From a1b2151d65513edc9a163e69154da0fbb6285ec1 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Mon, 1 Jun 2020 12:23:37 +0300 Subject: [PATCH 01/54] feat(upkeep): improve the contribution guidelines & version bump --- CONTRIBUTION.md | 46 +++++++++++++++++++++++++++++++++++++++++----- DESCRIPTION | 2 +- 2 files changed, 42 insertions(+), 6 deletions(-) diff --git a/CONTRIBUTION.md b/CONTRIBUTION.md index 8011cdf2..c70419d1 100644 --- a/CONTRIBUTION.md +++ b/CONTRIBUTION.md @@ -1,11 +1,11 @@ # Contributing to immunarch -This outlines how to propose a change to immunarch. +This document outlines how to propose a change to immunarch. ### Fixing typos Small typos or grammatical errors in documentation may be edited directly using -the GitLab web IDE functionality / GitHub web interface, so long as the changes are made in the _source_ file. +the GitLab web IDE functionality / GitHub web interface, so long as the changes are made in the _source_ file. Please make sure to create a Pull Request instead of commiting directly to the `master` branch. * YES: you edit a roxygen comment in a `.R` file below `R/`. * NO: you edit an `.Rd` file below `man/`. @@ -18,15 +18,51 @@ bug, create an associated issue and illustrate the bug with a minimal [reprex](https://www.tidyverse.org/help/#reprex). If you need to send a data and can't do that via GitHub, please note it in the issue. +### Version naming + +We employ Major.Minor.Patch. E.g., 0.4.0. + +Hotfixes: Major.Minor.Patch → Major.Minor.(Patch + 1) + +Guidelines: [http://r-pkgs.had.co.nz/release.html](http://r-pkgs.had.co.nz/release.html) + +### Commit naming + +We follow the [following guidelines for commit naming](https://medium.com/@kevinkreuzer/the-way-to-fully-automated-releases-in-open-source-projects-44c015f38fd6). + +#### Commit types + +There are eight types of commits: chore, docs, feat, fix, refactor, test, perf, style. + +#### Commit scopes + +Chanes in analysis and visualisation functions: diversity, overlap, public-repertoire, clonality, gene-usage, explore, kmers, spectratype, dynamics, tools + +Changes in parsing: io + +Changes in databases support: db + +Changes in additional functions such as general statistics functions: utility + +Changes in NAMESPACE, DESCRIPTION, citations, ISSUE_TEMPLATE.md, etc., without README: upkeep + +Changes in README and vignettes: vignette + +Changes in Continuous Integration: ci + +Changes in Shiny applications: shiny + +### How to create an Issue + +We have a rich list of templates for Issues [here](https://github.com/immunomind/immunarch/tree/master/.github/ISSUE_TEMPLATE). + ### Pull request process * We recommend that you create a Git branch for each pull request (PR). * Look at the CI (Continuous Integration) build status before and after making changes. The `README` should contain badges for any continuous integration services used by the package. -* New code should follow our style guide that is the tidyverse [style guide](http://style.tidyverse.org) -with a very minor addition of using `=` instead of `<-` in variable assignment. -You can use the [styler](https://CRAN.R-project.org/package=styler) package to +* New code should follow our style guide that is the tidyverse [style guide](http://style.tidyverse.org) You can use the [styler](https://CRAN.R-project.org/package=styler) package to apply these styles, but please don't restyle code that has nothing to do with your PR. * We use [roxygen2](https://cran.r-project.org/package=roxygen2) for documentation. diff --git a/DESCRIPTION b/DESCRIPTION index a7af1aaa..71899df3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: immunarch Type: Package Title: Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires -Version: 0.6.4 +Version: 0.6.5 Authors@R: c( person("Vadim I.", "Nazarov", , "vdm.nazarov@gmail.com", c("aut", "cre")), person("Vasily O.", "Tsvetkov", , role = "aut"), From 86a5d5498534fe7f8c64d6ff0b99ef24c239beff Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Mon, 1 Jun 2020 12:24:42 +0300 Subject: [PATCH 02/54] refactor(io): refactor warning messages to make them more correct & add "na.rm = TRUE" to eliminates NAs when computing new Proportion columns --- R/io.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/io.R b/R/io.R index be7835af..171cf6a2 100644 --- a/R/io.R +++ b/R/io.R @@ -1320,14 +1320,14 @@ parse_mixcr <- function(.filename) { warn_msg <- c(" [!] Warning: can't find a column with clonal counts. Setting all clonal counts to 1.") warn_msg <- c(warn_msg, "\n Did you apply repLoad to MiXCR file *_alignments.txt?") warn_msg <- c(warn_msg, " If so please consider moving all *.clonotypes.*.txt MiXCR files to") - warn_msg <- c(warn_msg, " a separate folder and applying repLoad to it.") + warn_msg <- c(warn_msg, " a separate folder and apply repLoad to the folder.") warn_msg <- c(warn_msg, "\n Note: The *_alignments.txt file IS NOT a repertoire file suitable for any analysis.") message(warn_msg) df[[.count]] <- 1 } .freq <- "Proportion" - df$Proportion <- df[[.count]] / sum(df[[.count]]) + df$Proportion <- df[[.count]] / sum(df[[.count]], na.rm = TRUE) .aa.seq <- IMMCOL$cdr3aa df[[.aa.seq]] <- bunch_translate(df[[.nuc.seq]]) From 75a8cb7471c3c7b3b001e9cb0a3a34544306a8b2 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 4 Jun 2020 03:03:31 +0300 Subject: [PATCH 03/54] feat(upkeep): add the tag for the website --- CONTRIBUTION.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CONTRIBUTION.md b/CONTRIBUTION.md index c70419d1..83df4ac8 100644 --- a/CONTRIBUTION.md +++ b/CONTRIBUTION.md @@ -52,6 +52,8 @@ Changes in Continuous Integration: ci Changes in Shiny applications: shiny +Changes in the website: web + ### How to create an Issue We have a rich list of templates for Issues [here](https://github.com/immunomind/immunarch/tree/master/.github/ISSUE_TEMPLATE). From 27bef117f15c5842136b3870a093ba6630bf3e0c Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 4 Jun 2020 03:03:58 +0300 Subject: [PATCH 04/54] refactor(upkeep): minor changes in text --- DESCRIPTION | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 71899df3..bc9382a4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -12,7 +12,7 @@ Authors@R: c( person("ImmunoMind", role = c("cph", "fnd")) ) Contact: support@immunomind.io -Description: A comprehensive framework for bioinformatics analysis of bulk and single-cell +Description: A comprehensive framework for bioinformatics exploratory analysis of bulk and single-cell T-cell receptor and antibody repertoires. It provides seamless data loading, analysis and visualisation for AIRR (Adaptive Immune Receptor Repertoire) data, both bulk immunosequencing (RepSeq) and single-cell sequencing (scRNAseq). It implements most of the widely used AIRR analysis methods, @@ -24,11 +24,13 @@ License: AGPL-3 URL: https://immunarch.com/, https://github.com/immunomind/immunarch BugReports: https://github.com/immunomind/immunarch/issues Imports: + factoextra (>= 1.0.4), + fpc, + treemap, + UpSetR (>= 1.4.0), pheatmap (>= 1.0.12), ggrepel (>= 0.8.0), reshape2 (>= 1.4.2), - factoextra (>= 1.0.4), - fpc, circlize, MASS (>= 7.3), Rtsne (>= 0.15), @@ -36,10 +38,8 @@ Imports: readxl (>= 1.3.1), shiny (>= 1.4.0), shinythemes, - treemap, airr, ggseqlogo, - UpSetR (>= 1.4.0), stringr (>= 1.4.0), ggalluvial (>= 0.10.0), Rcpp (>= 1.0), From d5506644638358aacaa7de23204c99cde124c11b Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 4 Jun 2020 03:04:58 +0300 Subject: [PATCH 05/54] feat(vignette): make the README more succinct, add a quick start section and the visitors badge --- CONTRIBUTION.md | 2 -- README.md | 25 ++++++++++++++++--------- 2 files changed, 16 insertions(+), 11 deletions(-) diff --git a/CONTRIBUTION.md b/CONTRIBUTION.md index 83df4ac8..c70419d1 100644 --- a/CONTRIBUTION.md +++ b/CONTRIBUTION.md @@ -52,8 +52,6 @@ Changes in Continuous Integration: ci Changes in Shiny applications: shiny -Changes in the website: web - ### How to create an Issue We have a rich list of templates for Issues [here](https://github.com/immunomind/immunarch/tree/master/.github/ISSUE_TEMPLATE). diff --git a/README.md b/README.md index 4a3ca8fd..2509fbfd 100644 --- a/README.md +++ b/README.md @@ -5,26 +5,35 @@ [![Issues](https://img.shields.io/github/issues/immunomind/immunarch?style=flat-square)](http://github.com/immunomind/immunarch/issues) [![CI](https://gitlab.com/immunomind/immunarch/badges/master/pipeline.svg?style=flat-square)](https://gitlab.com/immunomind/immunarch/-/jobs) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3367200.svg)](https://doi.org/10.5281/zenodo.3367200) +![Visitors](https://visitor-badge.glitch.me/badge?page_id=immunomind.immunarch) -# `immunarch` --- An R Package for Painless Bioinformatics Analysis of T-cell and B-cell Immune Repertoire Data - +# `immunarch` --- Bioinformatics R package for Seamless Exploration of Single-cell and Bulk T-cell/Antibody Immune Repertoires ## Why `immunarch`? - **Work with any type of data:** single-cell, bulk, data tables, databases --- you name it. -- **Community at the heart:** thrive in the community of almost 30,000 researchers and medical scientists worldwide, including researchers from **Pfizer, Novartis, Regeneron, Stanford, UCSF** and **MIT**. +- **Community at the heart:** ask questions, share knowledge and thrive in the community of almost 30,000 researchers and medical scientists worldwide. **Pfizer, Novartis, Regeneron, Stanford, UCSF** and **MIT** trust us. - **One plot --- one line:** write a [whole PhD thesis in 8 lines of code](https://twitter.com/Nusob88/status/1127601201112129536) or reproduce almost any publication in 5-10 lines of `immunarch` code. - **Be on the bleeding edge of science:** we regularly update `immunarch` with the latest methods. [Let us know what you need!](#help-the-community) - **Automatic format detection and parsing** for all popular immunosequencing formats: from **MiXCR** and **ImmunoSEQ** to **10XGenomics** and **ArcherDX**. -And the **most important**: `immunarch` is not just a tool --- it is an **ecosystem**. + +## Lightning-fast Start +```r +install.packages("immunarch") # Install the package +library(immunarch) # Load the package into R +data(immdata) # Load the test dataset +repOverlap(immdata$data) %>% vis() # Compute and visualise the most important statistics +geneUsage(immdata$data[[1]]) %>% vis() +repDiversity(immdata$data) %>% vis(.by = "Status", .meta = immdata$meta) # Group samples +``` ## From Berkeley with devotion -`immunarch` is brought to you by [ImmunoMind](https://immunomind.io) --- a [UC Berkeley SkyDeck](https://www.forbes.com/sites/avivalegatt/2019/01/07/launch-your-startup-at-these-five-college-incubators/) startup. +`immunarch` is brought to you by [ImmunoMind](https://immunomind.io) --- a [UC Berkeley SkyDeck](https://www.forbes.com/sites/avivalegatt/2019/01/07/launch-your-startup-at-these-five-college-incubators/) startup. ImmunoMind Data Science tools for single-cell and immunomics [exploration](https://immunarch.com) and [biomarker discovery](https://immunomind.io) are trusted by researchers from top pharma companies and universities, including 10X Genomics, Pfizer, Regeneron, UCSF, MIT, Stanford, John Hopkins School of Medicine and Vanderbilt University. -We have been helping researchers to extract insights from sequencing data of T-cell and antibody repertoires since the inception of the RepSeq domain. Our bioinformatics tools are trusted by top universities including Stanford, UCSF, MIT, King's College London and big pharma companies including Pfizer and Novartis. +[![Follow](https://img.shields.io/twitter/follow/immunomind.svg?style=social)](https://twitter.com/intent/follow?screen_name=immunomind) ## Stay connected! @@ -44,8 +53,6 @@ We have been helping researchers to extract insights from sequencing data of T-c -Feel free to follow us on [Twitter](https://twitter.com/immunomind) as well. - --- ## Table of Contents @@ -61,7 +68,7 @@ Feel free to follow us on [Twitter](https://twitter.com/immunomind) as well. ## Introduction -`immunarch` is an R package designed to analyse T-cell receptor (TCR) and B-cell receptor (BCR) repertoires, aimed at medical scientists and bioinformaticians. The mission of `immunarch` is to make immune sequencing data analysis as effortless as possible and help you focus on research instead of coding. Follow us on [Twitter](https://twitter.com/immunomind) for news and updates. +`immunarch` is an R package designed to analyse T-cell receptor (TCR) and B-cell receptor (BCR) repertoires, aimed at medical scientists and bioinformaticians. The mission of `immunarch` is to make immune sequencing data analysis as effortless as possible and help you focus on research instead of coding. ## Contact From d1962ca4d30175f418fe5db3871c419ec25f2a4d Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 4 Jun 2020 03:05:18 +0300 Subject: [PATCH 06/54] feat(upkeep): remove "Home" and "News", add a link to the covid-19 repo --- _pkgdown.yml | 19 +++++++------------ 1 file changed, 7 insertions(+), 12 deletions(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index a0b84894..2d1c1741 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -29,16 +29,14 @@ articles: navbar: structure: - left: - - intro - - articles - - reference - - news - right: - - im_link - - twitter - - github + left: [covid19, intro, articles, reference] + right: [im_link, twitter, github] components: + home: ~ + news: ~ + covid19: + text: "COVID-19" + href: https://github.com/immunomind/covid19 intro: text: "Get started" href: articles/v1_introduction.html @@ -81,9 +79,6 @@ navbar: href: articles/web_only/v8_tracking.html - text: Kmer and sequence motif analysis and visualisation href: articles/web_only/v9_kmers.html - news: - text: Changelog - href: https://github.com/immunomind/immunarch/releases twitter: icon: fa-lg fa-twitter href: http://twitter.com/immunomind From 3171d1edaf63a74713e72ce3da00f3d59a569175 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 4 Jun 2020 17:57:07 +0300 Subject: [PATCH 07/54] feat(upkeep): add the pkgdown folder to the ignore --- .Rbuildignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.Rbuildignore b/.Rbuildignore index dcd837c0..35b12209 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -13,6 +13,7 @@ ^.gitlab-ci.yml$ ^CONTRIBUTION.md$ ^_pkgdown.yml$ +^pkgdown$ ^code-of-conduct.md$ ^LICENSE$ ^immunarch-citation.xml$ From 15fba1376652396c8f306c7c7c78a7bc126906bc Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 4 Jun 2020 17:57:19 +0300 Subject: [PATCH 08/54] feat(vignette): cosmetic changes to the README --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 2509fbfd..b8f17613 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ ![Visitors](https://visitor-badge.glitch.me/badge?page_id=immunomind.immunarch) -# `immunarch` --- Bioinformatics R package for Seamless Exploration of Single-cell and Bulk T-cell/Antibody Immune Repertoires +# `immunarch` --- Fast and Seamless Exploration of Single-cell and Bulk T-cell/Antibody Immune Repertoires in R ## Why `immunarch`? - **Work with any type of data:** single-cell, bulk, data tables, databases --- you name it. @@ -18,18 +18,18 @@ - **Automatic format detection and parsing** for all popular immunosequencing formats: from **MiXCR** and **ImmunoSEQ** to **10XGenomics** and **ArcherDX**. -## Lightning-fast Start +### Lightning-fast Start ```r install.packages("immunarch") # Install the package -library(immunarch) # Load the package into R -data(immdata) # Load the test dataset -repOverlap(immdata$data) %>% vis() # Compute and visualise the most important statistics -geneUsage(immdata$data[[1]]) %>% vis() -repDiversity(immdata$data) %>% vis(.by = "Status", .meta = immdata$meta) # Group samples +library(immunarch) # Load the package into R +data(immdata) # Load the test dataset +repOverlap(immdata$data) %>% vis() # Compute and visualise the most important statistics: +geneUsage(immdata$data[[1]]) %>% vis() # public clonotypes, gene usage, sample diversity +repDiversity(immdata$data) %>% vis(.by = "Status", .meta = immdata$meta) # Group samples ``` -## From Berkeley with devotion +### From Berkeley with devotion `immunarch` is brought to you by [ImmunoMind](https://immunomind.io) --- a [UC Berkeley SkyDeck](https://www.forbes.com/sites/avivalegatt/2019/01/07/launch-your-startup-at-these-five-college-incubators/) startup. ImmunoMind Data Science tools for single-cell and immunomics [exploration](https://immunarch.com) and [biomarker discovery](https://immunomind.io) are trusted by researchers from top pharma companies and universities, including 10X Genomics, Pfizer, Regeneron, UCSF, MIT, Stanford, John Hopkins School of Medicine and Vanderbilt University. From da62a8f7835cb537562ebcc14508f8d35e4a0825 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 4 Jun 2020 17:57:59 +0300 Subject: [PATCH 09/54] refactor(tools): add_class() now adds the class to the beginning of the class attributes in order to comply with the R class system --- R/diversity.R | 2 +- R/tools.R | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/diversity.R b/R/diversity.R index 31b4837e..62f69d39 100644 --- a/R/diversity.R +++ b/R/diversity.R @@ -169,7 +169,7 @@ repDiversity <- function(.data, .method = "chao1", .col = "aa", .max.q = 6, .min .extrapolation = .extrapolation, .perc = .perc, .verbose = .verbose, .do.norm = .do.norm, .laplace = .laplace ) - new_class <- tail(class(res[[1]]), 1) + new_class <- head(class(res[[1]]), 1) res <- do.call(rbind, res) if (.method == "hill") { res <- reshape2::melt(res) diff --git a/R/tools.R b/R/tools.R index 32e9fe41..929ce86a 100644 --- a/R/tools.R +++ b/R/tools.R @@ -81,7 +81,7 @@ check_distribution <- function(.data, .do.norm = NA, .laplace = 1, .na.val = 0, #' tmp <- immunarch:::add_class(tmp, "new_class") #' class(tmp) add_class <- function(.obj, .class) { - class(.obj) <- c(class(.obj), .class) + class(.obj) <- c(.class, class(.obj)) .obj } From 18ec2d5ea0193eddf6e04103ea572697e05b2e15 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 4 Jun 2020 18:04:13 +0300 Subject: [PATCH 10/54] fix(vignette): replace "develop" with "dev" --- README.md | 2 +- vignettes/v1_introduction.Rmd | 93 +++++++++++++++-------------------- 2 files changed, 40 insertions(+), 55 deletions(-) diff --git a/README.md b/README.md index b8f17613..78bb8bf1 100644 --- a/README.md +++ b/README.md @@ -103,7 +103,7 @@ Since releasing on CRAN is limited to one release per one-two months, you can in ```r install.packages("devtools") # skip this if you already installed devtools -devtools::install_github("immunomind/immunarch", ref="develop") +devtools::install_github("immunomind/immunarch", ref="dev") ``` You can find the list of releases of `immunarch` here: https://github.com/immunomind/immunarch/releases diff --git a/vignettes/v1_introduction.Rmd b/vignettes/v1_introduction.Rmd index e54380cf..b3269cf3 100644 --- a/vignettes/v1_introduction.Rmd +++ b/vignettes/v1_introduction.Rmd @@ -27,90 +27,75 @@ output: ## Installation -In order to install `immunarch` execute the following R command: + +### Latest release on CRAN +In order to install `immunarch` execute the following command: ```r -install.packages("devtools") # skip this if you already installed devtools -devtools::install_url("https://github.com/immunomind/immunarch/raw/master/immunarch.tar.gz") +install.packages("immunarch") ``` -That's it, you can start using `immunarch` now! See the [Quick Start](#quick-start) section below to dive into immune repertoire data analysis. If you run in any trouble with installation, take a look at the [Installation Troubleshooting](#installation-troubleshooting) section below. +That's it, you can start using `immunarch` now! See the [Quick Start](#quick-start) section below to dive into immune repertoire data analysis. If you run in any trouble with installation, take a look at the [Installation Troubleshooting](https://immunarch.com/articles/v1_introduction.html#installation-troubleshooting) section. -Note that there are quite a lot of dependencies to install with the package because it installs all the widely-used packages for data analysis and visualisation. You got both the AIRR data analysis framework and Data Science package eco-system with only one command! +Note: there are quite a lot of dependencies to install with the package because it installs all the widely-used packages for data analysis and visualisation. You got both the AIRR data analysis framework and the full Data Science package ecosystem with only one command, making `immunarch` the entry-point for single-cell & immune repertoire Data Science. - -You can find the list of releases of `immunarch` here: https://github.com/immunomind/immunarch/releases -## Quick start -The gist of the typical TCR or BCR data analysis workflow can be reduced to the next few lines of code. +### Latest pre-release on GitHub +Since releasing on CRAN is limited to one release per one-two months, you can install the latest pre-release version with bleeding edge features and optimisations directly from the code repository. In order to install the latest pre-release version, you need to execute only two commands: -**1) Load the package and the data** +```r +install.packages("devtools") # skip this if you already installed devtools +devtools::install_github("immunomind/immunarch", ref="dev") +``` -```{r eval=FALSE} -# 1.1) Load the package into R: -library(immunarch) +You can find the list of releases of `immunarch` here: https://github.com/immunomind/immunarch/releases -# 1.2a) To quickly test immunarch, load the test dataset: -data(immdata) -# 1.2b) To try immunarch on your own data, use the `repLoad` function on your data folder: -immdata = repLoad("path/to/your/folder/with/repertoires") -``` +## Quick start +The gist of the typical TCR or BCR data analysis workflow can be reduced to the next few lines of code. -**2) Analyse repertoire similarity at the clonotype level** +### Use `immunarch` data -```{r eval=FALSE} -# 2.1) Find the number of shared clonotypes and visualise it: -ov = repOverlap(immdata$data) -vis(ov) +**1) Load the package and the data** -# 2.2) Cluster samples by their similarity: -ov.kmeans = repOverlapAnalysis(ov, .method = "mds+kmeans") -vis(ov.kmeans) +```r +library(immunarch) # Load the package into R +data(immdata) # Load the test dataset ``` -**3) Find repertoire differences in the Variable gene usage** +**2) Calculate and visualise basic statistics** -```{r eval=FALSE} -# 3.1) Compute V gene usage and and highlight gene differences in groups with different clinical status: -gu = geneUsage(immdata$data) -vis(gu, .by="Status", .meta=immdata$meta) - -# 3.2) Cluster samples by their V gene usage similarity: -gu.clust = geneUsageAnalysis(gu, .method = "js+hclust") -vis(gu.clust) +```r +repExplore(immdata$data, "lens") %>% vis() # Visualise the length distribution of CDR3 +repClonality(immdata$data, "homeo") %>% vis() # Visualise the relative abundance of clonotypes ``` -**4) Find differences in the diversity of repertoires** - -```{r eval=F} -# 4.1) Compare diversity of repertoires and visualise samples, grouped by both clinical status and sequencing Lane: -div = repDiversity(immdata$data, .method = "chao1") -vis(div, .by=c("Status", "Lane"), .meta=immdata$meta) +**3) Explore and compare T-cell and B-cell repertoires** +```r +repOverlap(immdata$data) %>% vis() # Build the heatmap of public clonotypes shared between repertoires +geneUsage(immdata$data[[1]]) %>% vis() # Visualise the V-gene distribution for the first repertoire +repDiversity(immdata$data) %>% vis(.by = "Status", .meta = immdata$meta) # Visualise the Chao1 diversity of repertoires, grouped by the patient status ``` -**5) Manipulate plots to make them publication-ready** +### Use your own data -```{r eval=FALSE} -# 5.1) Manipulate the visualisation of diversity estimates to make the plot publication-ready: -div = repDiversity(immdata$data, .method = "chao1") -div.plot = vis(div, .by=c("Status", "Lane"), .meta=immdata$meta) -fixVis(div.plot) +```r +library(immunarch) # Load the package into R +immdata <- repLoad("path/to/your/data") # Replace it with the path to your data. Immunarch automatically detects the file format. ``` -**6) Advanced methods** +### Advanced methods -For advanced methods such as clonotype tracking, kmer analysis and public repertoire analysis see tutorials on the [immunarch website](https://immunarch.com/). +For advanced methods such as clonotype annotation, clonotype tracking, kmer analysis and public repertoire analysis see "Tutorials". ## Installation troubleshooting @@ -254,4 +239,4 @@ devtools::install_local("path/to/your/folder/with/immunarch.tar.gz", dependencie Check your path to the downloaded package archive file. It should not be "path/to/your/folder/with/immunarch.tar.gz", but a path on your PC to the downloaded file, e.g., "C:/Users/UserName/Downloads/immunarch.tar.gz" or "/Users/UserName/Downloads/immunarch.tar.gz". -9. If troubles still persist, message us on support@immunomind.io or create an issue in [our GitHub]( https://github.com/immunomind/immunarch/issues) with the code that represents the issue and the output you get in the console. +9. If troubles still persist, let us know via [GitHub](https://github.com/immunomind/immunarch/issues) (preferably) or [support@immunomind.io](mailto:support@immunomind.io) (in case of private data). From a93eee39077e6f6866d9f876ec0ba66d26a12376 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Sat, 6 Jun 2020 19:36:13 +0300 Subject: [PATCH 11/54] refactor(vis): remove treemap and replace gridExtra with patchwork --- DESCRIPTION | 3 +- NAMESPACE | 6 +- R/vis.R | 133 +++++++--------------------- man/vis.Rd | 2 +- man/vis.immunr_chao1.Rd | 1 - man/vis.immunr_clonal_prop.Rd | 1 - man/vis.immunr_gene_usage.Rd | 14 +-- man/vis.immunr_hclust.Rd | 13 +-- man/vis.immunr_kmeans.Rd | 6 +- man/vis.immunr_kmer_table.Rd | 3 +- man/vis.immunr_mds.Rd | 1 - man/vis.immunr_ov_matrix.Rd | 1 - man/vis.immunr_public_repertoire.Rd | 1 - man/vis.immunr_public_statistics.Rd | 3 +- man/vis_bar.Rd | 1 - man/vis_box.Rd | 1 - man/vis_heatmap.Rd | 1 - man/vis_heatmap2.Rd | 1 - man/vis_hist.Rd | 3 - man/vis_public_clonotypes.Rd | 5 +- man/vis_treemap.Rd | 26 ------ 21 files changed, 46 insertions(+), 180 deletions(-) delete mode 100644 man/vis_treemap.Rd diff --git a/DESCRIPTION b/DESCRIPTION index bc9382a4..9936e18e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -26,7 +26,6 @@ BugReports: https://github.com/immunomind/immunarch/issues Imports: factoextra (>= 1.0.4), fpc, - treemap, UpSetR (>= 1.4.0), pheatmap (>= 1.0.12), ggrepel (>= 0.8.0), @@ -57,7 +56,7 @@ Depends: dplyr (>= 0.8.0), dtplyr (>= 1.0.0), data.table (>= 1.12.6), - gridExtra (>= 2.2.1) + patchwork LinkingTo: Rcpp Suggests: knitr (>= 1.8), diff --git a/NAMESPACE b/NAMESPACE index e4a6e98c..d3db8048 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -93,7 +93,6 @@ export(vis_hist) export(vis_immunr_kmer_profile_main) export(vis_seqlogo) export(vis_textlogo) -export(vis_treemap) import(ggplot2) importFrom(Rcpp,cppFunction) importFrom(Rcpp,sourceCpp) @@ -149,10 +148,10 @@ importFrom(grDevices,colorRampPalette) importFrom(graphics,plot) importFrom(grid,gpar) importFrom(grid,rectGrob) -importFrom(gridExtra,arrangeGrob) -importFrom(gridExtra,grid.arrange) importFrom(magrittr,"%>%") importFrom(methods,as) +importFrom(patchwork,plot_annotation) +importFrom(patchwork,wrap_plots) importFrom(pheatmap,pheatmap) importFrom(plyr,mapvalues) importFrom(readr,col_character) @@ -213,7 +212,6 @@ importFrom(stringr,str_replace_all) importFrom(stringr,str_sort) importFrom(stringr,str_split) importFrom(tibble,tibble) -importFrom(treemap,treemap) importFrom(utils,packageVersion) importFrom(utils,read.table) importFrom(utils,setTxtProgressBar) diff --git a/R/vis.R b/R/vis.R index 84ad1d5b..73663450 100644 --- a/R/vis.R +++ b/R/vis.R @@ -152,7 +152,7 @@ theme_cleveland2 <- function(rotate = TRUE) { #' #' Gene usage: #' -#' - Gene usage statistics (from \link{geneUsage}) using bar plots, box plots, treemaps - see \link{vis.immunr_gene_usage}; +#' - Gene usage statistics (from \link{geneUsage}) using bar plots, box plots - see \link{vis.immunr_gene_usage}; #' #' - Gene usage distances (from \link{geneUsageAnalysis}) using heatmaps, circos plots, polar area plots - see \link{vis.immunr_ov_matrix}; #' @@ -246,7 +246,6 @@ vis <- function(.data, ...) { #' vis(ov, "heatmap") #' vis(ov, "heatmap2") #' vis(ov, "circos") -#' #' @export vis.immunr_ov_matrix <- function(.data, .plot = c("heatmap", "heatmap2", "circos"), ...) { args <- list(...) @@ -334,7 +333,6 @@ vis.immunr_gu_matrix <- function(.data, .plot = c("heatmap", "heatmap2", "circos #' vis_heatmap(ov) #' gu <- geneUsage(immdata$data, "hs.trbj") #' vis_heatmap(gu) -#' #' @export vis_heatmap <- function(.data, .text = TRUE, .scientific = FALSE, .signif.digits = 2, .text.size = 4, .labs = c("Sample", "Sample"), .title = "Overlap", @@ -433,7 +431,6 @@ vis_heatmap <- function(.data, .text = TRUE, .scientific = FALSE, .signif.digits #' data(immdata) #' ov <- repOverlap(immdata$data) #' vis_heatmap2(ov) -#' #' @export vis_heatmap2 <- function(.data, .title = NA, .labs = NA, .color = colorRampPalette(c("#67001f", "#d6604d", "#f7f7f7", "#4393c3", "#053061"))(1024), ...) { args <- list() @@ -610,7 +607,7 @@ vis.immunr_inc_overlap <- function(.data, .target = 1, .grid = FALSE, .ncol = 2, p }) - return(do.call(grid.arrange, c(p_list, list(ncol = .ncol)))) + return(do.call(wrap_plots, c(p_list, list(ncol = .ncol)))) } else { if (data_is_bootstrapped) { sample_names <- colnames(.data[[1]][[1]]) @@ -694,7 +691,6 @@ vis.immunr_inc_overlap <- function(.data, .target = 1, .grid = FALSE, .ncol = 2, #' vis(pr, "freq", .type = "none") #' #' vis(pr, "clonotypes", 1, 2) -#' #' @export vis.immunr_public_repertoire <- function(.data, .plot = c("freq", "clonotypes"), ...) { .plot <- .plot[1] @@ -728,9 +724,8 @@ vis.immunr_public_repertoire <- function(.data, .plot = c("freq", "clonotypes"), #' @examples #' data(immdata) #' immdata$data <- lapply(immdata$data, head, 2000) -#' pr <- pubRep(immdata$data, .verbose=FALSE) +#' pr <- pubRep(immdata$data, .verbose = FALSE) #' pubRepStatistics(pr) %>% vis() -#' #' @export vis.immunr_public_statistics <- function(.data, ...) { upsetr_data <- as.list(.data$Count) @@ -838,6 +833,7 @@ vis_public_frequencies <- function(.data, .by = NA, .meta = NA, #' #' @importFrom grid rectGrob gpar #' @importFrom stats lm +#' @importFrom patchwork wrap_plots plot_annotation #' #' @description Visualise correlation of public clonotype frequencies in pairs of repertoires. #' @@ -867,8 +863,6 @@ vis_public_frequencies <- function(.data, .by = NA, .meta = NA, #' @param .radj.size An integer value, that defines the size of the The text #' for the R adjusted coefficient. #' -#' @param .return.grob If TRUE then returh the gridArrange grob instead of the plot. -#' #' @return #' A ggplot2 object. #' @@ -881,7 +875,7 @@ vis_public_frequencies <- function(.data, .by = NA, .meta = NA, vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA, .title = NA, .ncol = 3, .point.size.modif = 1, .cut.axes = TRUE, - .density = TRUE, .lm = TRUE, .radj.size = 3.5, .return.grob = FALSE) { + .density = TRUE, .lm = TRUE, .radj.size = 3.5) { .shared.rep <- .data mat <- public_matrix(.shared.rep) @@ -892,12 +886,11 @@ vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA, for (j in 1:ncol(mat)) { ps <- c(ps, list(vis_public_clonotypes(.shared.rep, i, j, "", .point.size.modif = .point.size.modif, - .cut.axes = .cut.axes, .density = .density, .lm = .lm, .radj.size = .radj.size, - .return.grob = TRUE + .cut.axes = .cut.axes, .density = .density, .lm = .lm, .radj.size = .radj.size ))) } } - grid.arrange(grobs = ps, ncol = .ncol, top = .title) + do.call(wrap_plots, c(ps, ncol = .ncol)) + plot_annotation(title = .title) } else if (is.na(.x.rep)) { ps <- lapply(1:ncol(mat), function(i) { vis_public_clonotypes(.shared.rep, i, .y.rep, "", @@ -905,7 +898,7 @@ vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA, .cut.axes = .cut.axes, .density = .density, .lm = .lm ) }) - do.call(grid.arrange, c(ps, ncol = .ncol, top = .title)) + do.call(wrap_plots, c(ps, ncol = .ncol)) + plot_annotation(title = .title) } else if (is.na(.y.rep)) { ps <- lapply(1:ncol(mat), function(j) { vis_public_clonotypes(.shared.rep, .x.rep, j, "", @@ -913,7 +906,7 @@ vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA, .cut.axes = .cut.axes, .density = .density, .lm = .lm ) }) - do.call(grid.arrange, c(ps, ncol = .ncol, top = .title)) + do.call(wrap_plots, c(ps, ncol = .ncol)) + plot_annotation(title = .title) } else { if (!is.character(.x.rep)) { .x.rep <- colnames(mat)[.x.rep] @@ -1028,11 +1021,7 @@ vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA, ) + scale_fill_manual(values = colorRampPalette(c(.colourblind_vector()[1], grey_col))(2)) - if (!.return.grob) { - grid.arrange(top_plot, empty, points, right_plot, ncol = 2, nrow = 2, widths = c(4, 1), heights = c(1, 4)) - } else { - arrangeGrob(top_plot, empty, points, right_plot, ncol = 2, nrow = 2, widths = c(4, 1), heights = c(1, 4)) - } + wrap_plots(top_plot, empty, points, right_plot, ncol = 2, nrow = 2, widths = c(4, 1), heights = c(1, 4)) } else { points } @@ -1040,7 +1029,7 @@ vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA, } -##### Gene usage & histogram plot, boxplot and treemap ##### +##### Gene usage & histogram plot, boxplot ##### #' Histograms and boxplots (general case / gene usage) @@ -1049,7 +1038,7 @@ vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA, #' #' @name vis.immunr_gene_usage #' -#' @description Visualise distributions of genes using heatmaps, treemaps or other plots. +#' @description Visualise distributions of genes using heatmaps or other plots. #' #' @param .data Output from the \link{geneUsage} function. #' @@ -1057,8 +1046,6 @@ vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA, #' #' - "hist" for histograms using \link{vis_hist}; #' -#' - "tree" for histograms using \link{vis_treemap}; -#' #' - "heatmap" for heatmaps using \link{vis_heatmap}; #' #' - "heatmap2" for heatmaps using \link{vis_heatmap2}; @@ -1071,8 +1058,6 @@ vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA, #' #' - "box" - passes arguments to \link{vis_box}; #' -#' - "tree" - passes arguments to \link{vis_treemap} and \link{treemap} from the treemap package; -#' #' - "heatmap" - passes arguments to \link{vis_heatmap}; #' #' - "heatmap2" - passes arguments to \link{vis_heatmap2} and \link{heatmap} from the "pheatmap" package; @@ -1080,7 +1065,7 @@ vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA, #' - "circos" - passes arguments to \link{vis_circos} and \link{chordDiagram} from the "circlize" package. #' #' @return -#' A ggplot2, pheatmap, circlize or treemap object. +#' A ggplot2 object, pheatmap or circlize object. #' #' @examples #' data(immdata) @@ -1094,7 +1079,7 @@ vis_public_clonotypes <- function(.data, .x.rep = NA, .y.rep = NA, #' @seealso \link{geneUsage} #' #' @export -vis.immunr_gene_usage <- function(.data, .plot = c("hist", "box", "tree", "heatmap", "heatmap2", "circos"), ...) { +vis.immunr_gene_usage <- function(.data, .plot = c("hist", "box", "heatmap", "heatmap2", "circos"), ...) { .plot <- .plot[1] if (.plot == "hist") { vis_hist(.data, ...) @@ -1104,8 +1089,6 @@ vis.immunr_gene_usage <- function(.data, .plot = c("hist", "box", "tree", "heatm .labs = c("Gene", "Usage"), .title = "Gene usage", .subtitle = NULL, ... ) - } else if (.plot == "tree") { - vis_treemap(.data, ...) } else if (.plot == "heatmap") { row.names(.data) <- .data[[1]] .data <- t(as.matrix(.data[2:ncol(.data)])) @@ -1119,7 +1102,7 @@ vis.immunr_gene_usage <- function(.data, .plot = c("hist", "box", "tree", "heatm .data <- t(as.matrix(.data[2:ncol(.data)])) vis_circos(.data, ...) } else { - stop("Error: Unknown value of the .plot parameter. Please provide one of the following: 'hist', 'box', 'tree'.") + stop("Error: Unknown value of the .plot parameter. Please provide one of the following: 'hist', 'box', 'heatmap', 'heatmap2', 'circos'.") } } @@ -1128,8 +1111,6 @@ vis.immunr_gene_usage <- function(.data, .plot = c("hist", "box", "tree", "heatm #' #' @concept vis #' -#' @importFrom gridExtra arrangeGrob grid.arrange -#' #' @name vis_hist #' #' @description Visualisation of distributions using ggplot2-based histograms. @@ -1164,8 +1145,6 @@ vis.immunr_gene_usage <- function(.data, .plot = c("hist", "box", "tree", "heatm #' #' @param .labs A character vector of length two with names for x-axis and y-axis, respectively. #' -#' @param .return.grob If TRUE then returh the gridArrange grob instead of the plot. -#' #' @param .melt If TRUE then apply \link{melt} to the ".data" before plotting. #' In this case ".data" is supposed to be a data frame with the first character column reserved #' for names of genes and other numeric columns reserved to counts or frequencies of genes. @@ -1208,7 +1187,7 @@ vis.immunr_gene_usage <- function(.data, .plot = c("hist", "box", "tree", "heatm #' @export vis_hist <- function(.data, .by = NA, .meta = NA, .title = "Gene usage", .ncol = NA, .points = TRUE, .test = TRUE, .coord.flip = FALSE, - .grid = FALSE, .labs = c("Gene", NA), .return.grob = FALSE, + .grid = FALSE, .labs = c("Gene", NA), .melt = TRUE, .legend = NA, .add.layer = NULL, ...) { res <- .data @@ -1286,27 +1265,24 @@ vis_hist <- function(.data, .by = NA, .meta = NA, .title = "Gene usage", .ncol = .add.layer } - if (.return.grob) { - return(do.call(gridExtra::arrangeGrob, c(ps, ncol = .ncol, top = .title))) - } else { - return(do.call(gridExtra::grid.arrange, c(ps, ncol = .ncol, top = .title))) - } + p <- do.call(wrap_plots, c(ps, ncol = .ncol)) + p <- p + plot_annotation(title = .title) + return(p) + } else { res <- split(res, res$Sample) ps <- list() for (i in 1:length(res)) { ps[[i]] <- vis_hist(res[[i]], .title = names(res)[i], .ncol = NA, .coord.flip = .coord.flip, - .grid = FALSE, .labs = c("Gene", NA), .return.grob = FALSE, + .grid = FALSE, .labs = c("Gene", NA), .melt = FALSE, .legend = .legend, .add.layer = .add.layer, ... ) } - if (.return.grob) { - return(do.call(gridExtra::arrangeGrob, c(ps, ncol = .ncol, top = .title))) - } else { - return(do.call(gridExtra::grid.arrange, c(ps, ncol = .ncol, top = .title))) - } + p <- do.call(wrap_plots, c(ps, ncol = .ncol)) + p <- p + plot_annotation(title = .title) + return(p) } } else { res$Value <- res$Freq @@ -1373,7 +1349,6 @@ vis_hist <- function(.data, .by = NA, .meta = NA, .title = "Gene usage", .ncol = #' #' @examples #' vis_box(data.frame(Sample = sample(c("A", "B", "C"), 100, TRUE), Value = rnorm(100)), .melt = FALSE) -#' #' @export vis_box <- function(.data, .by = NA, .meta = NA, .melt = TRUE, .points = TRUE, .test = TRUE, .signif.label.size = 3.5, .defgroupby = "Sample", .grouping.var = "Group", @@ -1500,33 +1475,6 @@ vis_box <- function(.data, .by = NA, .meta = NA, .melt = TRUE, rotate_x_text() + theme_cleveland2() } -#' Visualisation of data frames and matrices using treemaps -#' -#' @concept vis -#' -#' @name vis_treemap -#' -#' @importFrom treemap treemap -#' -#' @param .data Input data frame or matrix. -#' -#' @param ... Other arguments passed to \link{treemap}. -#' -#' @return -#' A treemap object. -#' -#' @examples -#' data(immdata) -#' gu <- geneUsage(immdata$data) -#' vis_treemap(gu) -#' -#' @export -vis_treemap <- function(.data, ...) { - melted <- reshape2::melt(.data) - colnames(melted) <- c("name", "group", "value") - treemap::treemap(melted, index = c("group", "name"), vSize = "value", ...) -} - ##### Clustering ##### @@ -1546,11 +1494,10 @@ vis_treemap <- function(.data, ...) { #' @param .plot A character vector of length one or two specifying which plots to visualise. #' If "clust" then plot only the clustering. If "best" then plot the number of optimal clusters. #' If both then plot both. -#' @param .return.grob If TRUE then return grob instead of plot. #' @param ... Not used here. #' #' @return -#' Ggplot2 objects inside the gridExtra container. +#' Ggplot2 objects inside the patchwork container. #' #' @seealso \link{vis}, \link{repOverlapAnalysis}, \link{geneUsageAnalysis} #' @@ -1558,9 +1505,8 @@ vis_treemap <- function(.data, ...) { #' data(immdata) #' ov <- repOverlap(immdata$data) #' repOverlapAnalysis(ov, "mds+hclust") %>% vis() -#' #' @export -vis.immunr_hclust <- function(.data, .rect = FALSE, .plot = c("clust", "best"), .return.grob = FALSE, ...) { +vis.immunr_hclust <- function(.data, .rect = FALSE, .plot = c("clust", "best"), ...) { p1 <- NULL if ("clust" %in% .plot) { p1 <- fviz_dend(.data[[1]], main = "Hierarchical clustering", rect = .rect) @@ -1578,11 +1524,8 @@ vis.immunr_hclust <- function(.data, .rect = FALSE, .plot = c("clust", "best"), p1 } } else { - if (.return.grob) { - gridExtra::arrangeGrob(p1, p2, ncol = 2) - } else { - gridExtra::grid.arrange(p1, p2, ncol = 2) - } + p <- p1 + p2 + p } } @@ -1606,11 +1549,10 @@ vis.immunr_hclust <- function(.data, .rect = FALSE, .plot = c("clust", "best"), #' @param .plot A character vector of length one or two specifying which plots to visualise. #' If "clust" then plot only the clustering. If "best" then plot the number of optimal clusters. #' If both then plot both. -#' @param .return.grob If TRUE then return grob instead of plot. #' @param ... Not used here. #' #' @return -#' Ggplot2 objects inside the gridExtra container. +#' Ggplot2 objects inside the pathwork container. #' #' @seealso \link{vis}, \link{repOverlapAnalysis}, \link{geneUsageAnalysis} #' @@ -1618,11 +1560,10 @@ vis.immunr_hclust <- function(.data, .rect = FALSE, .plot = c("clust", "best"), #' data(immdata) #' ov <- repOverlap(immdata$data) #' repOverlapAnalysis(ov, "mds+kmeans") %>% vis() -#' #' @export vis.immunr_kmeans <- function(.data, .point = TRUE, .text = TRUE, .ellipse = TRUE, .point.size = 2, .text.size = 10, .plot = c("clust", "best"), - .return.grob = FALSE, ...) { + ...) { p1 <- NULL if ("clust" %in% .plot) { p1 <- fviz_cluster(.data[[1]], @@ -1647,11 +1588,7 @@ vis.immunr_kmeans <- function(.data, .point = TRUE, .text = TRUE, .ellipse = TRU } # p = ifelse(is.null(p1), p2, p1) # doesnt work for some reason } else { - if (.return.grob) { - gridExtra::arrangeGrob(p1, p2, ncol = 2) - } else { - gridExtra::grid.arrange(p1, p2, ncol = 2) - } + p1 + p2 } } @@ -1716,7 +1653,6 @@ vis.immunr_dbscan <- function(.data, .point = TRUE, .text = TRUE, .ellipse = TRU #' data(immdata) #' ov <- repOverlap(immdata$data) #' repOverlapAnalysis(ov, "mds") %>% vis() -#' #' @export vis.immunr_mds <- function(.data, .by = NA, .meta = NA, .point = TRUE, .text = TRUE, .ellipse = TRUE, @@ -1925,7 +1861,6 @@ vis_bar_stacked <- function(.data, .by = NA, .meta = NA, #' data(immdata) #' clp <- repClonality(immdata$data, "clonal.prop") #' vis(clp) -#' #' @export vis.immunr_clonal_prop <- function(.data, .by = NA, .meta = NA, .errorbars = c(0.025, 0.975), .errorbars.off = FALSE, .points = TRUE, .test = TRUE, .signif.label.size = 3.5, ...) { # ToDo: this and other repClonality and repDiversity functions doesn't work on a single repertoire. Fix it @@ -2101,7 +2036,6 @@ vis.immunr_rare_prop <- function(.data, .by = NA, .meta = NA, .errorbars = c(0.0 #' #' @examples #' vis_bar(data.frame(Sample = c("A", "B", "C"), Value = c(1, 2, 3))) -#' #' @export vis_bar <- function(.data, .by = NA, .meta = NA, .errorbars = c(0.025, 0.975), .errorbars.off = FALSE, .stack = FALSE, .points = TRUE, .test = TRUE, .signif.label.size = 3.5, .errorbar.width = 0.2, .defgroupby = "Sample", .grouping.var = "Group", @@ -2322,7 +2256,6 @@ vis_bar <- function(.data, .by = NA, .meta = NA, .errorbars = c(0.025, 0.975), . #' data(immdata) #' dv <- repDiversity(immdata$data, "chao1") #' vis(dv) -#' #' @export vis.immunr_chao1 <- function(.data, .by = NA, .meta = NA, .errorbars = c(0.025, 0.975), .errorbars.off = FALSE, .points = TRUE, .test = TRUE, .signif.label.size = 3.5, ...) { .data <- data.frame(Sample = row.names(.data), Value = .data[, 1]) @@ -2698,14 +2631,13 @@ vis.immunr_exp_clones <- function(.data, .by = NA, .meta = NA, #' #' @examples #' # Load necessary data and package. -#' library(gridExtra) #' data(immdata) #' # Get 5-mers. #' imm.km <- getKmers(immdata$data[[1]], 5) #' # Plots for kmer proportions in each data frame in immdata. #' p1 <- vis(imm.km, .position = "stack") #' p2 <- vis(imm.km, .position = "fill") -#' grid.arrange(p1, p2) +#' p1 + p2 #' @export vis.immunr_kmer_table <- function(.data, .head = 100, .position = c("stack", "dodge", "fill"), .log = FALSE, ...) { .position <- switch(substr(.position[1], 1, 1), s = "stack", d = "dodge", f = "fill") @@ -2975,6 +2907,7 @@ vis.immunr_dynamics <- function(.data, .plot = c("smooth", "area", "line"), .ord melted$Sample <- factor(melted$Sample, levels = ordered_sample_names, ordered = TRUE) } + melted$Count <- melted$Count + min(melted$Count[melted$Count != 0]) / 1000 p <- ggplot(melted, aes( x = Sample, fill = Clonotype, stratum = Clonotype, alluvium = Clonotype, y = Count, label = Clonotype diff --git a/man/vis.Rd b/man/vis.Rd index 7ce0d4ba..134a95ab 100644 --- a/man/vis.Rd +++ b/man/vis.Rd @@ -43,7 +43,7 @@ Overlaps and public clonotypes: Gene usage: -- Gene usage statistics (from \link{geneUsage}) using bar plots, box plots, treemaps - see \link{vis.immunr_gene_usage}; +- Gene usage statistics (from \link{geneUsage}) using bar plots, box plots - see \link{vis.immunr_gene_usage}; - Gene usage distances (from \link{geneUsageAnalysis}) using heatmaps, circos plots, polar area plots - see \link{vis.immunr_ov_matrix}; diff --git a/man/vis.immunr_chao1.Rd b/man/vis.immunr_chao1.Rd index fd5c5eca..acb0c94a 100644 --- a/man/vis.immunr_chao1.Rd +++ b/man/vis.immunr_chao1.Rd @@ -72,7 +72,6 @@ You can execute the command \code{?p.adjust} in the R console to see more. data(immdata) dv <- repDiversity(immdata$data, "chao1") vis(dv) - } \seealso{ \link{repDiversity} \link{vis} diff --git a/man/vis.immunr_clonal_prop.Rd b/man/vis.immunr_clonal_prop.Rd index a44badea..e098e896 100644 --- a/man/vis.immunr_clonal_prop.Rd +++ b/man/vis.immunr_clonal_prop.Rd @@ -69,7 +69,6 @@ You can execute the command \code{?p.adjust} in the R console to see more. data(immdata) clp <- repClonality(immdata$data, "clonal.prop") vis(clp) - } \seealso{ \link{repClonality} \link{vis} diff --git a/man/vis.immunr_gene_usage.Rd b/man/vis.immunr_gene_usage.Rd index f7d92577..939fc26c 100644 --- a/man/vis.immunr_gene_usage.Rd +++ b/man/vis.immunr_gene_usage.Rd @@ -4,11 +4,7 @@ \alias{vis.immunr_gene_usage} \title{Histograms and boxplots (general case / gene usage)} \usage{ -\method{vis}{immunr_gene_usage}( - .data, - .plot = c("hist", "box", "tree", "heatmap", "heatmap2", "circos"), - ... -) +\method{vis}{immunr_gene_usage}(.data, .plot = c("hist", "box", "heatmap", "heatmap2", "circos"), ...) } \arguments{ \item{.data}{Output from the \link{geneUsage} function.} @@ -17,8 +13,6 @@ - "hist" for histograms using \link{vis_hist}; -- "tree" for histograms using \link{vis_treemap}; - - "heatmap" for heatmaps using \link{vis_heatmap}; - "heatmap2" for heatmaps using \link{vis_heatmap2}; @@ -31,8 +25,6 @@ - "box" - passes arguments to \link{vis_box}; -- "tree" - passes arguments to \link{vis_treemap} and \link{treemap} from the treemap package; - - "heatmap" - passes arguments to \link{vis_heatmap}; - "heatmap2" - passes arguments to \link{vis_heatmap2} and \link{heatmap} from the "pheatmap" package; @@ -40,10 +32,10 @@ - "circos" - passes arguments to \link{vis_circos} and \link{chordDiagram} from the "circlize" package.} } \value{ -A ggplot2, pheatmap, circlize or treemap object. +A ggplot2 object, pheatmap or circlize object. } \description{ -Visualise distributions of genes using heatmaps, treemaps or other plots. +Visualise distributions of genes using heatmaps or other plots. } \examples{ data(immdata) diff --git a/man/vis.immunr_hclust.Rd b/man/vis.immunr_hclust.Rd index ddac4397..93bb2a6f 100644 --- a/man/vis.immunr_hclust.Rd +++ b/man/vis.immunr_hclust.Rd @@ -4,13 +4,7 @@ \alias{vis.immunr_hclust} \title{Visualisation of hierarchical clustering} \usage{ -\method{vis}{immunr_hclust}( - .data, - .rect = FALSE, - .plot = c("clust", "best"), - .return.grob = FALSE, - ... -) +\method{vis}{immunr_hclust}(.data, .rect = FALSE, .plot = c("clust", "best"), ...) } \arguments{ \item{.data}{Clustering results from \link{repOverlapAnalysis} or \link{geneUsageAnalysis}.} @@ -21,12 +15,10 @@ If "clust" then plot only the clustering. If "best" then plot the number of optimal clusters. If both then plot both.} -\item{.return.grob}{If TRUE then return grob instead of plot.} - \item{...}{Not used here.} } \value{ -Ggplot2 objects inside the gridExtra container. +Ggplot2 objects inside the patchwork container. } \description{ Visualisation of the results of hierarchical clustering. @@ -36,7 +28,6 @@ For other clustering visualisations see \link{vis.immunr_kmeans}. data(immdata) ov <- repOverlap(immdata$data) repOverlapAnalysis(ov, "mds+hclust") \%>\% vis() - } \seealso{ \link{vis}, \link{repOverlapAnalysis}, \link{geneUsageAnalysis} diff --git a/man/vis.immunr_kmeans.Rd b/man/vis.immunr_kmeans.Rd index 1911acb8..736c133e 100644 --- a/man/vis.immunr_kmeans.Rd +++ b/man/vis.immunr_kmeans.Rd @@ -13,7 +13,6 @@ .point.size = 2, .text.size = 10, .plot = c("clust", "best"), - .return.grob = FALSE, ... ) } @@ -34,12 +33,10 @@ If "clust" then plot only the clustering. If "best" then plot the number of optimal clusters. If both then plot both.} -\item{.return.grob}{If TRUE then return grob instead of plot.} - \item{...}{Not used here.} } \value{ -Ggplot2 objects inside the gridExtra container. +Ggplot2 objects inside the pathwork container. } \description{ Visualisation of the results of K-means and DBSCAN clustering. @@ -49,7 +46,6 @@ For hierarhical clustering visualisations see \link{vis.immunr_hclust}. data(immdata) ov <- repOverlap(immdata$data) repOverlapAnalysis(ov, "mds+kmeans") \%>\% vis() - } \seealso{ \link{vis}, \link{repOverlapAnalysis}, \link{geneUsageAnalysis} diff --git a/man/vis.immunr_kmer_table.Rd b/man/vis.immunr_kmer_table.Rd index dee28d2c..7b00ea7d 100644 --- a/man/vis.immunr_kmer_table.Rd +++ b/man/vis.immunr_kmer_table.Rd @@ -31,14 +31,13 @@ Plot a distribution (bar plot) of the most frequent kmers in a data. } \examples{ # Load necessary data and package. -library(gridExtra) data(immdata) # Get 5-mers. imm.km <- getKmers(immdata$data[[1]], 5) # Plots for kmer proportions in each data frame in immdata. p1 <- vis(imm.km, .position = "stack") p2 <- vis(imm.km, .position = "fill") -grid.arrange(p1, p2) +p1 + p2 } \seealso{ \code{get.kmers} diff --git a/man/vis.immunr_mds.Rd b/man/vis.immunr_mds.Rd index 31345c8b..9528c305 100644 --- a/man/vis.immunr_mds.Rd +++ b/man/vis.immunr_mds.Rd @@ -66,6 +66,5 @@ Other visualisation methods: data(immdata) ov <- repOverlap(immdata$data) repOverlapAnalysis(ov, "mds") \%>\% vis() - } \concept{post_analysis} diff --git a/man/vis.immunr_ov_matrix.Rd b/man/vis.immunr_ov_matrix.Rd index cb35ed75..8b789428 100644 --- a/man/vis.immunr_ov_matrix.Rd +++ b/man/vis.immunr_ov_matrix.Rd @@ -40,6 +40,5 @@ vis(ov) vis(ov, "heatmap") vis(ov, "heatmap2") vis(ov, "circos") - } \concept{overlap} diff --git a/man/vis.immunr_public_repertoire.Rd b/man/vis.immunr_public_repertoire.Rd index 88a63777..b0872511 100644 --- a/man/vis.immunr_public_repertoire.Rd +++ b/man/vis.immunr_public_repertoire.Rd @@ -34,6 +34,5 @@ vis(pr, "freq") vis(pr, "freq", .type = "none") vis(pr, "clonotypes", 1, 2) - } \concept{pubrep} diff --git a/man/vis.immunr_public_statistics.Rd b/man/vis.immunr_public_statistics.Rd index 3deec0bf..3ceb35ed 100644 --- a/man/vis.immunr_public_statistics.Rd +++ b/man/vis.immunr_public_statistics.Rd @@ -20,8 +20,7 @@ Visualise public clonotype frequencies. \examples{ data(immdata) immdata$data <- lapply(immdata$data, head, 2000) -pr <- pubRep(immdata$data, .verbose=FALSE) +pr <- pubRep(immdata$data, .verbose = FALSE) pubRepStatistics(pr) \%>\% vis() - } \concept{pubrep} diff --git a/man/vis_bar.Rd b/man/vis_bar.Rd index 33daa8a9..a36ebc41 100644 --- a/man/vis_bar.Rd +++ b/man/vis_bar.Rd @@ -84,6 +84,5 @@ Bar plots } \examples{ vis_bar(data.frame(Sample = c("A", "B", "C"), Value = c(1, 2, 3))) - } \concept{vis} diff --git a/man/vis_box.Rd b/man/vis_box.Rd index d3fb43ff..fdaa8b14 100644 --- a/man/vis_box.Rd +++ b/man/vis_box.Rd @@ -73,7 +73,6 @@ Visualisation of distributions using ggplot2-based boxplots. } \examples{ vis_box(data.frame(Sample = sample(c("A", "B", "C"), 100, TRUE), Value = rnorm(100)), .melt = FALSE) - } \seealso{ \link{vis.immunr_gene_usage}, \link{geneUsage} diff --git a/man/vis_heatmap.Rd b/man/vis_heatmap.Rd index d9b5e3fa..4aff77d9 100644 --- a/man/vis_heatmap.Rd +++ b/man/vis_heatmap.Rd @@ -63,7 +63,6 @@ ov <- repOverlap(immdata$data) vis_heatmap(ov) gu <- geneUsage(immdata$data, "hs.trbj") vis_heatmap(gu) - } \seealso{ \link{vis}, \link{repOverlap}. diff --git a/man/vis_heatmap2.Rd b/man/vis_heatmap2.Rd index 51107f38..595dcbbf 100644 --- a/man/vis_heatmap2.Rd +++ b/man/vis_heatmap2.Rd @@ -36,7 +36,6 @@ package with minimum amount of arguments. data(immdata) ov <- repOverlap(immdata$data) vis_heatmap2(ov) - } \seealso{ \link{vis}, \link{repOverlap} diff --git a/man/vis_hist.Rd b/man/vis_hist.Rd index 20654e9a..b15cb753 100644 --- a/man/vis_hist.Rd +++ b/man/vis_hist.Rd @@ -15,7 +15,6 @@ vis_hist( .coord.flip = FALSE, .grid = FALSE, .labs = c("Gene", NA), - .return.grob = FALSE, .melt = TRUE, .legend = NA, .add.layer = NULL, @@ -53,8 +52,6 @@ to automatically detect the optimal number of columns.} \item{.labs}{A character vector of length two with names for x-axis and y-axis, respectively.} -\item{.return.grob}{If TRUE then returh the gridArrange grob instead of the plot.} - \item{.melt}{If TRUE then apply \link{melt} to the ".data" before plotting. In this case ".data" is supposed to be a data frame with the first character column reserved for names of genes and other numeric columns reserved to counts or frequencies of genes. diff --git a/man/vis_public_clonotypes.Rd b/man/vis_public_clonotypes.Rd index 70584c36..1b0e1a4d 100644 --- a/man/vis_public_clonotypes.Rd +++ b/man/vis_public_clonotypes.Rd @@ -14,8 +14,7 @@ vis_public_clonotypes( .cut.axes = TRUE, .density = TRUE, .lm = TRUE, - .radj.size = 3.5, - .return.grob = FALSE + .radj.size = 3.5 ) } \arguments{ @@ -44,8 +43,6 @@ that shows how similar samples are in terms of shared clonotypes.} \item{.radj.size}{An integer value, that defines the size of the The text for the R adjusted coefficient.} - -\item{.return.grob}{If TRUE then returh the gridArrange grob instead of the plot.} } \value{ A ggplot2 object. diff --git a/man/vis_treemap.Rd b/man/vis_treemap.Rd deleted file mode 100644 index bf54a282..00000000 --- a/man/vis_treemap.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/vis.R -\name{vis_treemap} -\alias{vis_treemap} -\title{Visualisation of data frames and matrices using treemaps} -\usage{ -vis_treemap(.data, ...) -} -\arguments{ -\item{.data}{Input data frame or matrix.} - -\item{...}{Other arguments passed to \link{treemap}.} -} -\value{ -A treemap object. -} -\description{ -Visualisation of data frames and matrices using treemaps -} -\examples{ -data(immdata) -gu <- geneUsage(immdata$data) -vis_treemap(gu) - -} -\concept{vis} From 55f81194f60c639c4d974064a6986249acda765c Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Sat, 6 Jun 2020 19:36:23 +0300 Subject: [PATCH 12/54] refactor(vignette): remove treemap and replace gridExtra with patchwork --- vignettes/v3_basic_analysis.Rmd | 62 ++++++++++++++-------------- vignettes/web_only/v4_overlap.Rmd | 37 ++++++++++------- vignettes/web_only/v5_gene_usage.Rmd | 50 +++++++++++----------- vignettes/web_only/v6_diversity.Rmd | 33 ++++++++------- vignettes/web_only/v8_tracking.Rmd | 34 ++++++++------- vignettes/web_only/v9_kmers.Rmd | 45 ++++++++++---------- 6 files changed, 137 insertions(+), 124 deletions(-) diff --git a/vignettes/v3_basic_analysis.Rmd b/vignettes/v3_basic_analysis.Rmd index bc02ccd4..30de2cfa 100644 --- a/vignettes/v3_basic_analysis.Rmd +++ b/vignettes/v3_basic_analysis.Rmd @@ -44,12 +44,12 @@ For each task in this section `immunarch` includes separate functions that are g Note: all functions in immunarch require that the input immune repertoire data list have names. If you use the `repLoad` function, you will have no issues. If you compose your list by-hand, you must name elements in the list, e.g.: ```{r rename-list, eval=F} -your_data # Your list with repertoires without names +your_data # Your list with repertoires without names names(your_data) # Output: NULL -names(your_data) = sapply(1:length(your_data), function (i) paste0("Sample", i)) +names(your_data) <- sapply(1:length(your_data), function(i) paste0("Sample", i)) names(your_data) # Output: Sample1 Sample2 ... Sample10 ``` @@ -83,18 +83,18 @@ Almost all visualisations of analysis results are support grouping data by their 1) You can pass `.by` as a character vector with one or several column names from the metadata table to group your data before plotting. In this case you should provide also the `.meta` argument with the metadata table. ```{r eda-by-meta, warning=F, fig.width=10, fig.height=4.5} -exp_vol = repExplore(immdata$data, .method = "volume") -p1 = vis(exp_vol, .by = c("Status"), .meta = immdata$meta) -p2 = vis(exp_vol, .by = c("Status", "Sex"), .meta = immdata$meta) -grid.arrange(p1, p2, ncol=2) +exp_vol <- repExplore(immdata$data, .method = "volume") +p1 <- vis(exp_vol, .by = c("Status"), .meta = immdata$meta) +p2 <- vis(exp_vol, .by = c("Status", "Sex"), .meta = immdata$meta) +p1 + p2 ``` 2) You can pass `.by` as a character vector that exactly matches the number of samples in your data, each value should correspond to a sample's property. It will be used to group data based on the values provided. Note that in this case you should pass NA to `.meta`. ```{r eda-by-only, warning=F, fig.width=6, fig.height=4.5} -exp_vol = repExplore(immdata$data, .method = "volume") -by_vec = c('C', 'C', 'C', 'C', 'C', 'C', 'MS', 'MS', 'MS', 'MS', 'MS', 'MS') -p = vis(exp_vol, .by = by_vec) +exp_vol <- repExplore(immdata$data, .method = "volume") +by_vec <- c("C", "C", "C", "C", "C", "C", "MS", "MS", "MS", "MS", "MS", "MS") +p <- vis(exp_vol, .by = by_vec) p ``` @@ -106,10 +106,10 @@ Adjusted for multiple comparisons P-values are plotted on the top of groups. P-v Plots generated by the `vis` function as well as any ggplot2-based plots can be passed to `fixVis`---built-in software tool for making publication-ready plots: ```{r eda-fixvis, eval=F} # 1. Analyse -exp_len = repExplore(immdata$data, .method = "len", .col = "aa") +exp_len <- repExplore(immdata$data, .method = "len", .col = "aa") # 2. Visualise -p1 = vis(exp_len) +p1 <- vis(exp_len) # 3. Fix and make publication-ready results fixVis(p1) @@ -120,63 +120,65 @@ See the `fixVis` tutorial [here](https://immunarch.com/articles/web_only/v7_fixv ## Exploratory analysis For the basic exploratory analysis such as comparing of number of reads / UMIs per repertoire or distribution use the function `repExplore`. ```{r eda-1, warning=F, fig.width=12, fig.height=4} -exp_len = repExplore(immdata$data, .method = "len", .col = "aa") -exp_cnt = repExplore(immdata$data, .method = "count") -exp_vol = repExplore(immdata$data, .method = "volume") +exp_len <- repExplore(immdata$data, .method = "len", .col = "aa") +exp_cnt <- repExplore(immdata$data, .method = "count") +exp_vol <- repExplore(immdata$data, .method = "volume") -p1 = vis(exp_len) -p2 = vis(exp_cnt) -p3 = vis(exp_vol) +p1 <- vis(exp_len) +p2 <- vis(exp_cnt) +p3 <- vis(exp_vol) p1 ``` ```{r eda-2, warning=F, fig.width=14, fig.height=4} -grid.arrange(p2, p3, ncol = 2) +p2 + p3 ``` ```{r eda-3, warning=F, fig.width=10, fig.height=4} # You can group samples by their metainformation -p4 = vis(exp_len, .by="Status", .meta=immdata$meta) -p5 = vis(exp_cnt, .by="Sex", .meta=immdata$meta) -p6 = vis(exp_vol, .by=c("Status", "Sex"), .meta=immdata$meta) +p4 <- vis(exp_len, .by = "Status", .meta = immdata$meta) +p5 <- vis(exp_cnt, .by = "Sex", .meta = immdata$meta) +p6 <- vis(exp_vol, .by = c("Status", "Sex"), .meta = immdata$meta) p4 ``` ```{r eda-4, warning=F, fig.width=10, fig.height=5} -grid.arrange(p5, p6, ncol = 2) +p5 + p6 ``` ## Clonality One of the ways to estimate the diversity of samples is to evaluate clonality. `repClonality` measures the amount of the most or the least frequent clonotypes. There are several methods to assess clonality, let us take a view of them. The `clonal.prop` method computes the proportion of repertoire occupied by the pools of cell clones: ```{r clonality-pr} -imm_pr = repClonality(immdata$data, .method = "clonal.prop") +imm_pr <- repClonality(immdata$data, .method = "clonal.prop") imm_pr ``` The `top` method considers the most abundant cell clonotypes: ```{r clonality-top} -imm_top = repClonality(immdata$data, .method = "top", .head = c(10, 100, 1000, 3000, 10000)) +imm_top <- repClonality(immdata$data, .method = "top", .head = c(10, 100, 1000, 3000, 10000)) imm_top ``` While the `rare` method deals with the least prolific clonotypes: ```{r clonality-rare} -imm_rare = repClonality(immdata$data, .method = "rare") +imm_rare <- repClonality(immdata$data, .method = "rare") imm_rare ``` Finally, the `homeo` method assesses the clonal space homeostasis, i.e., the proportion of the repertoire occupied by the clones of a given size: ```{r clonality-hom-vis-1, message=F, warning=F, fig.width=11, fig.height=4.5} -imm_hom = repClonality(immdata$data, .method = "homeo", - .clone.types = c(Small = .0001, Medium = .001, Large = .01, Hyperexpanded = 1)) +imm_hom <- repClonality(immdata$data, + .method = "homeo", + .clone.types = c(Small = .0001, Medium = .001, Large = .01, Hyperexpanded = 1) +) imm_hom -grid.arrange(vis(imm_top), vis(imm_top, .by="Status", .meta=immdata$meta), ncol = 2) +vis(imm_top) + vis(imm_top, .by = "Status", .meta = immdata$meta) -grid.arrange(vis(imm_rare), vis(imm_rare, .by="Status", .meta=immdata$meta), ncol = 2) +vis(imm_rare) + vis(imm_rare, .by = "Status", .meta = immdata$meta) -grid.arrange(vis(imm_hom), vis(imm_hom, .by=c("Status", "Sex"), .meta=immdata$meta), ncol = 2) +vis(imm_hom) + vis(imm_hom, .by = c("Status", "Sex"), .meta = immdata$meta) ``` diff --git a/vignettes/web_only/v4_overlap.Rmd b/vignettes/web_only/v4_overlap.Rmd index 555c846f..28a70a36 100644 --- a/vignettes/web_only/v4_overlap.Rmd +++ b/vignettes/web_only/v4_overlap.Rmd @@ -57,17 +57,23 @@ Repertoire overlap is the most common approach to measure repertoire similarity. The function that includes described methods is `repOverlap`. Again the output is easily visualised when passed to `vis()` function that does all the work: ```{r overlap, message=F, warning=FALSE, fig.width=12, fig.height=7} -imm_ov1 = repOverlap(immdata$data, .method = "public", .verbose = F) -imm_ov2 = repOverlap(immdata$data, .method = "morisita", .verbose = F) +imm_ov1 <- repOverlap(immdata$data, .method = "public", .verbose = F) +imm_ov2 <- repOverlap(immdata$data, .method = "morisita", .verbose = F) -grid.arrange(vis(imm_ov1), vis(imm_ov2, .text.size=2), ncol = 2) +p1 <- vis(imm_ov1) +p2 <- vis(imm_ov2, .text.size = 2) + +p1 + p2 vis(imm_ov1, "heatmap2") ``` You can easily change the number of significant digits: ```{r overlap-signif-digits, message=F, warning=FALSE, fig.width=12, fig.height=7} -grid.arrange(vis(imm_ov2, .text.size=2.5, .signif.digits=1), vis(imm_ov2, .text.size=2, .signif.digits=2), ncol = 2) +p1 <- vis(imm_ov2, .text.size = 2.5, .signif.digits = 1) +p2 <- vis(imm_ov2, .text.size = 2, .signif.digits = 2) + +p1 + p2 ``` 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zSo3reM6O=HV%0^%QfrTmf`i%YpBI3dEQT9ncV05DlXM_dZ!oDM!eO8!q1BGE1Ma+a zJD>t<`l!f`QU%DxS(S2#L9O4=`OSE-=fCZ Date: Thu, 11 Jun 2020 03:32:50 +0300 Subject: [PATCH 36/54] docs(upkeep): update the docs for bulk data --- man/immdata.Rd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/man/immdata.Rd b/man/immdata.Rd index 5f26d503..b9e2d865 100644 --- a/man/immdata.Rd +++ b/man/immdata.Rd @@ -3,7 +3,7 @@ \docType{data} \name{immdata} \alias{immdata} -\title{Immune repertoire dataset} +\title{Single chain immune repertoire dataset} \format{ A list of two elements. First element ("data") is a list with data frames with clonotype tables. Second element ("meta") is a metadata table. @@ -17,7 +17,7 @@ Second element ("meta") is a metadata table. immdata } \description{ -A dataset with single chain data for testing and examplatory purposes. +A dataset with single chain TCR data for testing and examplatory purposes. } \concept{data} \keyword{datasets} From 89d5f7d2910af3607a26748e9e8dd1b60a9ba3c9 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 11 Jun 2020 03:33:13 +0300 Subject: [PATCH 37/54] docs(single-cell): add docs for single-cell functions --- R/singlecell.R | 3 ++- man/scdata.Rd | 26 ++++++++++++++++++++++++++ man/select_barcodes.Rd | 22 ++++++++++++++-------- man/select_clusters.Rd | 15 +++++++++++++-- 4 files changed, 55 insertions(+), 11 deletions(-) create mode 100644 man/scdata.Rd diff --git a/R/singlecell.R b/R/singlecell.R index 4c88ee75..d9823d7f 100644 --- a/R/singlecell.R +++ b/R/singlecell.R @@ -108,7 +108,8 @@ select_barcodes <- function(.data, .barcodes, .force.list = FALSE) { #' @concept single_cell #' #' @description Given the vector of barcodes from Seurat, split the input repertoires -#' to separate subsets following the barcodes' assigned cluster or sample labels. +#' to separate subsets following the barcodes' assigned IDs. Useful when you want to +#' split immune repertoires by patients or clusters. #' #' @param .data List of two elements "data" and "meta", with "data" being a list of #' immune repertoires, and "meta" being a metadata table. diff --git a/man/scdata.Rd b/man/scdata.Rd new file mode 100644 index 00000000..27b95712 --- /dev/null +++ b/man/scdata.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data_docs.R +\docType{data} +\name{scdata} +\alias{scdata} +\title{Paired chain immune repertoire dataset} +\format{ +A list of four elements. +"data" is a list with data frames with clonotype tables. +"meta" is a metadata table. +"bc_patients" is a list of barcodes corresponding to specific patients. +"bc_clusters" is a list of barcodes corresponding to specific cell clusters. +\describe{ + \item{data}{List of immune repertoire data frames.} + \item{meta}{Metadata} + ... +} +} +\usage{ +scdata +} +\description{ +A dataset with paired chain IG data for testing and examplatory purposes. +} +\concept{data} +\keyword{datasets} diff --git a/man/select_barcodes.Rd b/man/select_barcodes.Rd index d65240f1..eb07d8e5 100644 --- a/man/select_barcodes.Rd +++ b/man/select_barcodes.Rd @@ -4,7 +4,7 @@ \alias{select_barcodes} \title{Select specific clonotypes using barcodes from single-cell metadata} \usage{ -select_barcodes(.data, .barcodes) +select_barcodes(.data, .barcodes, .force.list = FALSE) } \arguments{ \item{.data}{The data to be processed. Can be \link{data.frame}, @@ -19,17 +19,20 @@ of these objects. They are supported with the same limitations as basic objects. Note: each connection must represent a separate repertoire.} -\item{.barcodes}{Character vector. Vector of barcodes to use for -filtering.} +\item{.barcodes}{Either a character vector with barcodes or a named character/factor vector with +barcodes as names and cluster IDs a vector elements. The output of Seurat's \code{Idents} function works.} } \value{ -An immune repertoire or a list of immune repertoires with clonotypes which have -one or more barcodes in the input barcode vector and with clonotype abundances corrected. +An immune repertoire (if ".barcodes" is a barcode vector) or a list of immune repertoires +(if ".barcodes" is named vector or an output from Seurat::Idents()). Each element is an immune repertoire +with clonotype barcodes corresponding to the input barcodes. The output list's names are cluster names +in the ".barcode" argument (Seurat::Idents() case only). } \description{ -Given the input data and a vector of barcodes, -the function returns the input with cells which has -at least one of the given barcode. Columns with clonotype counts +Subset the input immune repertoire by barcodes. Pass a vector of +barcodes to subset or a vector cluster IDs and corresponding barcodes to +get a list of immune repertoires corresponding to cluster IDs. +Columns with clonotype counts and proportions are changed accordingly to the filtered barcodes. } \examples{ @@ -42,4 +45,7 @@ barcodes <- "AAAAACCCCC" df <- select_barcodes(df, barcodes) nrow(df) } +\seealso{ +\link{select_clusters} +} \concept{single_cell} diff --git a/man/select_clusters.Rd b/man/select_clusters.Rd index ca9e1444..27feb878 100644 --- a/man/select_clusters.Rd +++ b/man/select_clusters.Rd @@ -4,7 +4,7 @@ \alias{select_clusters} \title{Split the immune repertoire data to clusters from single-cell barcodes} \usage{ -select_clusters(.data, .clusters) +select_clusters(.data, .clusters, .field = "Cluster") } \arguments{ \item{.data}{List of two elements "data" and "meta", with "data" being a list of @@ -12,10 +12,18 @@ immune repertoires, and "meta" being a metadata table.} \item{.clusters}{Factor vector with barcodes as vector names and cluster IDs as vector elements. The output of the Seurat \code{Idents} function works.} + +\item{.field}{A string specifying the name of the field in the input metadata. New immune +repertoire subsets will have cluster IDs in this field.} +} +\value{ +A list with two elements "data" and "meta" with updated immune repertoire tables and +metadata. } \description{ Given the vector of barcodes from Seurat, split the input repertoires -to separate subsets following the barcodes' assigned cluster or sample labels. +to separate subsets following the barcodes' assigned IDs. Useful when you want to +split immune repertoires by patients or clusters. } \examples{ \dontrun{ @@ -27,4 +35,7 @@ new_cluster_ids pbmc_small <- RenameIdents(pbmc_small, new_cluster_ids) } } +\seealso{ +\link{select_barcodes} +} \concept{single_cell} From 866fd953b5e173ede35a014a5aa159b4ef9d155a Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 11 Jun 2020 03:34:17 +0300 Subject: [PATCH 38/54] feat(data): add new test files for single-cell --- inst/extdata/sc/flu.csv.gz | Bin 0 -> 749048 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 inst/extdata/sc/flu.csv.gz diff --git a/inst/extdata/sc/flu.csv.gz b/inst/extdata/sc/flu.csv.gz new file mode 100644 index 0000000000000000000000000000000000000000..0f32f6dbd1d5ec05727d56d8a6cb59a689bd592a GIT binary patch literal 749048 zcmV)JK)b&miwFoNY~fx417>V>E@N|c0JK}%ZsRr(eebu>fOygEk(FY`jpI5JAwy5W zHgVe&X|~wxw&>rNLsBuvw9FQE7qLyw233#GnJc;PpML-2bx(f!^yTUE^ParCKR)eW 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zwGevTf7d`x9`o}^iT`rv@Mja?F1+iIc(n3n90e=!vv2gg(+AZ1zp(5+9WvnN6ttoX zm%A)vu;#}rSE|=Z2E*a}#4w(?Nn<96+$I2L|ABJvtUXI@O*={2JJBm6xXmbH*Q%g? z$spoDUfd!>ln@y9oa!K7_k;Ml>uV$V%VAPM-uC&z{G}zD&sZi6Sk8-`HJ7Siu*Lo8T5txIOGn-xU$Q-t1B zRw;k01t~M>XnXs*w2INHSE1E>9fl31pLk`0 z_sFiO?*e_9FG(MI3&W@=#uSXhe|{?#6N`Lx*`|J>>#zr;#GUei{b?$;^QQY7o{c{4 r_UF*iInIU)UZ|vOY`JC*u5Z;{sjVy@RIyC>7w9NK@eQ?v;t>A>SDZ#p literal 0 HcmV?d00001 From ed4822946efa0464c59b68342b93b7617616d539 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 11 Jun 2020 03:34:30 +0300 Subject: [PATCH 39/54] feat(io): add a paired chain parsing support for 10X --- R/io.R | 203 +++++++++++++++++++++++++++++-------------------- man/repLoad.Rd | 2 +- 2 files changed, 122 insertions(+), 83 deletions(-) diff --git a/R/io.R b/R/io.R index cb91c706..a15f9b21 100644 --- a/R/io.R +++ b/R/io.R @@ -135,7 +135,7 @@ if (getRversion() >= "2.15.1") { #' # > names(immdata) #' # [1] "data" "meta" #' @export repLoad -repLoad <- function(.path, .format = NA, .coding = TRUE) { +repLoad <- function(.path, .format = NA, .mode = "paired", .coding = TRUE) { if (!is.na(.format)) { warning("Please don't provide the .format argument, immunarch detects the format automatically. @@ -146,7 +146,7 @@ repLoad <- function(.path, .format = NA, .coding = TRUE) { # Process a repertoire file: detect format and load the data # Return: a named list with a repertoire data frame and it's name - .read_repertoire <- function(.path, .format, .coding) { + .read_repertoire <- function(.path, .format, .mode, .coding) { parse_res <- list() # Detect format @@ -183,7 +183,7 @@ repLoad <- function(.path, .format = NA, .coding = TRUE) { message("unknown format, skipping") } else { - parse_res <- parse_fun(.path) + parse_res <- parse_fun(.path, .mode) if (is.null(parse_res)) { message(" [!] Warning: zero clonotypes found, skipping") @@ -209,7 +209,7 @@ repLoad <- function(.path, .format = NA, .coding = TRUE) { # just load all repertoire files. # Do NOT (!) create a dummy metadata, return en empty data frame instead # Return: list with data, metadata and barcodes (if necessary) - .process_batch <- function(.files, .format, .coding) { + .process_batch <- function(.files, .format, .mode, .coding) { parsed_batch <- list() metadata <- tibble() @@ -234,7 +234,7 @@ repLoad <- function(.path, .format = NA, .coding = TRUE) { # TODO: add the barcode processing subroutine to split by samples } else { - repertoire <- .read_repertoire(.filepath, .format, .coding) + repertoire <- .read_repertoire(.filepath, .format, .mode, .coding) if (length(repertoire) != 0) { parsed_batch <- c(parsed_batch, repertoire) } @@ -337,14 +337,14 @@ repLoad <- function(.path, .format = NA, .coding = TRUE) { for (batch_i in 1:length(batches)) { if (length(batches[[batch_i]])) { message('Processing "', names(batches)[batch_i], '" ...') - parsed_batches[[names(batches)[batch_i]]] <- .process_batch(batches[[batch_i]], .format, .coding) + parsed_batches[[names(batches)[batch_i]]] <- .process_batch(batches[[batch_i]], .format, .mode, .coding) } } # # Step 2: check metadata files # - message("\n== Step 2/3: checking metadata files and merging... ==\n") + message("\n== Step 2/3: checking metadata files and merging files... ==\n") for (batch_i in 1:length(parsed_batches)) { message('Processing "', names(parsed_batches)[batch_i], '" ...') @@ -362,7 +362,7 @@ repLoad <- function(.path, .format = NA, .coding = TRUE) { # # Step 3: split by chains and barcodes # - message("\n== Step 3/3: splitting data by barcodes and chains... ==\n") + message("\n== Step 3/3: processing paired chain data... ==\n") # Check for mixed chains repertoire files and split them if necessary rep_names <- names(parsed_batches$data) @@ -376,29 +376,40 @@ repLoad <- function(.path, .format = NA, .coding = TRUE) { } if ("chain" %in% colnames(parsed_batches$data[[source_name]])) { - df_split <- split(parsed_batches$data[[source_name]], parsed_batches$data[[source_name]]$chain) - if (length(df_split) > 1) { - chain_col <- "Chain" - source_col <- "Source" - - if (!(chain_col %in% colnames(metadata))) { - message("Splitting repertoires by their chain type. Columns ", chain_col, " and ", source_col, " added to the metadata.") - message(" -- Column ", chain_col, " specifies chain types: TRA, TRB, etc.") - message(" -- Column ", source_col, " specifies the initial sample name for a repertoire after splitting by chain types") - } + # + # If mode is "single" then split paired chain data to two chain-specific immune repertoires + # + if (.mode == "single") { + df_split <- split(parsed_batches$data[[source_name]], parsed_batches$data[[source_name]]$chain) + if (length(df_split) > 1) { + chain_col <- "Chain" + source_col <- "Source" + + if (!(chain_col %in% colnames(metadata))) { + message("Splitting repertoires by their chain type. Columns ", chain_col, " and ", source_col, " added to the metadata.") + message(" -- Column ", chain_col, " specifies chain types: TRA, TRB, etc.") + message(" -- Column ", source_col, " specifies the initial sample name for a repertoire after splitting by chain types") + } - for (i in 1:length(df_split)) { - sample_name <- paste0(source_name, "_", names(df_split)[i]) - parsed_batches$data[[sample_name]] <- df_split[[i]] - new_record <- metadata[which(metadata$Sample == source_name), ] - new_record$Sample <- sample_name - new_record[[chain_col]] <- names(df_split)[i] - new_record[[source_col]] <- source_name - metadata <- merge(metadata, new_record, all = TRUE) - } + for (i in 1:length(df_split)) { + sample_name <- paste0(source_name, "_", names(df_split)[i]) + parsed_batches$data[[sample_name]] <- df_split[[i]] + new_record <- metadata[which(metadata$Sample == source_name), ] + new_record$Sample <- sample_name + new_record[[chain_col]] <- names(df_split)[i] + new_record[[source_col]] <- source_name + metadata <- merge(metadata, new_record, all = TRUE) + } - parsed_batches$data[[source_name]] <- NULL - metadata <- metadata[-(which(metadata$Sample == source_name)), ] + parsed_batches$data[[source_name]] <- NULL + metadata <- metadata[-(which(metadata$Sample == source_name)), ] + } + } + # + # If mode is "paired" then nothing to do here + # + else { + # pass } } } @@ -657,7 +668,7 @@ repSave <- function(.data, .path, .format = c("immunarch", "vdjtools"), } } -.postprocess <- function(.data) { +.postprocess <- function(.data, .mode) { .data[[IMMCOL$cdr3nt]][.data[[IMMCOL$cdr3nt]] == "NONE"] <- NA logic <- is.na(.data[[IMMCOL$cdr3aa]]) & !is.na(.data[[IMMCOL$cdr3nt]]) if (any(logic)) { @@ -668,39 +679,11 @@ repSave <- function(.data, .path, .format = c("immunarch", "vdjtools"), if (any(logic)) { warn_msg <- c(" [!] Removed ", sum(logic)) warn_msg <- c(warn_msg, " clonotypes with no nucleotide and amino acid CDR3 sequence.") - # warn_msg = c(warn_msg, "\n Please check if your files are the final repertoire files, not the intermediate ones before filtering out bad clonotypes.\n") message(warn_msg) } .data <- .data[!logic, ] if (nrow(.data)) { - # Process 10xGenomics filtered contigs files - count barcodes, merge consensues ids, clonotype ids and contig ids - if (all(c("contig_id", "barcode") %in% names(.data))) { - .data <- .data %>% - group_by(CDR3.nt, V.name, J.name) %>% - summarise( - Clones = length(unique(barcode)), - CDR3.aa = head(CDR3.aa, 1), - D.name = head(D.name, 1), - Sequence = head(CDR3.nt, 1), - V.end = head(V.end, 1), - J.start = head(J.start, 1), - D.end = head(D.end, 1), - D.start = head(D.start, 1), - VD.ins = head(VD.ins, 1), - DJ.ins = head(DJ.ins, 1), - VJ.ins = head(VJ.ins, 1), - chain = head(chain, 1), - barcode = paste0(unique(barcode), collapse = IMMCOL_ADD$scsep), - # raw_clonotype_id = gsub("clonotype", "", paste0(raw_clonotype_id, collapse = IMMCOL_ADD$scsep)), - # raw_consensus_id = gsub("clonotype|consensus", "", paste0(raw_consensus_id, collapse = IMMCOL_ADD$scsep)), - contig_id = paste0(contig_id, collapse = IMMCOL_ADD$scsep) - ) %>% - ungroup() - .data[[IMMCOL$prop]] <- .data[[IMMCOL$count]] / sum(.data[[IMMCOL$count]]) - setcolorder(.data, IMMCOL$order) - } - for (colname in c(IMMCOL$ve, IMMCOL$ds, IMMCOL$de, IMMCOL$js, IMMCOL$vnj, IMMCOL$vnd, IMMCOL$dnj)) { if (colname %in% colnames(.data)) { logic <- is.na(.data[[colname]]) @@ -736,7 +719,7 @@ repSave <- function(.data, .path, .format = c("immunarch", "vdjtools"), ##### Parsers ##### -parse_repertoire <- function(.filename, .nuc.seq, .aa.seq, .count, +parse_repertoire <- function(.filename, .mode, .nuc.seq, .aa.seq, .count, .vgenes, .jgenes, .dgenes, .vend, .jstart, .dstart, .dend, .vd.insertions, .dj.insertions, .total.insertions, @@ -900,10 +883,10 @@ parse_repertoire <- function(.filename, .nuc.seq, .aa.seq, .count, colnames(df)[13] <- IMMCOL$vnd colnames(df)[14] <- IMMCOL$dnj - .postprocess(df) + .postprocess(df, .mode) } -parse_immunoseq <- function(.filename, .wash.alleles = TRUE) { +parse_immunoseq <- function(.filename, .mode, .wash.alleles = TRUE) { .fix.immunoseq.genes <- function(.col) { # fix "," .col <- gsub(",", ", ", .col, fixed = TRUE, useBytes = TRUE) @@ -1060,7 +1043,7 @@ parse_immunoseq <- function(.filename, .wash.alleles = TRUE) { } } -parse_mitcr <- function(.filename) { +parse_mitcr <- function(.filename, .mode) { .skip <- 0 f <- file(.filename, "r") l <- readLines(f, 1) @@ -1118,14 +1101,14 @@ parse_mitcr <- function(.filename) { } parse_repertoire( - .filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, + .filename = filename, .mode = .mode, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dstart = dstart, .dend = dend, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep ) } -parse_mixcr <- function(.filename) { +parse_mixcr <- function(.filename, .mode) { fix.alleles <- function(.data) { .data[[IMMCOL$v]] <- gsub("[*][[:digit:]]*", "", .data[[IMMCOL$v]]) .data[[IMMCOL$d]] <- gsub("[*][[:digit:]]*", "", .data[[IMMCOL$d]]) @@ -1392,7 +1375,7 @@ parse_mixcr <- function(.filename) { .postprocess(fix.alleles(df)) } -parse_migec <- function(.filename) { +parse_migec <- function(.filename, .mode) { filename <- .filename nuc.seq <- "CDR3 nucleotide sequence" aa.seq <- "CDR3 amino acid sequence" @@ -1411,7 +1394,7 @@ parse_migec <- function(.filename) { .sep <- "\t" parse_repertoire( - .filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, + .filename = filename, .mode = .mode, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dstart = dstart, .dend = dend, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, @@ -1419,7 +1402,7 @@ parse_migec <- function(.filename) { ) } -parse_migmap <- function(.filename) { +parse_migmap <- function(.filename, .mode) { filename <- .filename nuc.seq <- "cdr3nt" aa.seq <- "cdr3aa" @@ -1438,14 +1421,14 @@ parse_migmap <- function(.filename) { .sep <- "\t" parse_repertoire( - .filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, + .filename = filename, .mode = .mode, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dstart = dstart, .dend = dend, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep ) } -parse_tcr <- function(.filename) { +parse_tcr <- function(.filename, .mode) { f <- file(.filename, "r") l <- readLines(f, 2)[2] close(f) @@ -1471,14 +1454,14 @@ parse_tcr <- function(.filename) { } parse_repertoire( - .filename = .filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, + .filename = .filename, .mode = .mode, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dstart = dstart, .dend = dend, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep ) } -parse_vdjtools <- function(.filename) { +parse_vdjtools <- function(.filename, .mode) { skip <- 0 # Check for different VDJtools outputs @@ -1528,14 +1511,14 @@ parse_vdjtools <- function(.filename) { } parse_repertoire( - .filename = filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, + .filename = filename, .mode = .mode, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dstart = dstart, .dend = dend, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep ) } -parse_imgt <- function(.filename) { +parse_imgt <- function(.filename, .mode) { .fix.imgt.alleles <- function(.col) { sapply(strsplit(.col, " "), function(x) { if (length(x) > 1) { @@ -1568,7 +1551,7 @@ parse_imgt <- function(.filename) { junc_start <- .make_names("JUNCTION start") df <- parse_repertoire( - .filename = .filename, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, + .filename = .filename, .mode = .mode, .nuc.seq = nuc.seq, .aa.seq = aa.seq, .count = .count, .vgenes = vgenes, .jgenes = jgenes, .dgenes = dgenes, .vend = vend, .jstart = jstart, .dstart = dstart, .dend = dend, .vd.insertions = vd.insertions, .dj.insertions = dj.insertions, .total.insertions = total.insertions, .skip = .skip, .sep = .sep, .add = junc_start @@ -1605,7 +1588,7 @@ parse_imgt <- function(.filename) { # stop(IMMUNR_ERROR_NOT_IMPL) # } -parse_airr <- function(.filename) { +parse_airr <- function(.filename, .mode) { df <- airr::read_rearrangement(.filename) df <- df %>% @@ -1656,7 +1639,7 @@ parse_airr <- function(.filename) { df } -parse_immunarch <- function(.filename) { +parse_immunarch <- function(.filename, .mode) { df <- readr::read_tsv(.filename, col_types = cols(), comment = "#") if (ncol(df) == 1) { # "," in the files, parse differently then @@ -1666,8 +1649,9 @@ parse_immunarch <- function(.filename) { df } -parse_10x_consensus <- function(.filename) { +parse_10x_consensus <- function(.filename, .mode) { df <- parse_repertoire(.filename, + .mode = .mode, .nuc.seq = "cdr3_nt", .aa.seq = NA, .count = "umis", .vgenes = "v_gene", .jgenes = "j_gene", .dgenes = "d_gene", .vend = NA, .jstart = NA, .dstart = NA, .dend = NA, @@ -1679,24 +1663,79 @@ parse_10x_consensus <- function(.filename) { df } -parse_10x_filt_contigs <- function(.filename) { +parse_10x_filt_contigs <- function(.filename, .mode) { df <- parse_repertoire(.filename, + .mode = .mode, .nuc.seq = "cdr3_nt", .aa.seq = NA, .count = "umis", .vgenes = "v_gene", .jgenes = "j_gene", .dgenes = "d_gene", .vend = NA, .jstart = NA, .dstart = NA, .dend = NA, .vd.insertions = NA, .dj.insertions = NA, .total.insertions = NA, .skip = 0, .sep = ",", # .add = c("chain", "raw_clonotype_id", "raw_consensus_id", "barcode", "contig_id") - .add = c("chain", "barcode", "contig_id") + .add = c("chain", "barcode", "raw_clonotype_id", "contig_id") ) - setnames(df, "contig_id", "ContigID") # setnames(df, "raw_clonotype_id", "RawClonotypeID") # setnames(df, "raw_consensus_id", "RawConsensusID") + + # Process 10xGenomics filtered contigs files - count barcodes, merge consensues ids, clonotype ids and contig ids + df <- df[order(df$chain),] + setDT(df) + + if (.mode == "paired") { + df <- df %>% + lazy_dt() %>% + group_by(barcode, raw_clonotype_id) %>% + summarise( + CDR3.nt = paste0(CDR3.nt, collapse = IMMCOL_ADD$scsep), + CDR3.aa = paste0(CDR3.aa, collapse = IMMCOL_ADD$scsep), + V.name = paste0(V.name, collapse = IMMCOL_ADD$scsep), + J.name = paste0(J.name, collapse = IMMCOL_ADD$scsep), + D.name = paste0(D.name, collapse = IMMCOL_ADD$scsep), + chain = paste0(chain, collapse = IMMCOL_ADD$scsep), + # raw_clonotype_id = gsub("clonotype", "", paste0(raw_clonotype_id, collapse = IMMCOL_ADD$scsep)), + # raw_consensus_id = gsub("clonotype|consensus", "", paste0(raw_consensus_id, collapse = IMMCOL_ADD$scsep)), + contig_id = gsub("_contig_", "", paste0(contig_id, collapse = IMMCOL_ADD$scsep)) + ) %>% + as.data.table() + } + df <- df %>% + lazy_dt() %>% + group_by(CDR3.nt, V.name, J.name) %>% + summarise( + Clones = length(unique(barcode)), + CDR3.aa = first(CDR3.aa), + D.name = first(D.name), + chain = first(chain), + barcode = paste0(unique(barcode), collapse = IMMCOL_ADD$scsep), + raw_clonotype_id = gsub("clonotype|None", "", paste0(unique(raw_clonotype_id), collapse = IMMCOL_ADD$scsep)), + # raw_clonotype_id = gsub("clonotype", "", paste0(raw_clonotype_id, collapse = IMMCOL_ADD$scsep)), + # raw_consensus_id = gsub("clonotype|consensus", "", paste0(raw_consensus_id, collapse = IMMCOL_ADD$scsep)), + contig_id = paste0(contig_id, collapse = IMMCOL_ADD$scsep) + ) %>% + as.data.table() + + df$V.end <- NA + df$J.start <- NA + df$D.end <- NA + df$D.start <- NA + df$VD.ins <- NA + df$DJ.ins <- NA + df$VJ.ins <- NA + df$Sequence <- df$CDR3.nt + + setnames(df, "contig_id", "ContigID") setnames(df, "barcode", "Barcode") - df + + df[[IMMCOL$prop]] <- df[[IMMCOL$count]] / sum(df[[IMMCOL$count]]) + setcolorder(df, IMMCOL$order) + + setDF(df) + + .postprocess(df) } -parse_archer <- function(.filename) { +parse_archer <- function(.filename, .mode) { parse_repertoire(.filename, + .mode = .mode, .nuc.seq = "Clonotype Sequence", .aa.seq = NA, .count = "Clone Abundance", .vgenes = "Predicted V Region", .jgenes = "Predicted J Region", .dgenes = "Predicted D Region", .vend = NA, .jstart = NA, .dstart = NA, .dend = NA, diff --git a/man/repLoad.Rd b/man/repLoad.Rd index d14944ed..47e0cc07 100644 --- a/man/repLoad.Rd +++ b/man/repLoad.Rd @@ -4,7 +4,7 @@ \alias{repLoad} \title{Load immune repertoire files into the R workspace} \usage{ -repLoad(.path, .format = NA, .coding = TRUE) +repLoad(.path, .format = NA, .mode = "paired", .coding = TRUE) } \arguments{ \item{.path}{A character string specifying the path to the input data. From 7cd27266c25b534228eaa53d5c44c4188a2e4705 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 11 Jun 2020 03:34:47 +0300 Subject: [PATCH 40/54] refactor(upkeep): add "single-cell" and "data" commit scopes --- CONTRIBUTION.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/CONTRIBUTION.md b/CONTRIBUTION.md index 66de9889..54a68a67 100644 --- a/CONTRIBUTION.md +++ b/CONTRIBUTION.md @@ -66,7 +66,7 @@ Commit name examples: `feat(diversity): added the Chao1 method for diversity est #### Commit scopes -- Changes in analysis- and visualisation-specific functions: `diversity`, `overlap`, `pub-rep`, `clonality`, `gene-usage`, `explore`, `kmers`, `spectratype`, `dynamics`, `tools` +- Changes in analysis- and visualisation-specific functions: `diversity`, `overlap`, `pub-rep`, `clonality`, `gene-usage`, `explore`, `kmers`, `spectratype`, `dynamics`, `tools`, `single-cell`. - General changes in visualisation functions (e.g., replace one package with another, or change a non-specific visualisation function such as `vis_bar`): `vis` @@ -76,6 +76,8 @@ Commit name examples: `feat(diversity): added the Chao1 method for diversity est - Changes in additional functions such as general statistics functions: `utility` +- Changes in data: `data` + - Changes in NAMESPACE, DESCRIPTION, citations, ISSUE_TEMPLATE.md, etc., without README: `upkeep` - Changes in README and vignettes: `vignette` From 550517d561dbcbc88b9d7f1a9dc55deeb1faa6e3 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 11 Jun 2020 03:35:02 +0300 Subject: [PATCH 41/54] feat(vignette): add a draft for the single-cell vignette --- vignettes/web_only/v21_singlecell.Rmd | 102 ++++++++++++++++++++++++++ 1 file changed, 102 insertions(+) create mode 100644 vignettes/web_only/v21_singlecell.Rmd diff --git a/vignettes/web_only/v21_singlecell.Rmd b/vignettes/web_only/v21_singlecell.Rmd new file mode 100644 index 00000000..414bfbab --- /dev/null +++ b/vignettes/web_only/v21_singlecell.Rmd @@ -0,0 +1,102 @@ +--- +title: "Single-cell and paired chain data: scTCRseq and scBCRseq exploration" +author: 'ImmunoMind' +date: "support@immunomind.io" +output: + html_document: + fig_height: 8 + fig_width: 10 + theme: spacelab + toc: yes + pdf_document: + toc: yes + word_document: + toc: yes +--- + + + + + + +```{r setup, include=FALSE, echo=FALSE} +# knitr::knit_hooks$set(optipng = knitr::hook_optipng) +# knitr::opts_chunk$set(optipng = '-o7') + +knitr::opts_chunk$set(echo = TRUE) +knitr::opts_chunk$set(fig.align = "center") +knitr::opts_chunk$set(fig.width = 12) +knitr::opts_chunk$set(fig.height = 6) + +library(immunarch) +data(scdata) +``` + + +# Executive Summary + +> This is a vignette dedicated to provide an overview on how to work with single-cell paired chain data in `immunarch` + +> Single-cell support is currently in the development version. In order to access it, you need to install the latest development version of the package by executing the following command: + +```r +install.packages("devtools"); devtools::install_github("immunomind/immunarch", ref="dev") +``` + +> To read paired chain data into `immunarch` use the `repLoad` function with `.mode = "paired"`. Currently we support 10X Genomics only. + +> To subset immune repertoires by specific barcodes use the `select_barcodes` function. Output of `Seurat::Idents()` as a barcode vector works. + +> To create cluster-specific and patient-specific datasets using barcodes from the output of `Seurat::Idents()` use the `select_clusters` function. + + +# Use the data packaged with `immunarch` + +Load the package into the R enviroment: +```r +library(immunarch) +``` + +For testing purposes we attached a new paired chain dataset to `immunarch`. Load it by executing the following command: + +```r +data(scdata) +``` + +# Load the paired chain data + +To load your own datasets, use the `repLoad` function. Currently we implemented paired chain data support for 10X Genomics data only. A working example of loading datasets into R: + +```{r} +file_path <- paste0(system.file(package = "immunarch"), "/extdata/sc/flu.csv.gz") +igdata <- repLoad(file_path, .mode = "paired") + +igdata$meta + +head(igdata$data[[1]][c(1:7, 16, 17)]) +``` + +# Subset by barcodes + +To subset the data by barcodes, use the `select_barcodes` function. + +## Patient-specific datasets + +To create a new dataset with cluster-specific immune repertoires, use the `select_clusters` function: + +```r +``` + +## Cluster-specific datasets + +To create a new dataset with cluster-specific immune repertoires, use the `select_clusters` function. You can apply this function after you created patient-specific datasets. + +```r +``` + +# Explore and compute statistics + From beddde3bf11571fd9c4d4cc11e0bd18812b05cac Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 11 Jun 2020 16:23:42 +0300 Subject: [PATCH 42/54] feat(vignette): finish the single-cell vignette --- vignettes/web_only/v21_singlecell.Rmd | 39 +++++++++++++++++++++++---- 1 file changed, 34 insertions(+), 5 deletions(-) diff --git a/vignettes/web_only/v21_singlecell.Rmd b/vignettes/web_only/v21_singlecell.Rmd index 414bfbab..07ecefde 100644 --- a/vignettes/web_only/v21_singlecell.Rmd +++ b/vignettes/web_only/v21_singlecell.Rmd @@ -57,13 +57,13 @@ install.packages("devtools"); devtools::install_github("immunomind/immunarch", r # Use the data packaged with `immunarch` Load the package into the R enviroment: -```r +```{r} library(immunarch) ``` For testing purposes we attached a new paired chain dataset to `immunarch`. Load it by executing the following command: -```r +```{r} data(scdata) ``` @@ -84,19 +84,48 @@ head(igdata$data[[1]][c(1:7, 16, 17)]) To subset the data by barcodes, use the `select_barcodes` function. +```{r} +barcodes <- c("AGTAGTCAGTGTACTC-1", "GGCGACTGTACCGAGA-1", "TTGAACGGTCACCTAA-1") + +new_df <- select_barcodes(scdata$data[[1]], barcodes) + +new_df +``` + ## Patient-specific datasets To create a new dataset with cluster-specific immune repertoires, use the `select_clusters` function: -```r +```{r} +scdata_pat <- select_clusters(scdata, scdata$bc_patient, "Patient") + +names(scdata_pat$data) + +scdata_pat$meta ``` ## Cluster-specific datasets -To create a new dataset with cluster-specific immune repertoires, use the `select_clusters` function. You can apply this function after you created patient-specific datasets. +To create a new dataset with cluster-specific immune repertoires, use the `select_clusters` function. You can apply this function after you created patient-specific datasets to get patient-specific cell cluster-specific immune repertoires, e.g., a Memory B Cell repertoire for a specific patient: -```r +```{r} +scdata_cl <- select_clusters(scdata_pat, scdata$bc_cluster, "Cluster") + +names(scdata_cl$data) + +scdata_cl$meta ``` # Explore and compute statistics +Most functions will work out-of-the-box with paired chain data. + +```{r} +p1 <- repOverlap(scdata_cl$data) %>% vis() +p2 <- repDiversity(scdata_cl$data) %>% vis() + +p1 + p2 +``` + +Several functions may work incorrectly with paired chain data in this release of `immunarch`. Let us know via [GitHub Issues](http://github.com/immunomind/immunarch/issues)! + From aa5e9efa47f3a6e5eec19bc49fc0fe0d17135889 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 11 Jun 2020 16:23:59 +0300 Subject: [PATCH 43/54] docs(single-cell): add docs for select_barcodes --- man/select_barcodes.Rd | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/man/select_barcodes.Rd b/man/select_barcodes.Rd index eb07d8e5..95f884dd 100644 --- a/man/select_barcodes.Rd +++ b/man/select_barcodes.Rd @@ -21,6 +21,8 @@ Note: each connection must represent a separate repertoire.} \item{.barcodes}{Either a character vector with barcodes or a named character/factor vector with barcodes as names and cluster IDs a vector elements. The output of Seurat's \code{Idents} function works.} + +\item{.force.list}{Logical. If TRUE then always return a list, even if the result is one data frame.} } \value{ An immune repertoire (if ".barcodes" is a barcode vector) or a list of immune repertoires @@ -36,6 +38,7 @@ Columns with clonotype counts and proportions are changed accordingly to the filtered barcodes. } \examples{ +\dontrun{ data(immdata) # Create a fake single-cell data df <- immdata$data[[1]] @@ -45,6 +48,7 @@ barcodes <- "AAAAACCCCC" df <- select_barcodes(df, barcodes) nrow(df) } +} \seealso{ \link{select_clusters} } From a1eea1f44d8080d99afb354bb548c1438d001990 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 11 Jun 2020 16:24:17 +0300 Subject: [PATCH 44/54] docs(io): update docs for repLoad / repSave to reflect single-cell support --- man/repLoad.Rd | 6 ++++++ man/repSave.Rd | 6 +++--- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/man/repLoad.Rd b/man/repLoad.Rd index 47e0cc07..1bbc3ce3 100644 --- a/man/repLoad.Rd +++ b/man/repLoad.Rd @@ -30,6 +30,12 @@ cell and chain types for different samples.} Leave NA (which is default) if you want `immunarch` to detect formats automatically.} +\item{.mode}{Either "single" for single chain data or "paired" for paired chain data. + +Currently "single" works for every format, and "paired" works only for 10X Genomics data. + +By default, 10X Genomics data will be loaded as paired chain data, and other files will be loaded as single chain data.} + \item{.coding}{A logical value. Pass TRUE to get coding-only clonotypes (by defaul). Pass FALSE to get all clonotypes.} } \value{ diff --git a/man/repSave.Rd b/man/repSave.Rd index 88a8af8a..d5d9dadc 100644 --- a/man/repSave.Rd +++ b/man/repSave.Rd @@ -37,8 +37,8 @@ dirpath <- tempdir() repSave(immdata, dirpath) # Load it and check if it is the same new_immdata <- repLoad(dirpath) -sum(immdata$data[[1]] != new_immdata$data[[1]], na.rm = TRUE) -sum(immdata$data[[2]] != new_immdata$data[[2]], na.rm = TRUE) -sum(immdata$meta != new_immdata$meta, na.rm = TRUE) +# sum(immdata$data[[1]] != new_immdata$data[[1]], na.rm = TRUE) +# sum(immdata$data[[2]] != new_immdata$data[[2]], na.rm = TRUE) +# sum(immdata$meta != new_immdata$meta, na.rm = TRUE) } \concept{io} From ae24dacfde3998b91e5d893ef750042f82068da0 Mon Sep 17 00:00:00 2001 From: Vadim Nazarov Date: Thu, 11 Jun 2020 16:24:30 +0300 Subject: [PATCH 45/54] feat(data): add the "scdata" dataset with paired chain data and barcodes --- data/scdata.rda | Bin 1917344 -> 2155676 bytes 1 file changed, 0 insertions(+), 0 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