: "Gene set enrichment analysis - RNA-seq" +author: "CCDL for ALSF" +date: "November 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 table below](#how-to-choose-a-pathway-analysis) to help you determine which pathway analysis method is best suited for your purposes. + +**IDENTICAL TO MICROARRAY EXAMPLE** + +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) ⬇️ + +**IDENTICAL TO OTHER PATHWAY ANALYSIS EXAMPLES** + +### What is pathway analysis? + +Pathway analysis refers to any one of many techniques that uses predetermined sets of genes that are related or coordinated in their expression in some way (e.g., participate in the same molecular process, are regulated by the same transcription factor) to interpret a high-throughput experiment. +In the context of [refine.bio](https://www.refine.bio/), we use these techniques to analyze and interpret genome-wide gene expression experiments. +The rationale for performing pathway analysis is that looking at the pathway-level may be more biologically meaningful than considering individual genes, especially if a large number of genes are differentially expressed between conditions of interest. +In addition, many relatively small changes in the expression values of genes in the same pathway could lead to a phenotypic outcome and these small changes may go undetected in differential gene expression analysis. + +We highly recommend taking a look at [Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002375) from @Khatri2012 for a more comprehensive overview. We have provided primary publications and documentation of the methods we will introduce below as well as some recommended reading in the [`Resources for further learning` section](#resources-for-further-learning). + +### How to choose a pathway analysis? + +This table summarizes the pathway analyses examples in this module. + +|Analysis|What is required for input|What output looks like |✅ Pros| ⚠️ Cons| +|--------|--------------------------|-----------------------|-------|-------| +|[**ORA (Over-representation Analysis)**](https://alexslemonade.github.io/refinebio-examples/03-rnaseq/pathway-analysis_rnaseq_01_ora.html)|A list of gene IDs (no stats needed)|A per-pathway hypergeometric test result|- Simple

    - Inexpensive computationally to calculate p-values| - Requires arbitrary thresholds and ignores any statistics associated with a gene

    - Assumes independence of genes and pathways| +|[**GSEA (Gene Set Enrichment Analysis)**](https://alexslemonade.github.io/refinebio-examples/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html)|A list of genes IDs with gene-level summary statistics|A per-pathway enrichment score|- Includes all genes (no arbitrary threshold!)

    - Attempts to measure coordination of genes|- Permutations can be expensive

    - Does not account for pathway overlap

    - Two-group comparisons not always appropriate/feasible| +|[**GSVA (Gene Set Variation Analysis)**](https://alexslemonade.github.io/refinebio-examples/03-rnaseq/pathway-analysis_rnaseq_03_gsva.html)|A gene expression matrix (like what you get from refine.bio directly)|Pathway-level scores on a per-sample basis|- Does not require two groups to compare upfront

    - Normally distributed scores|- Scores are not a good fit for gene sets that contain genes that go up AND down

    - Method doesn’t assign statistical significance itself

    - Recommended sample size n > 10| + +# 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/03-rnaseq/pathway-analysis_rnaseq_02_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 + +**DRAFT** + +In this example, we are using differential expression results table we obtained from an [example analysis of an acute myeloid leukemia (AML) dataset](https://alexslemonade.github.io/refinebio-examples/03-rnaseq/differential-expression_rnaseq_01.html) using the `DESeq2` package [@Love2014]. +The table contains summary statistics including Ensembl gene IDs, log fold change 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/03-rnaseq/differential-expression_rnaseq_01.html). + +## About the dataset we are using for this example + +For this example analysis, we will use this [acute myeloid leukemia (AML) dataset](https://www.refine.bio/experiments/SRP078441/rna-seq-of-primary-patient-aml-samples) [@Micol2017]. + +@Micol2017 performed RNA-seq on primary peripheral blood and bone marrow samples from AML patients with and without _ASXL1/2_ mutations. + +## 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 - RNA-seq + +## 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.Hs.eg.db`](http://bioconductor.org/packages/release/data/annotation/html/org.Hs.eg.db.html) package to perform gene identifier conversion [@Carlson2020-human]. + +```{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.Hs.eg.db" %in% installed.packages())) { + # Install this package if it isn't installed yet + BiocManager::install("org.Hs.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) + +# Human annotation package we'll use for gene identifier conversion +library(org.Hs.eg.db) + +# We will need this so we can use the pipe: %>% +library(magrittr) +``` + +## Import data + +**DRAFT** + +We will read in the differential expression results we will download from online. +These results are from a acute myeloid leukemia (AML) RNA-seq dataset we used for [differential expression analysis](https://alexslemonade.github.io/refinebio-examples/03-rnaseq/differential-expression_rnaseq_01.html) using `DESeq2` [@Love2014]. +The table contains summary statistics including Ensembl gene IDs, log fold change 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. + +**REVIEW** + +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/03-rnaseq/results/SRP078441/SRP078441_differential_expression_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", "SRP078441_differential_expression_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. + +**REVIEW** + +The data we're interested in here comes from human samples, so we can obtain just the gene sets relevant to _Homo sapiens_ by specifying `species = "Homo sapiens"` + +```{r} +hs_gene_sets <- msigdbr( + species = "Homo sapiens" # Replace with species name relevant to your data +) +``` + +If you run the chunk above with specifying `category = "H"` to the `msigdbr()` function, it will return only the Hallmark gene sets for _Homo sapiens_. + +Let's preview what's in `hs_gene_sets`. + +```{r} +head(hs_gene_sets) +``` + +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. + +**IDENTICAL TO MICROARRAY EXAMPLE** (except "ENTREZID" --> "SYMBOL" and "Dr" --> "Hs") +## Gene identifier conversion + +We're going to convert our identifiers in `dge_df` to gene symbols, 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.Hs.eg.db` contains information for different identifiers [@Carlson2020-human]. +`org.Hs.eg.db` is specific to _Homo sapiens_ -- this is what the `Hs` 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.Hs.eg.db) +``` + +Even though we'll use this package to convert from Ensembl gene IDs (`ENSEMBL`) to gene symbols (`SYMBOL`), we could just as easily use it to convert from an Ensembl transcript ID (`ENSEMBLTRANS`) to Entrez IDs (`ENTREZID`). + +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 gene symbols is called `mapIds()`. + +Let's create a data frame that shows the mapped gene symbols 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( + "gene_symbol" = mapIds( + org.Hs.eg.db, # Replace with annotation package for the organism relevant to your data + keys = dge_df$Gene, + column = "SYMBOL", # 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 gene symbol, drop that + # from the data frame + dplyr::filter(!is.na(gene_symbol)) %>% + # 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 gene symbols. +In this case, it's also possible that a gene symbol 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) +``` + +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 gene symbols that mapped to multiple Ensembl IDs. + +```{r} +any(duplicated(dge_mapped_df$gene_symbol)) +``` + +Looks like we do have duplicated gene symbols. +Let's find out which gene symbols have been duplicated. + +```{r} +dup_gene_symbols <- dge_mapped_df %>% + dplyr::filter(duplicated(gene_symbol)) %>% + dplyr::pull(gene_symbol) + +dup_gene_symbols +``` + +Now let's take a look at the rows associated with the duplicated gene symbols. + +```{r} +dge_mapped_df %>% + dplyr::filter(gene_symbol %in% dup_gene_symbols) %>% + dplyr::arrange(gene_symbol) +``` +**IDENTICAL TO MICROARRAY EXAMPLE** (except using absolute log2 fold change value instead of 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 gene symbols associated with the higher absolute value of the log fold change. +GSEA relies on genes' rankings on the basis of this statistic. +Retaining the instance of the gene symbol 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. + +```{r} +filtered_dge_mapped_df <- dge_mapped_df %>% + # Sort so that the highest absolute values of the log fold change are at the top + dplyr::arrange(dplyr::desc(abs(log2FoldChange))) %>% + # 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 absolute value of the log fold change + dplyr::distinct(gene_symbol, .keep_all = TRUE) +``` + +Let's check to see that we removed the duplicate gene symbols and kept the rows with the higher absolute value of the log fold change. + +```{r} +filtered_dge_mapped_df %>% + dplyr::filter(gene_symbol %in% dup_gene_symbols) %>% + dplyr::arrange(gene_symbol) +``` + +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 log fold change! + +## Perform gene set enrichment analysis (GSEA) + +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 + +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 log fold change values. + +```{r} +# Let's create a named vector ranked based on the log fold change values +lfc_vector <- filtered_dge_mapped_df$log2FoldChange +names(lfc_vector) <- filtered_dge_mapped_df$gene_symbol + +# We need to sort the log fold change values in descending order here +lfc_vector <- sort(lfc_vector, decreasing = TRUE) +``` + +Let's preview our pre-ranked named vector. + +```{r} +# Look at first entries of the ranked log fold change vector +head(lfc_vector) +``` + +### Run GSEA using the `GSEA()` function + +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()`). + +```{r} +gsea_results <- GSEA( + geneList = lfc_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( + hs_gene_sets, + gs_name, + gene_symbol + ) +) +``` + +Let's take a look at the results. + +```{r} +# We can access the results from our gseaResult object using `@result` +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. + +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) +``` + +**IDENTICAL TO MICROARRAY EXAMPLE** + +## 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. + +### Most Positive NES + +Let's look for the gene set with the most 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 = 3, wt = NES) +``` + +The gene set `HP_VERTEBRAL_CLEFTING` has the most positive NES score. + +```{r} +most_positive_nes_plot <- enrichplot::gseaplot( + gsea_results, + geneSetID = "HP_VERTEBRAL_CLEFTING", + title = "HP_VERTEBRAL_CLEFTING", + color.line = "#0d76ff" +) + +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). +As mentioned earlier, an ES is calculated by starting with the most highly ranked genes (according to the gene-level log fold change 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. + +```{r} +ggplot2::ggsave(file.path(plots_dir, "SRP078441_gsea_enrich_positive_plot.png"), + plot = most_positive_nes_plot +) +``` + +### Most 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 `-n` to display the top n values when sorted + # in descending order + dplyr::top_n(n = -3, wt = NES) +``` + +The gene set `HOLLERN_EMT_BREAST_TUMOR_DN` has the most negative NES. + +```{r} +most_negative_nes_plot <- enrichplot::gseaplot( + gsea_results, + geneSetID = "HOLLERN_EMT_BREAST_TUMOR_DN", + title = "HOLLERN_EMT_BREAST_TUMOR_DN", + color.line = "#0d76ff" +) + +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. +As we know, the ES is calculated by starting with the most highly ranked genes (according to the gene-level log fold change 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. + +```{r} +ggplot2::ggsave(file.path(plots_dir, "SRP078441_gsea_enrich_negative_plot.png"), + plot = most_negative_nes_plot +) +``` + +## Write results to file + +```{r} +readr::write_tsv( + gsea_result_df, + file.path( + results_dir, + "SRP078441_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/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html new file mode 100644 index 00000000..88e5a6b5 --- /dev/null +++ b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html @@ -0,0 +1,4533 @@ + + + + + + + + + + + + + + +Gene set enrichment analysis - RNA-seq + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    1 Purpose of this analysis

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

    -

    IDENTICAL TO MICROARRAY EXAMPLE

    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 ⬇️

    -

    IDENTICAL TO OTHER PATHWAY ANALYSIS EXAMPLES

    1.0.1 What is pathway analysis?

    Pathway analysis refers to any one of many techniques that uses predetermined sets of genes that are related or coordinated in their expression in some way (e.g., participate in the same molecular process, are regulated by the same transcription factor) to interpret a high-throughput experiment. In the context of refine.bio, we use these techniques to analyze and interpret genome-wide gene expression experiments. The rationale for performing pathway analysis is that looking at the pathway-level may be more biologically meaningful than considering individual genes, especially if a large number of genes are differentially expressed between conditions of interest. In addition, many relatively small changes in the expression values of genes in the same pathway could lead to a phenotypic outcome and these small changes may go undetected in differential gene expression analysis.

    @@ -3865,8 +3863,7 @@

    2.2 Set up your analysis folders<

    2.3 Obtain the gene set for this example

    -

    DRAFT

    -

    In this example, we are using differential expression results table we obtained from an example analysis of an acute myeloid leukemia (AML) dataset using the DESeq2 package (Love et al. 2014). The table contains summary statistics including Ensembl gene IDs, log fold change values, and adjusted p-values (FDR in this case).

    +

    In this example, we are using the differential expression results table we obtained from an example analysis of an acute myeloid leukemia (AML) dataset using the DESeq2 package (Love et al. 2014). The table contains summary statistics including Ensembl gene IDs, log2 fold change 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.

    @@ -3919,89 +3916,33 @@

    4.1 Install libraries

    }

    Attach the packages we need for this analysis.

    # Attach the library
-library(clusterProfiler)
    -
    ##
    -
    ## clusterProfiler v3.18.0  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)
-
-# Human annotation package we'll use for gene identifier conversion
-library(org.Hs.eg.db)
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    ## Loading required package: AnnotationDbi
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-##     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.max, which.min
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    ## Welcome to Bioconductor
-## 
-##     Vignettes contain introductory material; view with
-##     'browseVignettes()'. To cite Bioconductor, see
-##     'citation("Biobase")', and for packages 'citation("pkgname")'.
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    ## Loading required package: IRanges
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-## Attaching package: 'S4Vectors'
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-##     rename
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-## Attaching package: 'IRanges'
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-##     select
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    ##
    -
    # We will need this so we can use the pipe: %>%
-library(magrittr)
    +library(clusterProfiler) + +# Package that contains MSigDB gene sets in tidy format +library(msigdbr) + +# Human annotation package we'll use for gene identifier conversion +library(org.Hs.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.

    +
    # Set the seed so our results are reproducible:
+set.seed(2020)

    4.2 Import data

    -

    DRAFT

    -

    We will read in the differential expression results we will download from online. These results are from a acute myeloid leukemia (AML) RNA-seq dataset we used for differential expression analysis using DESeq2 (Love et al. 2014). The table contains summary statistics including Ensembl gene IDs, log fold change 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.

    -

    REVIEW

    +

    We will read in the differential expression results we will download from online. These results are from an acute myeloid leukemia (AML) RNA-seq dataset we used for differential expression analysis using DESeq2 (Love et al. 2014). The table contains summary statistics including Ensembl gene IDs, log2 fold change 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/03-rnaseq/results/SRP078441/SRP078441_differential_expression_results.tsv"
    +
    # Define the url to your differential expression results file
+dge_url <- "https://refinebio-examples.s3.us-east-2.amazonaws.com/03-rnaseq/results/SRP078441/SRP078441_differential_expression_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", "SRP078441_differential_expression_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(),
 ##   baseMean = col_double(),
@@ -4011,8 +3952,8 @@ 
    4.2 Import data
    
 ##   padj = col_double(),
 ##   threshold = col_logical()
 ## )
    -

    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
    +

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

    +
    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.

    -

    IDENTICAL TO MICROARRAY EXAMPLE (except “ENTREZID” –> “SYMBOL” and “Dr” –> “Hs”) ## Gene identifier conversion

    +
    +
    +

    4.4 Gene identifier conversion

    We’re going to convert our identifiers in dge_df to gene symbols, 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.Hs.eg.db contains information for different identifiers (Carlson 2020). org.Hs.eg.db is specific to Homo sapiens – this is what the Hs 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.Hs.eg.db)
    +
    keytypes(org.Hs.eg.db)
    ##  [1] "ACCNUM"       "ALIAS"        "ENSEMBL"      "ENSEMBLPROT"  "ENSEMBLTRANS"
 ##  [6] "ENTREZID"     "ENZYME"       "EVIDENCE"     "EVIDENCEALL"  "GENENAME"    
 ## [11] "GO"           "GOALL"        "IPI"          "MAP"          "OMIM"        
 ## [16] "ONTOLOGY"     "ONTOLOGYALL"  "PATH"         "PFAM"         "PMID"        
 ## [21] "PROSITE"      "REFSEQ"       "SYMBOL"       "UCSCKG"       "UNIGENE"     
 ## [26] "UNIPROT"
    -

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

    -

    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.

    +

    Even though we’ll use this package to convert from Ensembl gene IDs (ENSEMBL) to gene symbols (SYMBOL), we could just as easily use it to convert to and from any of these keytypes() listed above.

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

    Let’s create a data frame that shows the mapped gene symbols 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(
-  "gene_symbol" = mapIds(
-    org.Hs.eg.db, # Replace with annotation package for the organism relevant to your data
-    keys = dge_df$Gene,
-    column = "SYMBOL", # Replace with the type of gene identifiers you would like to map to
-    keytype = "ENSEMBL", # Replace with the type of gene identifiers in your data
-    multiVals = "first" # This will keep only the first mapped value for each Ensembl ID
-  )
-) %>%
-  # If an Ensembl gene identifier doesn't map to a gene symbol, drop that
-  # from the data frame
-  dplyr::filter(!is.na(gene_symbol)) %>%
-  # Make an `Ensembl` column to store the rownames
-  tibble::rownames_to_column("Ensembl") %>%
-  # Now let's join the rest of the expression data
-  dplyr::inner_join(dge_df, by = c("Ensembl" = "Gene"))
    +
    # First let's create a mapped data frame we can join to the differential expression stats
+dge_mapped_df <- data.frame(
+  "gene_symbol" = mapIds(
+    org.Hs.eg.db, # Replace with annotation package for the organism relevant to your data
+    keys = dge_df$Gene,
+    column = "SYMBOL", # 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 gene symbol, drop that
+  # from the data frame
+  dplyr::filter(!is.na(gene_symbol)) %>%
+  # 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 gene symbols. In this case, it’s also possible that a gene symbol 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.

    +

    This 1:many mapping between keys and columns message means that some Ensembl gene identifiers map to multiple gene symbols. In this case, it’s also possible that a gene symbol will map to multiple Ensembl IDs. For the purpose of performing GSEA later in this notebook, we keep only the first mapped IDs. 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.

    Let’s see a preview of dge_mapped_df.

    -
    head(dge_mapped_df)
    +
    head(dge_mapped_df)
    -

    IDENTICAL TO MICROARRAY EXAMPLE (except using absolute log2 fold change value instead of 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 gene symbols associated with the higher absolute value of the log fold change. GSEA relies on genes’ rankings on the basis of this statistic. Retaining the instance of the gene symbol 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.

    -
    filtered_dge_mapped_df <- dge_mapped_df %>%
-  # Sort so that the highest absolute values of the log fold change are at the top
-  dplyr::arrange(dplyr::desc(abs(log2FoldChange))) %>%
-  # 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 absolute value of the log fold change
-  dplyr::distinct(gene_symbol, .keep_all = TRUE)
    -

    Let’s check to see that we removed the duplicate gene symbols and kept the rows with the higher absolute value of the log fold change.

    -
    filtered_dge_mapped_df %>%
-  dplyr::filter(gene_symbol %in% dup_gene_symbols) %>%
-  dplyr::arrange(gene_symbol)
    -
    - -
    -

    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 log fold change!

    +

    As we mentioned earlier, we will want to remove duplicated gene identifiers in preparation for the GSEA steps later, so let’s keep the gene symbols associated with the higher ranked absolute value of the log2 fold change. GSEA relies on genes’ rankings on the basis of this statistic. Retaining the instance of the gene symbol 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.

    +

    In the next chunk, we are going to filter out the duplicated row using the dplyr::distinct() function. This will keep the first row with the duplicated value thus keeping the row with the highest ranked absolute value of the log2 fold change.

    +
    filtered_dge_mapped_df <- dge_mapped_df %>%
+  # First let's create a column to hold the rankings of our absolute log2 fold
+  # change values -- this handles duplicate log2 fold change values
+  dplyr::mutate(rank = rank(abs(log2FoldChange), ties.method = "random")) %>%
+  # Sort so that the highest ranked absolute values of the log2 fold change are at the top
+  dplyr::arrange(dplyr::desc(rank)) %>%
+  # Filter out the duplicated rows using `dplyr::distinct()`
+  dplyr::distinct(gene_symbol, .keep_all = TRUE)
    +

    Note that the log2 fold change values we use here for ranking have been transformed by DESeq2 to account for genes with low counts or highly variable counts. See the DESeq2 package vignette for more information on how DESeq2 handles the log2 fold change values with the lfcShrink() function.

    +

    Let’s check to see that we removed the duplicate gene symbols and kept the rows with the higher ranked absolute value of the log2 fold change.

    +
    any(duplicated(filtered_dge_mapped_df$gene_symbol))
    +
    ## [1] FALSE
    +

    Looks like we were able to successfully get rid of the duplicate gene identifiers and keep the observations with the higher ranked absolute value of the log2 fold change!

    -

    4.4 Perform gene set enrichment analysis (GSEA)

    +

    4.5 Perform gene set enrichment analysis (GSEA)

    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).

    -

    4.4.1 Determine our pre-ranked genes list

    +

    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. 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 log fold change values.

    -
    # Let's create a named vector ranked based on the log fold change values
-lfc_vector <- filtered_dge_mapped_df$log2FoldChange
-names(lfc_vector) <- filtered_dge_mapped_df$gene_symbol
-
-# We need to sort the log fold change values in descending order here
-lfc_vector <- sort(lfc_vector, decreasing = TRUE)
    +

    In the next chunk, we will create a named vector ranked based on the rankings of the gene-level log2 fold change values.

    +
    # Let's create a named vector of the rankings based on the log2 fold change values
+ranked_vector <- filtered_dge_mapped_df$rank
+names(ranked_vector) <- filtered_dge_mapped_df$gene_symbol
+
+# We need to sort the ranked vector in descending order here
+ranked_vector <- sort(ranked_vector, decreasing = TRUE)

    Let’s preview our pre-ranked named vector.

    -
    # Look at first entries of the ranked log fold change vector
-head(lfc_vector)
    -
    ##     UTS2     ALPL   GRIN2B      ADM    KRT23    IL1R2 
-## 6.174579 5.824919 4.172007 3.952953 3.731020 3.717043
    +
    # Look at first entries of the ranked vector
+head(ranked_vector)
    +
    ##      CLDN3 NUTM2B-AS1  MIR3648-2       UTS2       ALPL   ANKRD34A 
+##      22643      22642      22641      22640      22639      22638
    -

    4.4.2 Run GSEA using the GSEA() function

    +

    4.5.2 Run GSEA using the GSEA() function

    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 = lfc_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(
-    hs_gene_sets,
-    gs_name,
-    gene_symbol
-  )
-)
    +
    gsea_results <- GSEA(
+  geneList = ranked_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
+  scoreType = "pos",
+  TERM2GENE = dplyr::select(
+    hs_gene_sets,
+    gs_name,
+    gene_symbol
+  )
+)
    ## preparing geneSet collections...
    ## GSEA analysis...
    -
    ## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
-## The order of those tied genes will be arbitrary, which may produce unexpected results.
    -
    ## no term enriched under specific pvalueCutoff...
    +
    ## 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)
    +
    # We can access the results from our `gsea_results` object using `@result`
+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.

    +

    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, according to the p.adjust column 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)
    -

    IDENTICAL TO MICROARRAY EXAMPLE

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

    4.5 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 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. Note that if we didn’t have any significant pathways, our visualizations below would show up blank as nothing would have met our pvalueCutoff above.

    -

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

    The gene set HP_VERTEBRAL_CLEFTING has the most positive NES score.

    -
    most_positive_nes_plot <- enrichplot::gseaplot(
-  gsea_results,
-  geneSetID = "HP_VERTEBRAL_CLEFTING",
-  title = "HP_VERTEBRAL_CLEFTING",
-  color.line = "#0d76ff"
-)
-
-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). As mentioned earlier, an ES is calculated by starting with the most highly ranked genes (according to the gene-level log fold change 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 gene set KEGG_SELENOAMINO_ACID_METABOLISM has the most positive NES score.

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

    +

    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 log2 fold change 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, "SRP078441_gsea_enrich_positive_plot.png"),
-  plot = most_positive_nes_plot
-)
    +
    ggplot2::ggsave(file.path(plots_dir, "SRP078441_gsea_enrich_positive_plot.png"),
+  plot = most_positive_nes_plot
+)
    ## Saving 7 x 5 in image
    -

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

    The gene set HOLLERN_EMT_BREAST_TUMOR_DN has the most negative NES.

    -
    most_negative_nes_plot <- enrichplot::gseaplot(
-  gsea_results,
-  geneSetID = "HOLLERN_EMT_BREAST_TUMOR_DN",
-  title = "HOLLERN_EMT_BREAST_TUMOR_DN",
-  color.line = "#0d76ff"
-)
-
-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. As we know, the ES is calculated by starting with the most highly ranked genes (according to the gene-level log fold change 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.

    +

    The gene set KEGG_TIGHT_JUNCTION has the most negative NES.

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

    +

    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 log2 fold change 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, "SRP078441_gsea_enrich_negative_plot.png"),
-  plot = most_negative_nes_plot
-)
    +
    ggplot2::ggsave(file.path(plots_dir, "SRP078441_gsea_enrich_negative_plot.png"),
+  plot = most_negative_nes_plot
+)
    ## Saving 7 x 5 in image
    -

    4.6 Write results to file

    -
    readr::write_tsv(
-  gsea_result_df,
-  file.path(
-    results_dir,
-    "SRP078441_gsea_results.tsv"
-  )
-)
    +

    4.7 Write results to file

    +
    readr::write_tsv(
+  gsea_result_df,
+  file.path(
+    results_dir,
+    "SRP078441_gsea_results.tsv"
+  )
+)
    @@ -4292,8 +4211,8 @@

    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)
@@ -4304,7 +4223,7 @@ 
    6 Session info
    
 ##  collate  en_US.UTF-8                 
 ##  ctype    en_US.UTF-8                 
 ##  tz       Etc/UTC                     
-##  date     2020-12-02                  
+##  date     2020-12-04                  
 ## 
 ## ─ Packages ───────────────────────────────────────────────────────────────────
 ##  package         * version  date       lib source        
@@ -4327,7 +4246,7 @@ 
    6 Session info
    
 ##  data.table        1.13.0   2020-07-24 [1] RSPM (R 4.0.2)
 ##  DBI               1.1.0    2019-12-15 [1] RSPM (R 4.0.0)
 ##  digest            0.6.25   2020-02-23 [1] RSPM (R 4.0.0)
-##  DO.db             2.9      2020-11-18 [1] Bioconductor  
+##  DO.db             2.9      2020-12-02 [1] Bioconductor  
 ##  DOSE              3.16.0   2020-10-27 [1] Bioconductor  
 ##  downloader        0.4      2015-07-09 [1] RSPM (R 4.0.0)
 ##  dplyr             1.0.2    2020-08-18 [1] RSPM (R 4.0.2)
@@ -4345,7 +4264,7 @@ 
    6 Session info
    
 ##  ggraph            2.0.3    2020-05-20 [1] RSPM (R 4.0.2)
 ##  ggrepel           0.8.2    2020-03-08 [1] RSPM (R 4.0.2)
 ##  glue              1.4.2    2020-08-27 [1] RSPM (R 4.0.2)
-##  GO.db             3.12.1   2020-11-18 [1] Bioconductor  
+##  GO.db             3.12.1   2020-12-02 [1] Bioconductor  
 ##  GOSemSim          2.16.1   2020-10-29 [1] Bioconductor  
 ##  graphlayouts      0.7.0    2020-04-25 [1] RSPM (R 4.0.2)
 ##  gridExtra         2.3      2017-09-09 [1] RSPM (R 4.0.0)
@@ -4366,7 +4285,7 @@ 
    6 Session info
    
 ##  msigdbr         * 7.2.1    2020-10-02 [1] RSPM (R 4.0.2)
 ##  munsell           0.5.0    2018-06-12 [1] RSPM (R 4.0.0)
 ##  optparse        * 1.6.6    2020-04-16 [1] RSPM (R 4.0.0)
-##  org.Hs.eg.db    * 3.12.0   2020-11-18 [1] Bioconductor  
+##  org.Hs.eg.db    * 3.12.0   2020-12-02 [1] Bioconductor  
 ##  pillar            1.4.6    2020-07-10 [1] RSPM (R 4.0.2)
 ##  pkgconfig         2.0.3    2019-09-22 [1] RSPM (R 4.0.0)
 ##  plyr              1.8.6    2020-03-03 [1] RSPM (R 4.0.2)
@@ -4430,9 +4349,6 @@ 
    References
    
 
    
 
    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
    
-
    
 
    
 
    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
    
 
    

From 217b8678a9bed7024182bf51abecd773e4d6a6c1 Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Fri, 4 Dec 2020 19:02:54 -0500
Subject: [PATCH 6/7] add exploration plots and code

---
 03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd |  97 +++++++--
 .../pathway-analysis_rnaseq_02_gsea.html      | 203 +++++++++++-------
 2 files changed, 207 insertions(+), 93 deletions(-)

diff --git a/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd
index ac423381..0d5decc9 100644
--- a/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd
+++ b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd
@@ -227,9 +227,7 @@ The data we're interested in here comes from human samples, so we can obtain jus
 
 ```{r}
 hs_gene_sets <- msigdbr(
-  species = "Homo sapiens", # Replace with species name relevant to your data,
-  category = "C2",
-  subcategory = "KEGG"
+  species = "Homo sapiens", # Replace with species name relevant to your data
 )
 ```
 
@@ -341,9 +339,9 @@ This will keep the first row with the duplicated value thus keeping the row with
 filtered_dge_mapped_df <- dge_mapped_df %>%
   # First let's create a column to hold the rankings of our absolute log2 fold
   # change values -- this handles duplicate log2 fold change values
-  dplyr::mutate(rank = rank(abs(log2FoldChange), ties.method = "random")) %>%
+  # dplyr::mutate(rank = rank(abs(log2FoldChange), ties.method = "random")) %>%
   # Sort so that the highest ranked absolute values of the log2 fold change are at the top
-  dplyr::arrange(dplyr::desc(rank)) %>%
+  dplyr::arrange(dplyr::desc(abs(log2FoldChange))) %>%
   # Filter out the duplicated rows using `dplyr::distinct()`
   dplyr::distinct(gene_symbol, .keep_all = TRUE)
 ```
@@ -373,7 +371,7 @@ In the next chunk, we will create a named vector ranked based on the rankings of
 
 ```{r}
 # Let's create a named vector of the rankings based on the log2 fold change values
-ranked_vector <- filtered_dge_mapped_df$rank
+ranked_vector <- filtered_dge_mapped_df$log2FoldChange
 names(ranked_vector) <- filtered_dge_mapped_df$gene_symbol
 
 # We need to sort the ranked vector in descending order here
@@ -395,6 +393,12 @@ In this section, we will implement GSEA to calculate the enrichment score (ES) f
 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}
+gene_terms <- hs_gene_sets %>%
+  dplyr::select(
+    gs_name,
+    gene_symbol
+  )
+
 gsea_results <- GSEA(
   geneList = ranked_vector, # ordered ranked gene list
   minGSSize = 25, # minimum gene set size
@@ -403,12 +407,7 @@ gsea_results <- GSEA(
   eps = 0, # boundary for calculating the p value
   seed = TRUE, # set seed to make results reproducible
   pAdjustMethod = "BH", # Benjamini-Hochberg correction
-  scoreType = "pos",
-  TERM2GENE = dplyr::select(
-    hs_gene_sets,
-    gs_name,
-    gene_symbol
-  )
+  TERM2GENE = gene_terms
 )
 ```
 
@@ -447,13 +446,13 @@ gsea_result_df %>%
   dplyr::slice_max(NES, n = 3)
 ```
 
-The gene set `KEGG_SELENOAMINO_ACID_METABOLISM` has the most positive NES score.
+The gene set `HP_VERTEBRAL_CLEFTING` has the most positive NES score.
 
 ```{r}
 most_positive_nes_plot <- enrichplot::gseaplot(
   gsea_results,
-  geneSetID = "KEGG_SELENOAMINO_ACID_METABOLISM",
-  title = "KEGG_SELENOAMINO_ACID_METABOLISM",
+  geneSetID = "HP_VERTEBRAL_CLEFTING",
+  title = "HP_VERTEBRAL_CLEFTING",
   color.line = "#0d76ff"
 )
 
@@ -485,13 +484,13 @@ gsea_result_df %>%
   dplyr::slice_min(NES, n = 3)
 ```
 
-The gene set `KEGG_TIGHT_JUNCTION` has the most negative NES.
+The gene set `GO_LATERAL_PLASMA_MEMBRANE` has the most negative NES.
 
 ```{r}
 most_negative_nes_plot <- enrichplot::gseaplot(
   gsea_results,
-  geneSetID = "KEGG_TIGHT_JUNCTION",
-  title = "KEGG_TIGHT_JUNCTION",
+  geneSetID = "GO_LATERAL_PLASMA_MEMBRANE",
+  title = "GO_LATERAL_PLASMA_MEMBRANE",
   color.line = "#0d76ff"
 )
 
@@ -511,6 +510,68 @@ ggplot2::ggsave(file.path(plots_dir, "SRP078441_gsea_enrich_negative_plot.png"),
 )
 ```
 
+## Plot other pathways
+
+```{r}
+enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "HP_DECREASED_CIRCULATING_CORTISOL_LEVEL",
+  title = "HP_DECREASED_CIRCULATING_CORTISOL_LEVEL",
+  color.line = "#0d76ff"
+)
+```
+
+```{r}
+enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "BENPORATH_NOS_TARGETS",
+  title = "BENPORATH_NOS_TARGETS",
+  color.line = "#0d76ff"
+)
+
+benporath_genes <- hs_gene_sets %>%
+  dplyr::filter(gs_name == "BENPORATH_NOS_TARGETS")
+
+sum(filtered_dge_mapped_df$gene_symbol %in% benporath_genes$gene_symbol)
+length(benporath_genes$gene_symbol)
+```
+
+```{r}
+enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "GO_REGULATION_OF_BINDING",
+  title = "GO_REGULATION_OF_BINDING",
+  color.line = "#0d76ff"
+)
+```
+
+```{r}
+enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "KEGG_APOPTOSIS",
+  title = "KEGG_APOPTOSIS",
+  color.line = "#0d76ff"
+)
+```
+
+```{r}
+enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "KEGG_AMINOACYL_TRNA_BIOSYNTHESIS",
+  title = "KEGG_AMINOACYL_TRNA_BIOSYNTHESIS",
+  color.line = "#0d76ff"
+)
+```
+
+```{r}
+enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY",
+  title = "KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY",
+  color.line = "#0d76ff"
+)
+```
+
 ## Write results to file
 
 ```{r}
diff --git a/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html
index 55d5060e..44176e02 100644
--- a/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html
+++ b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html
@@ -3942,7 +3942,7 @@ 
    4.2 Import data
    
 # desired gene list results
 dge_df <- readr::read_tsv(dge_url)
    ## 
-## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────
+## ── Column specification ──────────────────────────────────────────────────────────────────────────────────
 ## cols(
 ##   Gene = col_character(),
 ##   baseMean = col_double(),
@@ -3980,16 +3980,14 @@ 
    4.3 Getting familiar with c
 C7: immunologic signatures

    The data we’re interested in here comes from human samples, so we can obtain just the gene sets (from all of the gene set collections listed above) relevant to Homo sapiens by specifying species = "Homo sapiens".

    hs_gene_sets <- msigdbr(
-  species = "Homo sapiens", # Replace with species name relevant to your data,
-  category = "C2",
-  subcategory = "KEGG"
-)
    + species = "Homo sapiens", # Replace with species name relevant to your data +)

    If you run the chunk above with specifying category = "H" to the msigdbr() function, it will return only the Hallmark gene sets for Homo sapiens. See ?msigdbr for more options.

    Let’s preview what’s in hs_gene_sets.

    head(hs_gene_sets)

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

    @@ -4059,9 +4057,9 @@

    4.4 Gene identifier conversion
    filtered_dge_mapped_df <- dge_mapped_df %>%
   # First let's create a column to hold the rankings of our absolute log2 fold
   # change values -- this handles duplicate log2 fold change values
-  dplyr::mutate(rank = rank(abs(log2FoldChange), ties.method = "random")) %>%
+  # dplyr::mutate(rank = rank(abs(log2FoldChange), ties.method = "random")) %>%
   # Sort so that the highest ranked absolute values of the log2 fold change are at the top
-  dplyr::arrange(dplyr::desc(rank)) %>%
+  dplyr::arrange(dplyr::desc(abs(log2FoldChange))) %>%
   # Filter out the duplicated rows using `dplyr::distinct()`
   dplyr::distinct(gene_symbol, .keep_all = TRUE)

    Note that the log2 fold change values we use here for ranking have been transformed by DESeq2 to account for genes with low counts or highly variable counts. See the DESeq2 package vignette for more information on how DESeq2 handles the log2 fold change values with the lfcShrink() function.

    @@ -4078,7 +4076,7 @@

    4.5.1 Determine our pre-ranked ge

    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 rankings of the gene-level log2 fold change values.

    # Let's create a named vector of the rankings based on the log2 fold change values
-ranked_vector <- filtered_dge_mapped_df$rank
+ranked_vector <- filtered_dge_mapped_df$log2FoldChange
 names(ranked_vector) <- filtered_dge_mapped_df$gene_symbol
 
 # We need to sort the ranked vector in descending order here
@@ -4086,44 +4084,47 @@ 
    4.5.1 Determine our pre-ranked ge
 
    Let’s preview our pre-ranked named vector.
    
 
    # Look at first entries of the ranked vector
 head(ranked_vector)
    
-
    ##      CLDN3 NUTM2B-AS1  MIR3648-2       UTS2       ALPL   ANKRD34A 
-##      22643      22642      22641      22640      22639      22638
    
+
    ##     UTS2     ALPL   GRIN2B      ADM    KRT23    IL1R2 
+## 6.174579 5.824919 4.172007 3.952953 3.731020 3.717043

    4.5.2 Run GSEA using the GSEA() function

    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 = ranked_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
-  scoreType = "pos",
-  TERM2GENE = dplyr::select(
-    hs_gene_sets,
-    gs_name,
-    gene_symbol
-  )
-)
    +
    gene_terms <- hs_gene_sets %>%
+  dplyr::select(
+    gs_name,
+    gene_symbol
+  )
+
+gsea_results <- GSEA(
+  geneList = ranked_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 = gene_terms
+)
    ## preparing geneSet collections...
    ## GSEA analysis...
    +
    ## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
+## The order of those tied genes will be arbitrary, which may produce unexpected results.
    ## leading edge analysis...
    ## done...

    Let’s take a look at the results.

    -
    # We can access the results from our `gsea_results` object using `@result`
-head(gsea_results@result)
    +
    # We can access the results from our `gsea_results` object using `@result`
+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, according to the p.adjust column 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)

    @@ -4132,71 +4133,123 @@

    4.6 Visualizing results

    4.6.1 Most Positive NES

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

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

    The gene set KEGG_SELENOAMINO_ACID_METABOLISM has the most positive NES score.

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

    +

    The gene set HP_VERTEBRAL_CLEFTING has the most positive NES score.

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

    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 log2 fold change 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, "SRP078441_gsea_enrich_positive_plot.png"),
-  plot = most_positive_nes_plot
-)
    +
    ggplot2::ggsave(file.path(plots_dir, "SRP078441_gsea_enrich_positive_plot.png"),
+  plot = most_positive_nes_plot
+)
    ## Saving 7 x 5 in image

    4.6.2 Most Negative NES

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

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

    The gene set KEGG_TIGHT_JUNCTION has the most negative NES.

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

    +

    The gene set GO_LATERAL_PLASMA_MEMBRANE has the most negative NES.

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

    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 log2 fold change 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, "SRP078441_gsea_enrich_negative_plot.png"),
-  plot = most_negative_nes_plot
-)
    +
    ggplot2::ggsave(file.path(plots_dir, "SRP078441_gsea_enrich_negative_plot.png"),
+  plot = most_negative_nes_plot
+)
    ## Saving 7 x 5 in image
    +
    +

    4.7 Plot other pathways

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

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

    +
    benporath_genes <- hs_gene_sets %>%
+  dplyr::filter(gs_name == "BENPORATH_NOS_TARGETS")
+
+sum(filtered_dge_mapped_df$gene_symbol %in% benporath_genes$gene_symbol)
    +
    ## [1] 177
    +
    length(benporath_genes$gene_symbol)
    +
    ## [1] 179
    +
    enrichplot::gseaplot(
+  gsea_results,
+  geneSetID = "GO_REGULATION_OF_BINDING",
+  title = "GO_REGULATION_OF_BINDING",
+  color.line = "#0d76ff"
+)
    +

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

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

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

    +
    -

    4.7 Write results to file

    -
    readr::write_tsv(
-  gsea_result_df,
-  file.path(
-    results_dir,
-    "SRP078441_gsea_results.tsv"
-  )
-)
    +

    4.8 Write results to file

    +
    readr::write_tsv(
+  gsea_result_df,
+  file.path(
+    results_dir,
+    "SRP078441_gsea_results.tsv"
+  )
+)
    @@ -4211,8 +4264,8 @@

    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 ecbabf086e0c44c4b1d66241494e3af3e6e1aba5 Mon Sep 17 00:00:00 2001
From: Chante Bethell 
Date: Mon, 7 Dec 2020 12:57:43 -0500
Subject: [PATCH 7/7] swap datasets and re work notebook

---
 03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd | 119 ++++---------
 .../pathway-analysis_rnaseq_02_gsea.html      | 168 ++++++------------
 2 files changed, 89 insertions(+), 198 deletions(-)

diff --git a/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd
index 0d5decc9..dffe1801 100644
--- a/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd
+++ b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.Rmd
@@ -1,7 +1,7 @@
 ---
 title: "Gene set enrichment analysis - RNA-seq"
 author: "CCDL for ALSF"
-date: "November 2020"
+date: "December 2020"
 output:  
   html_notebook:
     toc: true
@@ -95,9 +95,9 @@ We have provided this file for you and the code in this notebook will read in th
 
 ## About the dataset we are using for this example
 
-For this example analysis, we will use this [acute myeloid leukemia (AML) dataset](https://www.refine.bio/experiments/SRP078441/rna-seq-of-primary-patient-aml-samples) [@Micol2017].
-
-@Micol2017 performed RNA-seq on primary peripheral blood and bone marrow samples from AML patients with and without _ASXL1/2_ mutations. 
+For this example analysis, we are using RNA-seq data from an [acute lymphoblastic leukemia (ALL) mouse lymphoid cell model](https://www.refine.bio/experiments/SRP123625) [@Kampen2019].
+All lymphoid mouse cells have human RPL10 but three of the mice have a knock-in R98S mutated RPL10 and three have the human wildtype RPL10. 
+Differential expression was performed using these knock-in and wildtype mice designations.
 
 ## Check out our file structure!
 
@@ -130,7 +130,7 @@ See our Getting Started page with [instructions for package installation](https:
 
 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.Hs.eg.db`](http://bioconductor.org/packages/release/data/annotation/html/org.Hs.eg.db.html) package to perform gene identifier conversion [@Carlson2020-human].
+We will also need the [`org.Mm.eg.db`](http://bioconductor.org/packages/release/data/annotation/html/org.Mm.eg.db.html) package to perform gene identifier conversion [@Carlson2020-human].
 
 ```{r}
 if (!("clusterProfiler" %in% installed.packages())) {
@@ -143,9 +143,9 @@ if (!("msigdbr" %in% installed.packages())) {
   BiocManager::install("msigdbr", update = FALSE)
 }
 
-if (!("org.Hs.eg.db" %in% installed.packages())) {
+if (!("org.Mm.eg.db" %in% installed.packages())) {
   # Install this package if it isn't installed yet
-  BiocManager::install("org.Hs.eg.db", update = FALSE)
+  BiocManager::install("org.Mm.eg.db", update = FALSE)
 }
 ```
 
@@ -159,7 +159,7 @@ library(clusterProfiler)
 library(msigdbr)
 
 # Human annotation package we'll use for gene identifier conversion
-library(org.Hs.eg.db)
+library(org.Mm.eg.db)
 
 # We will need this so we can use the pipe: %>%
 library(magrittr)
@@ -184,11 +184,11 @@ 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/03-rnaseq/results/SRP078441/SRP078441_differential_expression_results.tsv"
+dge_url <- "https://refinebio-examples.s3.us-east-2.amazonaws.com/03-rnaseq/results/SRP123625/SRP123625_differential_expression_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", "SRP078441_differential_expression_results.tsv")`.
+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", "SRP123625_differential_expression_results.tsv")`.
 
 ```{r}
 # Read in the contents of your differential expression results file
@@ -226,8 +226,9 @@ MSigDB contains 8 different gene set collections [@Subramanian2005].
 The data we're interested in here comes from human samples, so we can obtain just the gene sets (from all of the gene set collections listed above) relevant to _Homo sapiens_ by specifying `species = "Homo sapiens"`.
 
 ```{r}
-hs_gene_sets <- msigdbr(
-  species = "Homo sapiens", # Replace with species name relevant to your data
+mm_gene_sets <- msigdbr(
+  species = "Mus musculus", # Replace with species name relevant to your data
+  category = "H"
 )
 ```
 
@@ -237,7 +238,7 @@ See `?msigdbr` for more options.
 Let's preview what's in `hs_gene_sets`.
 
 ```{r}
-head(hs_gene_sets)
+head(mm_gene_sets)
 ```
 
 Looks like we have a data frame of gene sets with associated gene symbols and Entrez IDs.
@@ -255,7 +256,7 @@ The annotation package `org.Hs.eg.db` contains information for different identif
 We can see what types of IDs are available to us in an annotation package with `keytypes()`.
 
 ```{r}
-keytypes(org.Hs.eg.db)
+keytypes(org.Mm.eg.db)
 ```
 
 Even though we'll use this package to convert from Ensembl gene IDs (`ENSEMBL`) to gene symbols (`SYMBOL`), we could just as easily use it to convert to and from any of these `keytypes()` listed above.
@@ -268,7 +269,7 @@ Let's create a data frame that shows the mapped gene symbols along with the diff
 # First let's create a mapped data frame we can join to the differential expression stats
 dge_mapped_df <- data.frame(
   "gene_symbol" = mapIds(
-    org.Hs.eg.db, # Replace with annotation package for the organism relevant to your data
+    org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data
     keys = dge_df$Gene,
     column = "SYMBOL", # 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
@@ -393,7 +394,7 @@ In this section, we will implement GSEA to calculate the enrichment score (ES) f
 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}
-gene_terms <- hs_gene_sets %>%
+gene_terms <- mm_gene_sets %>%
   dplyr::select(
     gs_name,
     gene_symbol
@@ -407,7 +408,11 @@ gsea_results <- GSEA(
   eps = 0, # boundary for calculating the p value
   seed = TRUE, # set seed to make results reproducible
   pAdjustMethod = "BH", # Benjamini-Hochberg correction
-  TERM2GENE = gene_terms
+  TERM2GENE = dplyr::select(
+    mm_gene_sets,
+    gs_name,
+    gene_symbol
+  )
 )
 ```
 
@@ -446,13 +451,13 @@ gsea_result_df %>%
   dplyr::slice_max(NES, n = 3)
 ```
 
-The gene set `HP_VERTEBRAL_CLEFTING` has the most positive NES score.
+The gene set `HALLMARK_MYC_TARGETS_V2` has the most positive NES score.
 
 ```{r}
 most_positive_nes_plot <- enrichplot::gseaplot(
   gsea_results,
-  geneSetID = "HP_VERTEBRAL_CLEFTING",
-  title = "HP_VERTEBRAL_CLEFTING",
+  geneSetID = "HALLMARK_MYC_TARGETS_V2",
+  title = "HALLMARK_MYC_TARGETS_V2",
   color.line = "#0d76ff"
 )
 
@@ -468,7 +473,7 @@ The plots returned by `enrichplot::gseaplot` are ggplots, so we can use `ggplot2
 Let's save to PNG.
 
 ```{r}
-ggplot2::ggsave(file.path(plots_dir, "SRP078441_gsea_enrich_positive_plot.png"),
+ggplot2::ggsave(file.path(plots_dir, "SRP123625_gsea_enrich_positive_plot.png"),
   plot = most_positive_nes_plot
 )
 ```
@@ -484,13 +489,13 @@ gsea_result_df %>%
   dplyr::slice_min(NES, n = 3)
 ```
 
-The gene set `GO_LATERAL_PLASMA_MEMBRANE` has the most negative NES.
+The gene set `HALLMARK_HYPOXIA` has the most negative NES.
 
 ```{r}
 most_negative_nes_plot <- enrichplot::gseaplot(
   gsea_results,
-  geneSetID = "GO_LATERAL_PLASMA_MEMBRANE",
-  title = "GO_LATERAL_PLASMA_MEMBRANE",
+  geneSetID = "HALLMARK_HYPOXIA",
+  title = "HALLMARK_HYPOXIA",
   color.line = "#0d76ff"
 )
 
@@ -505,73 +510,11 @@ 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, "SRP078441_gsea_enrich_negative_plot.png"),
+ggplot2::ggsave(file.path(plots_dir, "SRP123625_gsea_enrich_negative_plot.png"),
   plot = most_negative_nes_plot
 )
 ```
 
-## Plot other pathways
-
-```{r}
-enrichplot::gseaplot(
-  gsea_results,
-  geneSetID = "HP_DECREASED_CIRCULATING_CORTISOL_LEVEL",
-  title = "HP_DECREASED_CIRCULATING_CORTISOL_LEVEL",
-  color.line = "#0d76ff"
-)
-```
-
-```{r}
-enrichplot::gseaplot(
-  gsea_results,
-  geneSetID = "BENPORATH_NOS_TARGETS",
-  title = "BENPORATH_NOS_TARGETS",
-  color.line = "#0d76ff"
-)
-
-benporath_genes <- hs_gene_sets %>%
-  dplyr::filter(gs_name == "BENPORATH_NOS_TARGETS")
-
-sum(filtered_dge_mapped_df$gene_symbol %in% benporath_genes$gene_symbol)
-length(benporath_genes$gene_symbol)
-```
-
-```{r}
-enrichplot::gseaplot(
-  gsea_results,
-  geneSetID = "GO_REGULATION_OF_BINDING",
-  title = "GO_REGULATION_OF_BINDING",
-  color.line = "#0d76ff"
-)
-```
-
-```{r}
-enrichplot::gseaplot(
-  gsea_results,
-  geneSetID = "KEGG_APOPTOSIS",
-  title = "KEGG_APOPTOSIS",
-  color.line = "#0d76ff"
-)
-```
-
-```{r}
-enrichplot::gseaplot(
-  gsea_results,
-  geneSetID = "KEGG_AMINOACYL_TRNA_BIOSYNTHESIS",
-  title = "KEGG_AMINOACYL_TRNA_BIOSYNTHESIS",
-  color.line = "#0d76ff"
-)
-```
-
-```{r}
-enrichplot::gseaplot(
-  gsea_results,
-  geneSetID = "KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY",
-  title = "KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY",
-  color.line = "#0d76ff"
-)
-```
-
 ## Write results to file
 
 ```{r}
@@ -579,7 +522,7 @@ readr::write_tsv(
   gsea_result_df,
   file.path(
     results_dir,
-    "SRP078441_gsea_results.tsv"
+    "SRP123625_gsea_results.tsv"
   )
 )
 ```
diff --git a/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html
index 44176e02..af6a55e3 100644
--- a/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html
+++ b/03-rnaseq/pathway-analysis_rnaseq_02_gsea.html
@@ -3765,7 +3765,7 @@
 
 
    Gene set enrichment analysis - RNA-seq
    
 
    CCDL for ALSF
    
-
    November 2020
    
+
    December 2020
    @@ -3868,8 +3868,7 @@

    2.3 Obtain the gene set for this

    2.4 About the dataset we are using for this example

    -

    For this example analysis, we will use this acute myeloid leukemia (AML) dataset (Micol et al. 2017).

    -

    Micol et al. (2017) performed RNA-seq on primary peripheral blood and bone marrow samples from AML patients with and without ASXL1/2 mutations.

    +

    For this example analysis, we are using RNA-seq data from an acute lymphoblastic leukemia (ALL) mouse lymphoid cell model (???). All lymphoid mouse cells have human RPL10 but three of the mice have a knock-in R98S mutated RPL10 and three have the human wildtype RPL10. Differential expression was performed using these knock-in and wildtype mice designations.

    2.5 Check out our file structure!

    @@ -3899,7 +3898,7 @@

    4 Gene set enrichment analysis -

    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.Hs.eg.db package to perform gene identifier conversion (Carlson 2020).

    +

    We will also need the org.Mm.eg.db package to perform gene identifier conversion (Carlson 2020).

    if (!("clusterProfiler" %in% installed.packages())) {
   # Install this package if it isn't installed yet
   BiocManager::install("clusterProfiler", update = FALSE)
@@ -3910,9 +3909,9 @@ 
    4.1 Install libraries
    
   BiocManager::install("msigdbr", update = FALSE)
 }
 
-if (!("org.Hs.eg.db" %in% installed.packages())) {
+if (!("org.Mm.eg.db" %in% installed.packages())) {
   # Install this package if it isn't installed yet
-  BiocManager::install("org.Hs.eg.db", update = FALSE)
+  BiocManager::install("org.Mm.eg.db", update = FALSE)
 }

    Attach the packages we need for this analysis.

    # Attach the library
@@ -3922,7 +3921,7 @@ 
    4.1 Install libraries
    
 library(msigdbr)
 
 # Human annotation package we'll use for gene identifier conversion
-library(org.Hs.eg.db)
+library(org.Mm.eg.db)
 
 # We will need this so we can use the pipe: %>%
 library(magrittr)
    @@ -3935,14 +3934,14 @@

    4.2 Import data

    We will read in the differential expression results we will download from online. These results are from an acute myeloid leukemia (AML) RNA-seq dataset we used for differential expression analysis using DESeq2 (Love et al. 2014). The table contains summary statistics including Ensembl gene IDs, log2 fold change 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/03-rnaseq/results/SRP078441/SRP078441_differential_expression_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", "SRP078441_differential_expression_results.tsv").

    +dge_url <- "https://refinebio-examples.s3.us-east-2.amazonaws.com/03-rnaseq/results/SRP123625/SRP123625_differential_expression_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", "SRP123625_differential_expression_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 ──────────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────────────────
 ## cols(
 ##   Gene = col_character(),
 ##   baseMean = col_double(),
@@ -3956,7 +3955,7 @@ 
    4.2 Import data
    
 
    dge_df
    
 
    
 
 
    
 
@@ -3979,15 +3978,16 @@ 
    4.3 Getting familiar with c
 C6: oncogenic signatures
 C7: immunologic signatures

    The data we’re interested in here comes from human samples, so we can obtain just the gene sets (from all of the gene set collections listed above) relevant to Homo sapiens by specifying species = "Homo sapiens".

    -
    hs_gene_sets <- msigdbr(
-  species = "Homo sapiens", # Replace with species name relevant to your data
-)
    +
    mm_gene_sets <- msigdbr(
+  species = "Mus musculus", # Replace with species name relevant to your data
+  category = "H"
+)

    If you run the chunk above with specifying category = "H" to the msigdbr() function, it will return only the Hallmark gene sets for Homo sapiens. See ?msigdbr for more options.

    Let’s preview what’s in hs_gene_sets.

    -
    head(hs_gene_sets)
    +
    head(mm_gene_sets)

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

    @@ -3998,20 +3998,19 @@

    4.4 Gene identifier conversionWe’re going to convert our identifiers in dge_df to gene symbols, 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.Hs.eg.db contains information for different identifiers (Carlson 2020). org.Hs.eg.db is specific to Homo sapiens – this is what the Hs 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.Hs.eg.db)
    +
    keytypes(org.Mm.eg.db)
    ##  [1] "ACCNUM"       "ALIAS"        "ENSEMBL"      "ENSEMBLPROT"  "ENSEMBLTRANS"
 ##  [6] "ENTREZID"     "ENZYME"       "EVIDENCE"     "EVIDENCEALL"  "GENENAME"    
-## [11] "GO"           "GOALL"        "IPI"          "MAP"          "OMIM"        
-## [16] "ONTOLOGY"     "ONTOLOGYALL"  "PATH"         "PFAM"         "PMID"        
-## [21] "PROSITE"      "REFSEQ"       "SYMBOL"       "UCSCKG"       "UNIGENE"     
-## [26] "UNIPROT"
    +## [11] "GO" "GOALL" "IPI" "MGI" "ONTOLOGY" +## [16] "ONTOLOGYALL" "PATH" "PFAM" "PMID" "PROSITE" +## [21] "REFSEQ" "SYMBOL" "UNIGENE" "UNIPROT"

    Even though we’ll use this package to convert from Ensembl gene IDs (ENSEMBL) to gene symbols (SYMBOL), we could just as easily use it to convert to and from any of these keytypes() listed above.

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

    Let’s create a data frame that shows the mapped gene symbols 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(
   "gene_symbol" = mapIds(
-    org.Hs.eg.db, # Replace with annotation package for the organism relevant to your data
+    org.Mm.eg.db, # Replace with annotation package for the organism relevant to your data
     keys = dge_df$Gene,
     column = "SYMBOL", # 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
@@ -4031,7 +4030,7 @@ 
    4.4 Gene identifier conversion
    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.

    @@ -4048,7 +4047,7 @@

    4.4 Gene identifier conversion dplyr::arrange(gene_symbol)

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

    @@ -4084,14 +4083,14 @@

    4.5.1 Determine our pre-ranked ge

    Let’s preview our pre-ranked named vector.

    # Look at first entries of the ranked vector
 head(ranked_vector)
    -
    ##     UTS2     ALPL   GRIN2B      ADM    KRT23    IL1R2 
-## 6.174579 5.824919 4.172007 3.952953 3.731020 3.717043
    +
    ##   Lpgat1   Lgals7    Gm973     Bbs7     Clnk   Zfp575 
+## 13.34941 12.64196 12.51824 12.19278 11.52481 10.20900

    4.5.2 Run GSEA using the GSEA() function

    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()).

    -
    gene_terms <- hs_gene_sets %>%
+
    gene_terms <- mm_gene_sets %>%
   dplyr::select(
     gs_name,
     gene_symbol
@@ -4105,11 +4104,15 @@ 
    4.5.2 Run GSEA using the GS
   eps = 0, # boundary for calculating the p value
   seed = TRUE, # set seed to make results reproducible
   pAdjustMethod = "BH", # Benjamini-Hochberg correction
-  TERM2GENE = gene_terms
-)
    
+  TERM2GENE = dplyr::select(
+    mm_gene_sets,
+    gs_name,
+    gene_symbol
+  )
+)
    ## preparing geneSet collections...
    ## GSEA analysis...
    -
    ## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
+
    ## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.19% of the list).
 ## The order of those tied genes will be arbitrary, which may produce unexpected results.
    
 
    ## leading edge analysis...
    
 
    ## done...
    
@@ -4118,7 +4121,7 @@ 
    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.

    @@ -4138,23 +4141,23 @@

    4.6.1 Most Positive NES

    dplyr::slice_max(NES, n = 3)
    -

    The gene set HP_VERTEBRAL_CLEFTING has the most positive NES score.

    +

    The gene set HALLMARK_MYC_TARGETS_V2 has the most positive NES score.

    most_positive_nes_plot <- enrichplot::gseaplot(
   gsea_results,
-  geneSetID = "HP_VERTEBRAL_CLEFTING",
-  title = "HP_VERTEBRAL_CLEFTING",
+  geneSetID = "HALLMARK_MYC_TARGETS_V2",
+  title = "HALLMARK_MYC_TARGETS_V2",
   color.line = "#0d76ff"
 )
 
 most_positive_nes_plot
    -

    +

    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 log2 fold change 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, "SRP078441_gsea_enrich_positive_plot.png"),
+
    ggplot2::ggsave(file.path(plots_dir, "SRP123625_gsea_enrich_positive_plot.png"),
   plot = most_positive_nes_plot
 )
    
 
    ## Saving 7 x 5 in image
    
@@ -4168,88 +4171,36 @@ 
    4.6.2 Most Negative NES
    
   dplyr::slice_min(NES, n = 3)
    -

    The gene set GO_LATERAL_PLASMA_MEMBRANE has the most negative NES.

    +

    The gene set HALLMARK_HYPOXIA has the most negative NES.

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

    +

    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 log2 fold change 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, "SRP078441_gsea_enrich_negative_plot.png"),
+
    ggplot2::ggsave(file.path(plots_dir, "SRP123625_gsea_enrich_negative_plot.png"),
   plot = most_negative_nes_plot
 )
    
 
    ## Saving 7 x 5 in image
    -
    -

    4.7 Plot other pathways

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

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

    -
    benporath_genes <- hs_gene_sets %>%
-  dplyr::filter(gs_name == "BENPORATH_NOS_TARGETS")
-
-sum(filtered_dge_mapped_df$gene_symbol %in% benporath_genes$gene_symbol)
    -
    ## [1] 177
    -
    length(benporath_genes$gene_symbol)
    -
    ## [1] 179
    -
    enrichplot::gseaplot(
-  gsea_results,
-  geneSetID = "GO_REGULATION_OF_BINDING",
-  title = "GO_REGULATION_OF_BINDING",
-  color.line = "#0d76ff"
-)
    -

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

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

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

    -
    -

    4.8 Write results to file

    -
    readr::write_tsv(
-  gsea_result_df,
-  file.path(
-    results_dir,
-    "SRP078441_gsea_results.tsv"
-  )
-)
    +

    4.7 Write results to file

    +
    readr::write_tsv(
+  gsea_result_df,
+  file.path(
+    results_dir,
+    "SRP123625_gsea_results.tsv"
+  )
+)
    @@ -4264,8 +4215,8 @@

    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)
@@ -4276,7 +4227,7 @@ 
    6 Session info
    
 ##  collate  en_US.UTF-8                 
 ##  ctype    en_US.UTF-8                 
 ##  tz       Etc/UTC                     
-##  date     2020-12-04                  
+##  date     2020-12-07                  
 ## 
 ## ─ Packages ───────────────────────────────────────────────────────────────────
 ##  package         * version  date       lib source        
@@ -4338,7 +4289,7 @@ 
    6 Session info
    
 ##  msigdbr         * 7.2.1    2020-10-02 [1] RSPM (R 4.0.2)
 ##  munsell           0.5.0    2018-06-12 [1] RSPM (R 4.0.0)
 ##  optparse        * 1.6.6    2020-04-16 [1] RSPM (R 4.0.0)
-##  org.Hs.eg.db    * 3.12.0   2020-12-02 [1] Bioconductor  
+##  org.Mm.eg.db    * 3.12.0   2020-12-02 [1] Bioconductor  
 ##  pillar            1.4.6    2020-07-10 [1] RSPM (R 4.0.2)
 ##  pkgconfig         2.0.3    2019-09-22 [1] RSPM (R 4.0.0)
 ##  plyr              1.8.6    2020-03-03 [1] RSPM (R 4.0.2)
@@ -4405,9 +4356,6 @@ 
    References
    
 
    
 
    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
    
 
    
-
    
-
    Micol J. B., A. Pastore, D. Inoue, N. Duployez, and E. Kim et al., 2017 ASXL2 is essential for haematopoiesis and acts as a haploinsufficient tumour suppressor in leukemia. Nature Communications 8: 15429. https://doi.org/10.1038/ncomms15429
    
-
    
 
    
 
    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