From 43963733fa0f45e3bd5d56d50c386a15428a671d Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Wed, 2 Oct 2024 17:15:17 +0000 Subject: [PATCH 01/18] init module skeleton --- .../workflows/docker_cell-type-ETP-ALL-03.yml | 63 + .../workflows/run_cell-type-ETP-ALL-03.yml | 62 + analyses/cell-type-ETP-ALL-03/.Rprofile | 4 + analyses/cell-type-ETP-ALL-03/.dockerignore | 8 + analyses/cell-type-ETP-ALL-03/.gitignore | 7 + analyses/cell-type-ETP-ALL-03/Dockerfile | 10 + analyses/cell-type-ETP-ALL-03/README.md | 45 + .../cell-type-ETP-ALL-03.Rproj | 13 + .../components/dependencies.R | 2 + analyses/cell-type-ETP-ALL-03/conda-lock.yml | 7115 +++++++++++++++++ analyses/cell-type-ETP-ALL-03/environment.yml | 22 + analyses/cell-type-ETP-ALL-03/plots/.gitkeep | 0 analyses/cell-type-ETP-ALL-03/renv.lock | 2586 ++++++ analyses/cell-type-ETP-ALL-03/renv/.gitignore | 7 + analyses/cell-type-ETP-ALL-03/renv/activate.R | 1220 +++ .../cell-type-ETP-ALL-03/renv/settings.json | 5 + .../cell-type-ETP-ALL-03/results/README.md | 5 + .../cell-type-ETP-ALL-03/scratch/.gitkeep | 0 .../cell-type-ETP-ALL-03/scripts/.gitkeep | 0 .../scripts/00-01_processing_rds.R | 131 + .../scripts/02-03_annotation.R | 167 + 21 files changed, 11472 insertions(+) create mode 100644 .github/workflows/docker_cell-type-ETP-ALL-03.yml create mode 100644 .github/workflows/run_cell-type-ETP-ALL-03.yml create mode 100644 analyses/cell-type-ETP-ALL-03/.Rprofile create mode 100644 analyses/cell-type-ETP-ALL-03/.dockerignore create mode 100644 analyses/cell-type-ETP-ALL-03/.gitignore create mode 100644 analyses/cell-type-ETP-ALL-03/Dockerfile create mode 100644 analyses/cell-type-ETP-ALL-03/README.md create mode 100644 analyses/cell-type-ETP-ALL-03/cell-type-ETP-ALL-03.Rproj create mode 100644 analyses/cell-type-ETP-ALL-03/components/dependencies.R create mode 100644 analyses/cell-type-ETP-ALL-03/conda-lock.yml create mode 100644 analyses/cell-type-ETP-ALL-03/environment.yml create mode 100644 analyses/cell-type-ETP-ALL-03/plots/.gitkeep create mode 100644 analyses/cell-type-ETP-ALL-03/renv.lock create mode 100644 analyses/cell-type-ETP-ALL-03/renv/.gitignore create mode 100644 analyses/cell-type-ETP-ALL-03/renv/activate.R create mode 100644 analyses/cell-type-ETP-ALL-03/renv/settings.json create mode 100644 analyses/cell-type-ETP-ALL-03/results/README.md create mode 100644 analyses/cell-type-ETP-ALL-03/scratch/.gitkeep create mode 100644 analyses/cell-type-ETP-ALL-03/scripts/.gitkeep create mode 100644 analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R create mode 100644 analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R diff --git a/.github/workflows/docker_cell-type-ETP-ALL-03.yml b/.github/workflows/docker_cell-type-ETP-ALL-03.yml new file mode 100644 index 000000000..05885bbb4 --- /dev/null +++ b/.github/workflows/docker_cell-type-ETP-ALL-03.yml @@ -0,0 +1,63 @@ +# This is a workflow to build the docker image for the cell-type-ETP-ALL-03 module +# +# Docker modules are run on pull requests when code for files that affect the Docker image have changed. +# If other files are used during the Docker build, they should be added to `paths` +# +# At module initialization, this workflow is inactive, and needs to be activated manually + +name: Build docker image for cell-type-ETP-ALL-03 + +concurrency: + # only one run per branch at a time + group: "docker_cell-type-ETP-ALL-03_${{ github.ref }}" + cancel-in-progress: true + +on: + # pull_request: + # branches: + # - main + # paths: + # - "analyses/cell-type-ETP-ALL-03/Dockerfile" + # - "analyses/cell-type-ETP-ALL-03/.dockerignore" + # - "analyses/cell-type-ETP-ALL-03/renv.lock" + # - "analyses/cell-type-ETP-ALL-03/conda-lock.yml" + # push: + # branches: + # - main + # paths: + # - "analyses/cell-type-ETP-ALL-03/Dockerfile" + # - "analyses/cell-type-ETP-ALL-03/.dockerignore" + # - "analyses/cell-type-ETP-ALL-03/renv.lock" + # - "analyses/cell-type-ETP-ALL-03/conda-lock.yml" + workflow_dispatch: + inputs: + push-ecr: + description: "Push to AWS ECR" + type: boolean + required: true + +jobs: + test-build: + name: Test Build Docker Image + if: github.event_name == 'pull_request' || (contains(github.event_name, 'workflow_') && !inputs.push-ecr) + runs-on: ubuntu-latest + + steps: + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Build image + uses: docker/build-push-action@v5 + with: + context: "{{defaultContext}}:analyses/cell-type-ETP-ALL-03" + push: false + cache-from: type=gha + cache-to: type=gha,mode=max + + build-push: + name: Build and Push Docker Image + if: github.repository_owner == 'AlexsLemonade' && (github.event_name == 'push' || inputs.push-ecr) + uses: ./.github/workflows/build-push-docker-module.yml + with: + module: "cell-type-ETP-ALL-03" + push-ecr: true diff --git a/.github/workflows/run_cell-type-ETP-ALL-03.yml b/.github/workflows/run_cell-type-ETP-ALL-03.yml new file mode 100644 index 000000000..25c425624 --- /dev/null +++ b/.github/workflows/run_cell-type-ETP-ALL-03.yml @@ -0,0 +1,62 @@ +# This is a workflow to run the cell-type-ETP-ALL-03 module +# +# Analysis modules are run based on three triggers: +# - Manual trigger +# - On pull requests where code in the module has changed +# - As a reusable workflow called from a separate workflow which periodically runs all modules +# +# At initialization, only the manual trigger is active + +name: Run cell-type-ETP-ALL-03 analysis module +env: + MODULE_PATH: analyses/cell-type-ETP-ALL-03 + AWS_DEFAULT_REGION: us-east-2 + +concurrency: + # only one run per branch at a time + group: "run_cell-type-ETP-ALL-03_${{ github.ref }}" + cancel-in-progress: true + +on: + workflow_dispatch: + # workflow_call: + # pull_request: + # branches: + # - main + # paths: + # - analyses/cell-type-ETP-ALL-03/** + # - "!analyses/cell-type-ETP-ALL-03/Dockerfile" + # - "!analyses/cell-type-ETP-ALL-03/.dockerignore" + # - .github/workflows/run_cell-type-ETP-ALL-03.yml + +jobs: + run-module: + if: github.repository_owner == 'AlexsLemonade' + runs-on: ubuntu-latest + + steps: + - name: Checkout repo + uses: actions/checkout@v4 + + - name: Set up R + uses: r-lib/actions/setup-r@v2 + with: + r-version: 4.4.0 + use-public-rspm: true + + - name: Set up pandoc + uses: r-lib/actions/setup-pandoc@v2 + + - name: Set up renv + uses: r-lib/actions/setup-renv@v2 + with: + working-directory: ${{ env.MODULE_PATH }} + + # Update this step as needed to download the desired data + - name: Download test data + run: ./download-data.py --test-data --format SCE + + - name: Run analysis module + run: | + cd ${MODULE_PATH} + # run module script(s) here diff --git a/analyses/cell-type-ETP-ALL-03/.Rprofile b/analyses/cell-type-ETP-ALL-03/.Rprofile new file mode 100644 index 000000000..7eae9e102 --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/.Rprofile @@ -0,0 +1,4 @@ +# Don't activate renv in an OpenScPCA docker image +if (Sys.getenv('OPENSCPCA_DOCKER') != 'TRUE') { + source('renv/activate.R') +} diff --git a/analyses/cell-type-ETP-ALL-03/.dockerignore b/analyses/cell-type-ETP-ALL-03/.dockerignore new file mode 100644 index 000000000..23c79ccae --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/.dockerignore @@ -0,0 +1,8 @@ +# Ignore everything by default +* + +# Include specific files in the docker environment +!/renv.lock +!/requirements.txt +!/environment.yml +!/conda-lock.yml diff --git a/analyses/cell-type-ETP-ALL-03/.gitignore b/analyses/cell-type-ETP-ALL-03/.gitignore new file mode 100644 index 000000000..418755f52 --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/.gitignore @@ -0,0 +1,7 @@ +# Results should not be committed +/results/* +!/results/README.md + +# Ignore the scratch directory (but keep it present) +/scratch/* +!/scratch/.gitkeep diff --git a/analyses/cell-type-ETP-ALL-03/Dockerfile b/analyses/cell-type-ETP-ALL-03/Dockerfile new file mode 100644 index 000000000..80010ba65 --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/Dockerfile @@ -0,0 +1,10 @@ +# A template docker file for creating a new analysis +FROM ubuntu:22.04 + +# Labels following the Open Containers Initiative (OCI) recommendations +# For more information, see https://specs.opencontainers.org/image-spec/annotations/?v=v1.0.1 +LABEL org.opencontainers.image.authors="OpenScPCA scpca@ccdatalab.org" +LABEL org.opencontainers.image.source="https://github.com/AlexsLemonade/OpenScPCA-analysis/tree/main/templates/analysis-module" + +# Set an environment variable to allow checking if we are in an OpenScPCA container +ENV OPENSCPCA_DOCKER=TRUE diff --git a/analyses/cell-type-ETP-ALL-03/README.md b/analyses/cell-type-ETP-ALL-03/README.md new file mode 100644 index 000000000..638a0a4fc --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/README.md @@ -0,0 +1,45 @@ +# Template analysis module + +This is a template analysis module. +It is intended to be used as a starting point for new analysis modules. +Please fill in the content below with information specific to this analysis module. + +## Description + +Please provide a description of your module, including: + +- What type of analysis is included? +- What methods or tools are used? + +If there are multiple steps in the module, please include an outline of the analysis steps and scripts used. + +## Usage + +Please provide instructions on how to run the analysis module. +What commands are needed to execute all steps in the analysis? + +## Input files + +Please include a description of the required inputs to run the analysis module (e.g., processed `SingleCellExperiment` objects from the ScPCA Portal). +If the input to this module is dependent on running any other scripts (e.g., `download-data.py`) or on the output of another module, please state that. +If possible, include examples of specific commands used to obtain data. + +## Output files + +Please include a description of the output from your analysis, including: + +- What type of files are created? +- What are the contents of the output files? +- Where are the files stored? +- Are any intermediate files generated? +If so, where are they stored? + +## Software requirements + +Please describe the environment system used to manage software dependencies for the module (e.g., `conda`, `renv`, `docker`). +Include the locations of any files used to set up the environment. + +## Computational resources + +Please indicate the computational resources that are required to run the module. +If there are specific memory, CPU, and/or GPU requirements, please state that. diff --git a/analyses/cell-type-ETP-ALL-03/cell-type-ETP-ALL-03.Rproj b/analyses/cell-type-ETP-ALL-03/cell-type-ETP-ALL-03.Rproj new file mode 100644 index 000000000..8e3c2ebc9 --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/cell-type-ETP-ALL-03.Rproj @@ -0,0 +1,13 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX diff --git a/analyses/cell-type-ETP-ALL-03/components/dependencies.R b/analyses/cell-type-ETP-ALL-03/components/dependencies.R new file mode 100644 index 000000000..ae80dc0ac --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/components/dependencies.R @@ -0,0 +1,2 @@ +# R dependencies not captured by `renv` +# library("missing_package") diff --git a/analyses/cell-type-ETP-ALL-03/conda-lock.yml b/analyses/cell-type-ETP-ALL-03/conda-lock.yml new file mode 100644 index 000000000..bc4edee0a --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/conda-lock.yml @@ -0,0 +1,7115 @@ +# This lock file was generated by conda-lock (https://github.com/conda/conda-lock). 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"Hash": "51dab85c6c98e50a18d7551e9d49f76c" + }, + "zip": { + "Package": "zip", + "Version": "2.3.1", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "fcc4bd8e6da2d2011eb64a5e5cc685ab" + }, + "zlibbioc": { + "Package": "zlibbioc", + "Version": "1.50.0", + "Source": "Bioconductor", + "Repository": "Bioconductor 3.19", + "Hash": "3db02e3c460e1c852365df117a2b441b" + }, + "zoo": { + "Package": "zoo", + "Version": "1.8-12", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "grDevices", + "graphics", + "lattice", + "stats", + "utils" + ], + "Hash": "5c715954112b45499fb1dadc6ee6ee3e" + } + } +} diff --git a/analyses/cell-type-ETP-ALL-03/renv/.gitignore b/analyses/cell-type-ETP-ALL-03/renv/.gitignore new file mode 100644 index 000000000..0ec0cbba2 --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/renv/.gitignore @@ -0,0 +1,7 @@ +library/ +local/ +cellar/ +lock/ +python/ +sandbox/ +staging/ diff --git a/analyses/cell-type-ETP-ALL-03/renv/activate.R b/analyses/cell-type-ETP-ALL-03/renv/activate.R new file mode 100644 index 000000000..d13f9932a --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/renv/activate.R @@ -0,0 +1,1220 @@ + +local({ + + # the requested version of renv + version <- "1.0.7" + attr(version, "sha") <- NULL + + # the project directory + project <- Sys.getenv("RENV_PROJECT") + if (!nzchar(project)) + project <- getwd() + + # use start-up diagnostics if enabled + diagnostics <- Sys.getenv("RENV_STARTUP_DIAGNOSTICS", unset = "FALSE") + if (diagnostics) { + start <- Sys.time() + profile <- tempfile("renv-startup-", fileext = ".Rprof") + utils::Rprof(profile) + on.exit({ + utils::Rprof(NULL) + elapsed <- signif(difftime(Sys.time(), start, units = "auto"), digits = 2L) + writeLines(sprintf("- renv took %s to run the autoloader.", format(elapsed))) + writeLines(sprintf("- Profile: %s", profile)) + print(utils::summaryRprof(profile)) + }, add = TRUE) + } + + # figure out whether the autoloader is enabled + enabled <- local({ + + # first, check config option + override <- getOption("renv.config.autoloader.enabled") + if (!is.null(override)) + return(override) + + # if we're being run in a context where R_LIBS is already set, + # don't load -- presumably we're being run as a sub-process and + # the parent process has already set up library paths for us + rcmd <- Sys.getenv("R_CMD", unset = NA) + rlibs <- Sys.getenv("R_LIBS", unset = NA) + if (!is.na(rlibs) && !is.na(rcmd)) + return(FALSE) + + # next, check environment variables + # TODO: prefer using the configuration one in the future + envvars <- c( + "RENV_CONFIG_AUTOLOADER_ENABLED", + "RENV_AUTOLOADER_ENABLED", + "RENV_ACTIVATE_PROJECT" + ) + + for (envvar in envvars) { + envval <- Sys.getenv(envvar, unset = NA) + if (!is.na(envval)) + return(tolower(envval) %in% c("true", "t", "1")) + } + + # enable by default + TRUE + + }) + + # bail if we're not enabled + if (!enabled) { + + # if we're not enabled, we might still need to manually load + # the user profile here + profile <- Sys.getenv("R_PROFILE_USER", unset = "~/.Rprofile") + if (file.exists(profile)) { + cfg <- Sys.getenv("RENV_CONFIG_USER_PROFILE", unset = "TRUE") + if (tolower(cfg) %in% c("true", "t", "1")) + sys.source(profile, envir = globalenv()) + } + + return(FALSE) + + } + + # avoid recursion + if (identical(getOption("renv.autoloader.running"), TRUE)) { + warning("ignoring recursive attempt to run renv autoloader") + return(invisible(TRUE)) + } + + # signal that we're loading renv during R startup + options(renv.autoloader.running = TRUE) + on.exit(options(renv.autoloader.running = NULL), add = TRUE) + + # signal that we've consented to use renv + options(renv.consent = TRUE) + + # load the 'utils' package eagerly -- this ensures that renv shims, which + # mask 'utils' packages, will come first on the search path + library(utils, lib.loc = .Library) + + # unload renv if it's already been loaded + if ("renv" %in% loadedNamespaces()) + unloadNamespace("renv") + + # load bootstrap tools + `%||%` <- function(x, y) { + if (is.null(x)) y else x + } + + catf <- function(fmt, ..., appendLF = TRUE) { + + quiet <- getOption("renv.bootstrap.quiet", default = FALSE) + if (quiet) + return(invisible()) + + msg <- sprintf(fmt, ...) + cat(msg, file = stdout(), sep = if (appendLF) "\n" else "") + + invisible(msg) + + } + + header <- function(label, + ..., + prefix = "#", + suffix = "-", + n = min(getOption("width"), 78)) + { + label <- sprintf(label, ...) + n <- max(n - nchar(label) - nchar(prefix) - 2L, 8L) + if (n <= 0) + return(paste(prefix, label)) + + tail <- paste(rep.int(suffix, n), collapse = "") + paste0(prefix, " ", label, " ", tail) + + } + + heredoc <- function(text, leave = 0) { + + # remove leading, trailing whitespace + trimmed <- gsub("^\\s*\\n|\\n\\s*$", "", text) + + # split into lines + lines <- strsplit(trimmed, "\n", fixed = TRUE)[[1L]] + + # compute common indent + indent <- regexpr("[^[:space:]]", lines) + common <- min(setdiff(indent, -1L)) - leave + paste(substring(lines, common), collapse = "\n") + + } + + startswith <- function(string, prefix) { + substring(string, 1, nchar(prefix)) == prefix + } + + bootstrap <- function(version, library) { + + friendly <- renv_bootstrap_version_friendly(version) + section <- header(sprintf("Bootstrapping renv %s", friendly)) + catf(section) + + # attempt to download renv + catf("- Downloading renv ... ", appendLF = FALSE) + withCallingHandlers( + tarball <- renv_bootstrap_download(version), + error = function(err) { + catf("FAILED") + stop("failed to download:\n", conditionMessage(err)) + } + ) + catf("OK") + on.exit(unlink(tarball), add = TRUE) + + # now attempt to install + catf("- Installing renv ... ", appendLF = FALSE) + withCallingHandlers( + status <- renv_bootstrap_install(version, tarball, library), + error = function(err) { + catf("FAILED") + stop("failed to install:\n", conditionMessage(err)) + } + ) + catf("OK") + + # add empty line to break up bootstrapping from normal output + catf("") + + return(invisible()) + } + + renv_bootstrap_tests_running <- function() { + getOption("renv.tests.running", default = FALSE) + } + + renv_bootstrap_repos <- function() { + + # get CRAN repository + cran <- getOption("renv.repos.cran", "https://cloud.r-project.org") + + # check for repos override + repos <- Sys.getenv("RENV_CONFIG_REPOS_OVERRIDE", unset = NA) + if (!is.na(repos)) { + + # check for RSPM; if set, use a fallback repository for renv + rspm <- Sys.getenv("RSPM", unset = NA) + if (identical(rspm, repos)) + repos <- c(RSPM = rspm, CRAN = cran) + + return(repos) + + } + + # check for lockfile repositories + repos <- tryCatch(renv_bootstrap_repos_lockfile(), error = identity) + if (!inherits(repos, "error") && length(repos)) + return(repos) + + # retrieve current repos + repos <- getOption("repos") + + # ensure @CRAN@ entries are resolved + repos[repos == "@CRAN@"] <- cran + + # add in renv.bootstrap.repos if set + default <- c(FALLBACK = "https://cloud.r-project.org") + extra <- getOption("renv.bootstrap.repos", default = default) + repos <- c(repos, extra) + + # remove duplicates that might've snuck in + dupes <- duplicated(repos) | duplicated(names(repos)) + repos[!dupes] + + } + + renv_bootstrap_repos_lockfile <- function() { + + lockpath <- Sys.getenv("RENV_PATHS_LOCKFILE", unset = "renv.lock") + if (!file.exists(lockpath)) + return(NULL) + + lockfile <- tryCatch(renv_json_read(lockpath), error = identity) + if (inherits(lockfile, "error")) { + warning(lockfile) + return(NULL) + } + + repos <- lockfile$R$Repositories + if (length(repos) == 0) + return(NULL) + + keys <- vapply(repos, `[[`, "Name", FUN.VALUE = character(1)) + vals <- vapply(repos, `[[`, "URL", FUN.VALUE = character(1)) + names(vals) <- keys + + return(vals) + + } + + renv_bootstrap_download <- function(version) { + + sha <- attr(version, "sha", exact = TRUE) + + methods <- if (!is.null(sha)) { + + # attempting to bootstrap a development version of renv + c( + function() renv_bootstrap_download_tarball(sha), + function() renv_bootstrap_download_github(sha) + ) + + } else { + + # attempting to bootstrap a release version of renv + c( + function() renv_bootstrap_download_tarball(version), + function() renv_bootstrap_download_cran_latest(version), + function() renv_bootstrap_download_cran_archive(version) + ) + + } + + for (method in methods) { + path <- tryCatch(method(), error = identity) + if (is.character(path) && file.exists(path)) + return(path) + } + + stop("All download methods failed") + + } + + renv_bootstrap_download_impl <- function(url, destfile) { + + mode <- "wb" + + # https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17715 + fixup <- + Sys.info()[["sysname"]] == "Windows" && + substring(url, 1L, 5L) == "file:" + + if (fixup) + mode <- "w+b" + + args <- list( + url = url, + destfile = destfile, + mode = mode, + quiet = TRUE + ) + + if ("headers" %in% names(formals(utils::download.file))) + args$headers <- renv_bootstrap_download_custom_headers(url) + + do.call(utils::download.file, args) + + } + + renv_bootstrap_download_custom_headers <- function(url) { + + headers <- getOption("renv.download.headers") + if (is.null(headers)) + return(character()) + + if (!is.function(headers)) + stopf("'renv.download.headers' is not a function") + + headers <- headers(url) + if (length(headers) == 0L) + return(character()) + + if (is.list(headers)) + headers <- unlist(headers, recursive = FALSE, use.names = TRUE) + + ok <- + is.character(headers) && + is.character(names(headers)) && + all(nzchar(names(headers))) + + if (!ok) + stop("invocation of 'renv.download.headers' did not return a named character vector") + + headers + + } + + renv_bootstrap_download_cran_latest <- function(version) { + + spec <- renv_bootstrap_download_cran_latest_find(version) + type <- spec$type + repos <- spec$repos + + baseurl <- utils::contrib.url(repos = repos, type = type) + ext <- if (identical(type, "source")) + ".tar.gz" + else if (Sys.info()[["sysname"]] == "Windows") + ".zip" + else + ".tgz" + name <- sprintf("renv_%s%s", version, ext) + url <- paste(baseurl, name, sep = "/") + + destfile <- file.path(tempdir(), name) + status <- tryCatch( + renv_bootstrap_download_impl(url, destfile), + condition = identity + ) + + if (inherits(status, "condition")) + return(FALSE) + + # report success and return + destfile + + } + + renv_bootstrap_download_cran_latest_find <- function(version) { + + # check whether binaries are supported on this system + binary <- + getOption("renv.bootstrap.binary", default = TRUE) && + !identical(.Platform$pkgType, "source") && + !identical(getOption("pkgType"), "source") && + Sys.info()[["sysname"]] %in% c("Darwin", "Windows") + + types <- c(if (binary) "binary", "source") + + # iterate over types + repositories + for (type in types) { + for (repos in renv_bootstrap_repos()) { + + # retrieve package database + db <- tryCatch( + as.data.frame( + utils::available.packages(type = type, repos = repos), + stringsAsFactors = FALSE + ), + error = identity + ) + + if (inherits(db, "error")) + next + + # check for compatible entry + entry <- db[db$Package %in% "renv" & db$Version %in% version, ] + if (nrow(entry) == 0) + next + + # found it; return spec to caller + spec <- list(entry = entry, type = type, repos = repos) + return(spec) + + } + } + + # if we got here, we failed to find renv + fmt <- "renv %s is not available from your declared package repositories" + stop(sprintf(fmt, version)) + + } + + renv_bootstrap_download_cran_archive <- function(version) { + + name <- sprintf("renv_%s.tar.gz", version) + repos <- renv_bootstrap_repos() + urls <- file.path(repos, "src/contrib/Archive/renv", name) + destfile <- file.path(tempdir(), name) + + for (url in urls) { + + status <- tryCatch( + renv_bootstrap_download_impl(url, destfile), + condition = identity + ) + + if (identical(status, 0L)) + return(destfile) + + } + + return(FALSE) + + } + + renv_bootstrap_download_tarball <- function(version) { + + # if the user has provided the path to a tarball via + # an environment variable, then use it + tarball <- Sys.getenv("RENV_BOOTSTRAP_TARBALL", unset = NA) + if (is.na(tarball)) + return() + + # allow directories + if (dir.exists(tarball)) { + name <- sprintf("renv_%s.tar.gz", version) + tarball <- file.path(tarball, name) + } + + # bail if it doesn't exist + if (!file.exists(tarball)) { + + # let the user know we weren't able to honour their request + fmt <- "- RENV_BOOTSTRAP_TARBALL is set (%s) but does not exist." + msg <- sprintf(fmt, tarball) + warning(msg) + + # bail + return() + + } + + catf("- Using local tarball '%s'.", tarball) + tarball + + } + + renv_bootstrap_download_github <- function(version) { + + enabled <- Sys.getenv("RENV_BOOTSTRAP_FROM_GITHUB", unset = "TRUE") + if (!identical(enabled, "TRUE")) + return(FALSE) + + # prepare download options + pat <- Sys.getenv("GITHUB_PAT") + if (nzchar(Sys.which("curl")) && nzchar(pat)) { + fmt <- "--location --fail --header \"Authorization: token %s\"" + extra <- sprintf(fmt, pat) + saved <- options("download.file.method", "download.file.extra") + options(download.file.method = "curl", download.file.extra = extra) + on.exit(do.call(base::options, saved), add = TRUE) + } else if (nzchar(Sys.which("wget")) && nzchar(pat)) { + fmt <- "--header=\"Authorization: token %s\"" + extra <- sprintf(fmt, pat) + saved <- options("download.file.method", "download.file.extra") + options(download.file.method = "wget", download.file.extra = extra) + on.exit(do.call(base::options, saved), add = TRUE) + } + + url <- file.path("https://api.github.com/repos/rstudio/renv/tarball", version) + name <- sprintf("renv_%s.tar.gz", version) + destfile <- file.path(tempdir(), name) + + status <- tryCatch( + renv_bootstrap_download_impl(url, destfile), + condition = identity + ) + + if (!identical(status, 0L)) + return(FALSE) + + renv_bootstrap_download_augment(destfile) + + return(destfile) + + } + + # Add Sha to DESCRIPTION. This is stop gap until #890, after which we + # can use renv::install() to fully capture metadata. + renv_bootstrap_download_augment <- function(destfile) { + sha <- renv_bootstrap_git_extract_sha1_tar(destfile) + if (is.null(sha)) { + return() + } + + # Untar + tempdir <- tempfile("renv-github-") + on.exit(unlink(tempdir, recursive = TRUE), add = TRUE) + untar(destfile, exdir = tempdir) + pkgdir <- dir(tempdir, full.names = TRUE)[[1]] + + # Modify description + desc_path <- file.path(pkgdir, "DESCRIPTION") + desc_lines <- readLines(desc_path) + remotes_fields <- c( + "RemoteType: github", + "RemoteHost: api.github.com", + "RemoteRepo: renv", + "RemoteUsername: rstudio", + "RemotePkgRef: rstudio/renv", + paste("RemoteRef: ", sha), + paste("RemoteSha: ", sha) + ) + writeLines(c(desc_lines[desc_lines != ""], remotes_fields), con = desc_path) + + # Re-tar + local({ + old <- setwd(tempdir) + on.exit(setwd(old), add = TRUE) + + tar(destfile, compression = "gzip") + }) + invisible() + } + + # Extract the commit hash from a git archive. Git archives include the SHA1 + # hash as the comment field of the tarball pax extended header + # (see https://www.kernel.org/pub/software/scm/git/docs/git-archive.html) + # For GitHub archives this should be the first header after the default one + # (512 byte) header. + renv_bootstrap_git_extract_sha1_tar <- function(bundle) { + + # open the bundle for reading + # We use gzcon for everything because (from ?gzcon) + # > Reading from a connection which does not supply a 'gzip' magic + # > header is equivalent to reading from the original connection + conn <- gzcon(file(bundle, open = "rb", raw = TRUE)) + on.exit(close(conn)) + + # The default pax header is 512 bytes long and the first pax extended header + # with the comment should be 51 bytes long + # `52 comment=` (11 chars) + 40 byte SHA1 hash + len <- 0x200 + 0x33 + res <- rawToChar(readBin(conn, "raw", n = len)[0x201:len]) + + if (grepl("^52 comment=", res)) { + sub("52 comment=", "", res) + } else { + NULL + } + } + + renv_bootstrap_install <- function(version, tarball, library) { + + # attempt to install it into project library + dir.create(library, showWarnings = FALSE, recursive = TRUE) + output <- renv_bootstrap_install_impl(library, tarball) + + # check for successful install + status <- attr(output, "status") + if (is.null(status) || identical(status, 0L)) + return(status) + + # an error occurred; report it + header <- "installation of renv failed" + lines <- paste(rep.int("=", nchar(header)), collapse = "") + text <- paste(c(header, lines, output), collapse = "\n") + stop(text) + + } + + renv_bootstrap_install_impl <- function(library, tarball) { + + # invoke using system2 so we can capture and report output + bin <- R.home("bin") + exe <- if (Sys.info()[["sysname"]] == "Windows") "R.exe" else "R" + R <- file.path(bin, exe) + + args <- c( + "--vanilla", "CMD", "INSTALL", "--no-multiarch", + "-l", shQuote(path.expand(library)), + shQuote(path.expand(tarball)) + ) + + system2(R, args, stdout = TRUE, stderr = TRUE) + + } + + renv_bootstrap_platform_prefix <- function() { + + # construct version prefix + version <- paste(R.version$major, R.version$minor, sep = ".") + prefix <- paste("R", numeric_version(version)[1, 1:2], sep = "-") + + # include SVN revision for development versions of R + # (to avoid sharing platform-specific artefacts with released versions of R) + devel <- + identical(R.version[["status"]], "Under development (unstable)") || + identical(R.version[["nickname"]], "Unsuffered Consequences") + + if (devel) + prefix <- paste(prefix, R.version[["svn rev"]], sep = "-r") + + # build list of path components + components <- c(prefix, R.version$platform) + + # include prefix if provided by user + prefix <- renv_bootstrap_platform_prefix_impl() + if (!is.na(prefix) && nzchar(prefix)) + components <- c(prefix, components) + + # build prefix + paste(components, collapse = "/") + + } + + renv_bootstrap_platform_prefix_impl <- function() { + + # if an explicit prefix has been supplied, use it + prefix <- Sys.getenv("RENV_PATHS_PREFIX", unset = NA) + if (!is.na(prefix)) + return(prefix) + + # if the user has requested an automatic prefix, generate it + auto <- Sys.getenv("RENV_PATHS_PREFIX_AUTO", unset = NA) + if (is.na(auto) && getRversion() >= "4.4.0") + auto <- "TRUE" + + if (auto %in% c("TRUE", "True", "true", "1")) + return(renv_bootstrap_platform_prefix_auto()) + + # empty string on failure + "" + + } + + renv_bootstrap_platform_prefix_auto <- function() { + + prefix <- tryCatch(renv_bootstrap_platform_os(), error = identity) + if (inherits(prefix, "error") || prefix %in% "unknown") { + + msg <- paste( + "failed to infer current operating system", + "please file a bug report at https://github.com/rstudio/renv/issues", + sep = "; " + ) + + warning(msg) + + } + + prefix + + } + + renv_bootstrap_platform_os <- function() { + + sysinfo <- Sys.info() + sysname <- sysinfo[["sysname"]] + + # handle Windows + macOS up front + if (sysname == "Windows") + return("windows") + else if (sysname == "Darwin") + return("macos") + + # check for os-release files + for (file in c("/etc/os-release", "/usr/lib/os-release")) + if (file.exists(file)) + return(renv_bootstrap_platform_os_via_os_release(file, sysinfo)) + + # check for redhat-release files + if (file.exists("/etc/redhat-release")) + return(renv_bootstrap_platform_os_via_redhat_release()) + + "unknown" + + } + + renv_bootstrap_platform_os_via_os_release <- function(file, sysinfo) { + + # read /etc/os-release + release <- utils::read.table( + file = file, + sep = "=", + quote = c("\"", "'"), + col.names = c("Key", "Value"), + comment.char = "#", + stringsAsFactors = FALSE + ) + + vars <- as.list(release$Value) + names(vars) <- release$Key + + # get os name + os <- tolower(sysinfo[["sysname"]]) + + # read id + id <- "unknown" + for (field in c("ID", "ID_LIKE")) { + if (field %in% names(vars) && nzchar(vars[[field]])) { + id <- vars[[field]] + break + } + } + + # read version + version <- "unknown" + for (field in c("UBUNTU_CODENAME", "VERSION_CODENAME", "VERSION_ID", "BUILD_ID")) { + if (field %in% names(vars) && nzchar(vars[[field]])) { + version <- vars[[field]] + break + } + } + + # join together + paste(c(os, id, version), collapse = "-") + + } + + renv_bootstrap_platform_os_via_redhat_release <- function() { + + # read /etc/redhat-release + contents <- readLines("/etc/redhat-release", warn = FALSE) + + # infer id + id <- if (grepl("centos", contents, ignore.case = TRUE)) + "centos" + else if (grepl("redhat", contents, ignore.case = TRUE)) + "redhat" + else + "unknown" + + # try to find a version component (very hacky) + version <- "unknown" + + parts <- strsplit(contents, "[[:space:]]")[[1L]] + for (part in parts) { + + nv <- tryCatch(numeric_version(part), error = identity) + if (inherits(nv, "error")) + next + + version <- nv[1, 1] + break + + } + + paste(c("linux", id, version), collapse = "-") + + } + + renv_bootstrap_library_root_name <- function(project) { + + # use project name as-is if requested + asis <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT_ASIS", unset = "FALSE") + if (asis) + return(basename(project)) + + # otherwise, disambiguate based on project's path + id <- substring(renv_bootstrap_hash_text(project), 1L, 8L) + paste(basename(project), id, sep = "-") + + } + + renv_bootstrap_library_root <- function(project) { + + prefix <- renv_bootstrap_profile_prefix() + + path <- Sys.getenv("RENV_PATHS_LIBRARY", unset = NA) + if (!is.na(path)) + return(paste(c(path, prefix), collapse = "/")) + + path <- renv_bootstrap_library_root_impl(project) + if (!is.null(path)) { + name <- renv_bootstrap_library_root_name(project) + return(paste(c(path, prefix, name), collapse = "/")) + } + + renv_bootstrap_paths_renv("library", project = project) + + } + + renv_bootstrap_library_root_impl <- function(project) { + + root <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT", unset = NA) + if (!is.na(root)) + return(root) + + type <- renv_bootstrap_project_type(project) + if (identical(type, "package")) { + userdir <- renv_bootstrap_user_dir() + return(file.path(userdir, "library")) + } + + } + + renv_bootstrap_validate_version <- function(version, description = NULL) { + + # resolve description file + # + # avoid passing lib.loc to `packageDescription()` below, since R will + # use the loaded version of the package by default anyhow. note that + # this function should only be called after 'renv' is loaded + # https://github.com/rstudio/renv/issues/1625 + description <- description %||% packageDescription("renv") + + # check whether requested version 'version' matches loaded version of renv + sha <- attr(version, "sha", exact = TRUE) + valid <- if (!is.null(sha)) + renv_bootstrap_validate_version_dev(sha, description) + else + renv_bootstrap_validate_version_release(version, description) + + if (valid) + return(TRUE) + + # the loaded version of renv doesn't match the requested version; + # give the user instructions on how to proceed + dev <- identical(description[["RemoteType"]], "github") + remote <- if (dev) + paste("rstudio/renv", description[["RemoteSha"]], sep = "@") + else + paste("renv", description[["Version"]], sep = "@") + + # display both loaded version + sha if available + friendly <- renv_bootstrap_version_friendly( + version = description[["Version"]], + sha = if (dev) description[["RemoteSha"]] + ) + + fmt <- heredoc(" + renv %1$s was loaded from project library, but this project is configured to use renv %2$s. + - Use `renv::record(\"%3$s\")` to record renv %1$s in the lockfile. + - Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library. + ") + catf(fmt, friendly, renv_bootstrap_version_friendly(version), remote) + + FALSE + + } + + renv_bootstrap_validate_version_dev <- function(version, description) { + expected <- description[["RemoteSha"]] + is.character(expected) && startswith(expected, version) + } + + renv_bootstrap_validate_version_release <- function(version, description) { + expected <- description[["Version"]] + is.character(expected) && identical(expected, version) + } + + renv_bootstrap_hash_text <- function(text) { + + hashfile <- tempfile("renv-hash-") + on.exit(unlink(hashfile), add = TRUE) + + writeLines(text, con = hashfile) + tools::md5sum(hashfile) + + } + + renv_bootstrap_load <- function(project, libpath, version) { + + # try to load renv from the project library + if (!requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) + return(FALSE) + + # warn if the version of renv loaded does not match + renv_bootstrap_validate_version(version) + + # execute renv load hooks, if any + hooks <- getHook("renv::autoload") + for (hook in hooks) + if (is.function(hook)) + tryCatch(hook(), error = warnify) + + # load the project + renv::load(project) + + TRUE + + } + + renv_bootstrap_profile_load <- function(project) { + + # if RENV_PROFILE is already set, just use that + profile <- Sys.getenv("RENV_PROFILE", unset = NA) + if (!is.na(profile) && nzchar(profile)) + return(profile) + + # check for a profile file (nothing to do if it doesn't exist) + path <- renv_bootstrap_paths_renv("profile", profile = FALSE, project = project) + if (!file.exists(path)) + return(NULL) + + # read the profile, and set it if it exists + contents <- readLines(path, warn = FALSE) + if (length(contents) == 0L) + return(NULL) + + # set RENV_PROFILE + profile <- contents[[1L]] + if (!profile %in% c("", "default")) + Sys.setenv(RENV_PROFILE = profile) + + profile + + } + + renv_bootstrap_profile_prefix <- function() { + profile <- renv_bootstrap_profile_get() + if (!is.null(profile)) + return(file.path("profiles", profile, "renv")) + } + + renv_bootstrap_profile_get <- function() { + profile <- Sys.getenv("RENV_PROFILE", unset = "") + renv_bootstrap_profile_normalize(profile) + } + + renv_bootstrap_profile_set <- function(profile) { + profile <- renv_bootstrap_profile_normalize(profile) + if (is.null(profile)) + Sys.unsetenv("RENV_PROFILE") + else + Sys.setenv(RENV_PROFILE = profile) + } + + renv_bootstrap_profile_normalize <- function(profile) { + + if (is.null(profile) || profile %in% c("", "default")) + return(NULL) + + profile + + } + + renv_bootstrap_path_absolute <- function(path) { + + substr(path, 1L, 1L) %in% c("~", "/", "\\") || ( + substr(path, 1L, 1L) %in% c(letters, LETTERS) && + substr(path, 2L, 3L) %in% c(":/", ":\\") + ) + + } + + renv_bootstrap_paths_renv <- function(..., profile = TRUE, project = NULL) { + renv <- Sys.getenv("RENV_PATHS_RENV", unset = "renv") + root <- if (renv_bootstrap_path_absolute(renv)) NULL else project + prefix <- if (profile) renv_bootstrap_profile_prefix() + components <- c(root, renv, prefix, ...) + paste(components, collapse = "/") + } + + renv_bootstrap_project_type <- function(path) { + + descpath <- file.path(path, "DESCRIPTION") + if (!file.exists(descpath)) + return("unknown") + + desc <- tryCatch( + read.dcf(descpath, all = TRUE), + error = identity + ) + + if (inherits(desc, "error")) + return("unknown") + + type <- desc$Type + if (!is.null(type)) + return(tolower(type)) + + package <- desc$Package + if (!is.null(package)) + return("package") + + "unknown" + + } + + renv_bootstrap_user_dir <- function() { + dir <- renv_bootstrap_user_dir_impl() + path.expand(chartr("\\", "/", dir)) + } + + renv_bootstrap_user_dir_impl <- function() { + + # use local override if set + override <- getOption("renv.userdir.override") + if (!is.null(override)) + return(override) + + # use R_user_dir if available + tools <- asNamespace("tools") + if (is.function(tools$R_user_dir)) + return(tools$R_user_dir("renv", "cache")) + + # try using our own backfill for older versions of R + envvars <- c("R_USER_CACHE_DIR", "XDG_CACHE_HOME") + for (envvar in envvars) { + root <- Sys.getenv(envvar, unset = NA) + if (!is.na(root)) + return(file.path(root, "R/renv")) + } + + # use platform-specific default fallbacks + if (Sys.info()[["sysname"]] == "Windows") + file.path(Sys.getenv("LOCALAPPDATA"), "R/cache/R/renv") + else if (Sys.info()[["sysname"]] == "Darwin") + "~/Library/Caches/org.R-project.R/R/renv" + else + "~/.cache/R/renv" + + } + + renv_bootstrap_version_friendly <- function(version, shafmt = NULL, sha = NULL) { + sha <- sha %||% attr(version, "sha", exact = TRUE) + parts <- c(version, sprintf(shafmt %||% " [sha: %s]", substring(sha, 1L, 7L))) + paste(parts, collapse = "") + } + + renv_bootstrap_exec <- function(project, libpath, version) { + if (!renv_bootstrap_load(project, libpath, version)) + renv_bootstrap_run(version, libpath) + } + + renv_bootstrap_run <- function(version, libpath) { + + # perform bootstrap + bootstrap(version, libpath) + + # exit early if we're just testing bootstrap + if (!is.na(Sys.getenv("RENV_BOOTSTRAP_INSTALL_ONLY", unset = NA))) + return(TRUE) + + # try again to load + if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) { + return(renv::load(project = getwd())) + } + + # failed to download or load renv; warn the user + msg <- c( + "Failed to find an renv installation: the project will not be loaded.", + "Use `renv::activate()` to re-initialize the project." + ) + + warning(paste(msg, collapse = "\n"), call. = FALSE) + + } + + renv_json_read <- function(file = NULL, text = NULL) { + + jlerr <- NULL + + # if jsonlite is loaded, use that instead + if ("jsonlite" %in% loadedNamespaces()) { + + json <- tryCatch(renv_json_read_jsonlite(file, text), error = identity) + if (!inherits(json, "error")) + return(json) + + jlerr <- json + + } + + # otherwise, fall back to the default JSON reader + json <- tryCatch(renv_json_read_default(file, text), error = identity) + if (!inherits(json, "error")) + return(json) + + # report an error + if (!is.null(jlerr)) + stop(jlerr) + else + stop(json) + + } + + renv_json_read_jsonlite <- function(file = NULL, text = NULL) { + text <- paste(text %||% readLines(file, warn = FALSE), collapse = "\n") + jsonlite::fromJSON(txt = text, simplifyVector = FALSE) + } + + renv_json_read_default <- function(file = NULL, text = NULL) { + + # find strings in the JSON + text <- paste(text %||% readLines(file, warn = FALSE), collapse = "\n") + pattern <- '["](?:(?:\\\\.)|(?:[^"\\\\]))*?["]' + locs <- gregexpr(pattern, text, perl = TRUE)[[1]] + + # if any are found, replace them with placeholders + replaced <- text + strings <- character() + replacements <- character() + + if (!identical(c(locs), -1L)) { + + # get the string values + starts <- locs + ends <- locs + attr(locs, "match.length") - 1L + strings <- substring(text, starts, ends) + + # only keep those requiring escaping + strings <- grep("[[\\]{}:]", strings, perl = TRUE, value = TRUE) + + # compute replacements + replacements <- sprintf('"\032%i\032"', seq_along(strings)) + + # replace the strings + mapply(function(string, replacement) { + replaced <<- sub(string, replacement, replaced, fixed = TRUE) + }, strings, replacements) + + } + + # transform the JSON into something the R parser understands + transformed <- replaced + transformed <- gsub("{}", "`names<-`(list(), character())", transformed, fixed = TRUE) + transformed <- gsub("[[{]", "list(", transformed, perl = TRUE) + transformed <- gsub("[]}]", ")", transformed, perl = TRUE) + transformed <- gsub(":", "=", transformed, fixed = TRUE) + text <- paste(transformed, collapse = "\n") + + # parse it + json <- parse(text = text, keep.source = FALSE, srcfile = NULL)[[1L]] + + # construct map between source strings, replaced strings + map <- as.character(parse(text = strings)) + names(map) <- as.character(parse(text = replacements)) + + # convert to list + map <- as.list(map) + + # remap strings in object + remapped <- renv_json_read_remap(json, map) + + # evaluate + eval(remapped, envir = baseenv()) + + } + + renv_json_read_remap <- function(json, map) { + + # fix names + if (!is.null(names(json))) { + lhs <- match(names(json), names(map), nomatch = 0L) + rhs <- match(names(map), names(json), nomatch = 0L) + names(json)[rhs] <- map[lhs] + } + + # fix values + if (is.character(json)) + return(map[[json]] %||% json) + + # handle true, false, null + if (is.name(json)) { + text <- as.character(json) + if (text == "true") + return(TRUE) + else if (text == "false") + return(FALSE) + else if (text == "null") + return(NULL) + } + + # recurse + if (is.recursive(json)) { + for (i in seq_along(json)) { + json[i] <- list(renv_json_read_remap(json[[i]], map)) + } + } + + json + + } + + # load the renv profile, if any + renv_bootstrap_profile_load(project) + + # construct path to library root + root <- renv_bootstrap_library_root(project) + + # construct library prefix for platform + prefix <- renv_bootstrap_platform_prefix() + + # construct full libpath + libpath <- file.path(root, prefix) + + # run bootstrap code + renv_bootstrap_exec(project, libpath, version) + + invisible() + +}) diff --git a/analyses/cell-type-ETP-ALL-03/renv/settings.json b/analyses/cell-type-ETP-ALL-03/renv/settings.json new file mode 100644 index 000000000..96e0d180f --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/renv/settings.json @@ -0,0 +1,5 @@ +{ + "bioconductor.version": "3.19", + "ppm.enabled": true, + "r.version": "4.4.0" +} diff --git a/analyses/cell-type-ETP-ALL-03/results/README.md b/analyses/cell-type-ETP-ALL-03/results/README.md new file mode 100644 index 000000000..af51c690e --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/results/README.md @@ -0,0 +1,5 @@ +# Results directory instructions + +Files in the results directory should not be directly committed to the repository. + +Instead, copy results files to an S3 bucket and add a link to the S3 location in this README file. diff --git a/analyses/cell-type-ETP-ALL-03/scratch/.gitkeep b/analyses/cell-type-ETP-ALL-03/scratch/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/analyses/cell-type-ETP-ALL-03/scripts/.gitkeep b/analyses/cell-type-ETP-ALL-03/scripts/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R b/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R new file mode 100644 index 000000000..de07a8662 --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R @@ -0,0 +1,131 @@ +#!/usr/bin/env Rscript + +#This script uses SAM algorithm to perform soft feature selection for better separation of homogenous populations. +#So we use SAM to preprocess the data, perform feature selection/dimensionality reduction and clustering. +#The outputs are an intermediate rds file and umap plot showing leiden clustering results. + +library(Seurat) +library(SingleCellExperiment) +library(ggplot2) +######## step01 ####################################### +library(reticulate) +use_condaenv("openscpca-cell-type-ETP-ALL-03") +samalg <- import("samalg") #https://github.com/atarashansky/self-assembling-manifold/tree/master + +run_sam <- function(ind.sample, ind.library){ + sce <- readRDS(file.path(data_loc,ind.sample,paste0(ind.library,"_processed.rds"))) + + #reading in doublets information + doublets.data <- read.table(file = file.path(doublet_loc,ind.sample,paste0(ind.library,"_processed_scdblfinder.tsv")), + sep = "\t", header = T) + idx <- match(colnames(sce), doublets.data$barcodes) + sce$doublet_class <- doublets.data$class[idx] + sce <- sce[, which(sce$subsets_mito_percent < 25)] + + #step 01 feature selection/dimensionality using SAM + sam = samalg$SAM(counts = c(r_to_py(t(counts(sce))), + r_to_py(as.array(rownames(sce))), + r_to_py(as.array(colnames(sce))))) + sam$preprocess_data() + sam$run(distance = 'correlation') + sam$clustering(method = "leiden") #leiden clustering is the default algorithm in SAM + sam$save_anndata(paste0("sam.",ind.sample,".h5ad")) + + final.obj <- schard::h5ad2seurat(paste0("sam.",ind.sample,".h5ad")) + final.obj <- AddMetaData(final.obj, as.data.frame(colData(sce))) + final.obj[["RNA"]]@counts <- counts(sce) + final.obj[["RNA"]] <- AddMetaData(final.obj[["RNA"]], as.data.frame(rowData(sce))) + final.obj$nCount_RNA <- final.obj$sum + final.obj$nFeature_RNA <- final.obj$detected + # create a new assay to store ADT information + final.obj[["ADT"]] <- CreateAssayObject(counts = counts(altExp(sce, "adt"))) + final.obj[["ADT"]]@data <- logcounts(altExp(sce, "adt")) + final.obj[["ADT"]] <- AddMetaData(final.obj[["ADT"]], as.data.frame(rowData(altExp(sce, "adt")))) + + saveRDS(final.obj, file.path(out_loc,"results/rds",paste0(ind.sample,".rds"))) + unlink(paste0("sam.",ind.sample,".h5ad")) + + DimPlot(final.obj, reduction = "Xumap_", group.by = "leiden_clusters", label = T) + + ggtitle(paste0(ind.sample,": leiden_clusters")) + ggsave(file.path(out_loc,"plots",paste0(ind.sample,"_leidenCluster.pdf")), width = 7, height = 7) +} + +run_multisam <- function(ind.sam, libraries){ + seu.list <- list() + rowData_RNA <- c() + rowData_ADT <- c() + for (i in 1:length(libraries)){ + seu.list[[i]] <- as.Seurat(readRDS(file.path(data_loc,ind.sam,paste0(libraries[i],"_processed.rds")))) + #reading in doublets information + doublets.data <- read.table(file = file.path(doublet_loc,ind.sam,paste0(libraries[i],"_processed_scdblfinder.tsv")), + sep = "\t", header = T) + idx <- match(colnames(seu.list[[i]]), doublets.data$barcodes) + seu.list[[i]]$doublet_class <- doublets.data$class[idx] + seu.list[[i]]$libraryID <- libraries[i] + if (i == 1){ + rowData_RNA <- as.data.frame(seu.list[[i]][["originalexp"]]@meta.features) + rowData_ADT <- as.data.frame(seu.list[[i]][["adt"]]@meta.features) + } + else{ + rowData_RNA <- cbind(rowData_RNA, as.data.frame(seu.list[[i]][["originalexp"]]@meta.features[,3:4])) + rowData_ADT <- cbind(rowData_ADT, as.data.frame(seu.list[[i]][["adt"]]@meta.features[,3:4])) + } + } + colnames(rowData_RNA)[3:ncol(rowData_RNA)] <- paste0(rep(libraries, each = 2),"-",colnames(rowData_RNA)[3:ncol(rowData_RNA)]) + colnames(rowData_ADT)[3:ncol(rowData_ADT)] <- paste0(rep(libraries, each = 2),"-",colnames(rowData_ADT)[3:ncol(rowData_ADT)]) + sample.combined <- merge(seu.list[[1]], y = seu.list[2:length(libraries)], add.cell.ids = libraries, project = ind.sam) + sample.combined$nCount_originalexp <- NULL + sample.combined$nFeature_originalexp <- NULL + DefaultAssay(sample.combined) <- "originalexp" + + #step 01 feature selection/dimensionality using SAM + sam = samalg$SAM(counts = c(r_to_py(t(sample.combined[["originalexp"]]@counts)), + r_to_py(as.array(rownames(sample.combined))), + r_to_py(as.array(colnames(sample.combined))))) + sam$preprocess_data() + sam$run(distance = 'correlation') + sam$clustering(method = "leiden") #leiden clustering is the default algorithm in SAM + sam$save_anndata(paste0("sam.",ind.sam,".h5ad")) + + final.obj <- schard::h5ad2seurat(paste0("sam.",ind.sam,".h5ad")) + final.obj <- AddMetaData(final.obj, as.data.frame(sample.combined@meta.data)) + final.obj[["RNA"]]@counts <- sample.combined[["originalexp"]]@counts + final.obj[["RNA"]] <- AddMetaData(final.obj[["RNA"]], rowData_RNA) + final.obj$nCount_RNA <- final.obj$sum + final.obj$nFeature_RNA <- final.obj$detected + # create a new assay to store ADT information + final.obj[["ADT"]] <- CreateAssayObject(counts = sample.combined[["adt"]]@counts) + final.obj[["ADT"]]@data <- sample.combined[["adt"]]@data + final.obj[["ADT"]] <- AddMetaData(final.obj[["ADT"]], rowData_ADT) + + saveRDS(final.obj, file.path(out_loc,"results/rds",paste0(ind.sam,".rds"))) + unlink(paste0("sam.",ind.sam,".h5ad")) + + DimPlot(final.obj, reduction = "Xumap_", group.by = "leiden_clusters", split.by = "libraryID", label = T) + + ggtitle(paste0(ind.sam,": leiden_clusters")) + ggsave(file.path(out_loc,"plots",paste0(ind.sam,"_leidenCluster.pdf")), width = 10, height = 5) +} + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") +data_loc <- file.path(project_root, "data/current",projectID) +doublet_loc <- file.path(project_root, "data/current/results/doublet-detection",projectID) + +metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +sampleID <- metadata$scpca_sample_id +libraryID <- metadata$scpca_library_id + +## removing SCPCS000066 from the list of sampleID, because there are two libraries for this sample +special.case <- c("SCPCS000066") +special.library <- libraryID[which(sampleID==special.case)] +sampleID <- setdiff(sampleID, special.case) +libraryID <- setdiff(libraryID, special.library) + +purrr::walk2(sampleID, libraryID, run_sam) + +run_multisam(special.case, special.library) + + diff --git a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R new file mode 100644 index 000000000..93fc80f2d --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R @@ -0,0 +1,167 @@ +#!/usr/bin/env Rscript + +#This script uses ScType to perform cell type annotation and CopyKat for tumor cells identification. +#We provides Azimuth_BM_level1.csv as the marker files for ScType, and generates sctype_classification metadata for the seurat object +#We also provides the top 10 celltypes with the highest sctype score for each cluster in the results/. +#Based on the annotated B cells, we provide them as the normal cells for CopyKat to identify tumor cells. +#The outputs are final rds file and two umap plots showing cell type and tumor/normal cells status. + +lapply(c("dplyr","Seurat","HGNChelper","openxlsx"), library, character.only = T) +library(copykat) +library(ggplot2) + +# load gene set preparation function +#source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/gene_sets_prepare.R") +# load cell type annotation function +source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/sctype_score_.R") + +gene_sets_prepare <- function(path_to_db_file, cell_type){ + cell_markers = openxlsx::read.xlsx(path_to_db_file) + cell_markers = cell_markers[cell_markers$tissueType == cell_type,] + cell_markers$geneSymbolmore1 = gsub(" ","",cell_markers$geneSymbolmore1); cell_markers$geneSymbolmore2 = gsub(" ","",cell_markers$geneSymbolmore2) + + # correct gene symbols from the given DB (up-genes) + cell_markers$geneSymbolmore1 = sapply(1:nrow(cell_markers), function(i){ + markers_all = gsub(" ", "", unlist(strsplit(cell_markers$geneSymbolmore1[i],","))) + markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) + markers_all = sort(markers_all) + + if(length(markers_all) > 0){ + suppressMessages({markers_all = unique(na.omit(markers_all))}) #since the markers are provided in Ensembl ID, I removed checkGeneSymbols function here + paste0(markers_all, collapse=",") + } else { + "" + } + }) + + # correct gene symbols from the given DB (down-genes) + cell_markers$geneSymbolmore2 = sapply(1:nrow(cell_markers), function(i){ + markers_all = gsub(" ", "", unlist(strsplit(cell_markers$geneSymbolmore2[i],","))) + markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) + markers_all = sort(markers_all) + + if(length(markers_all) > 0){ + suppressMessages({markers_all = unique(na.omit(markers_all))}) #since the markers are provided in Ensembl ID, I removed checkGeneSymbols function here + paste0(markers_all, collapse=",") + } else { + "" + } + }) + + cell_markers$geneSymbolmore1 = gsub("///",",",cell_markers$geneSymbolmore1);cell_markers$geneSymbolmore1 = gsub(" ","",cell_markers$geneSymbolmore1) + cell_markers$geneSymbolmore2 = gsub("///",",",cell_markers$geneSymbolmore2);cell_markers$geneSymbolmore2 = gsub(" ","",cell_markers$geneSymbolmore2) + + gs = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$geneSymbolmore1[j]),",")))); names(gs) = cell_markers$cellName + gs2 = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$geneSymbolmore2[j]),",")))); names(gs2) = cell_markers$cellName + + list(gs_positive = gs, gs_negative = gs2) +} + +running_scType <- function(xls, tissue, annot.obj, assay = "RNA", thres = 4){ + gs_list <- gene_sets_prepare(xls, tissue) #prepare gene sets + # check Seurat object version (scRNA-seq matrix extracted differently in Seurat v4/v5) + seurat_package_v5 <- isFALSE('counts' %in% names(attributes(annot.obj[[assay]]))); + print(sprintf("Seurat object %s is used", ifelse(seurat_package_v5, "v5", "v4"))) + # extract scaled scRNA-seq matrix + scRNAseqData_scaled <- if (seurat_package_v5) as.matrix(annot.obj[[assay]]$scale.data) else as.matrix(annot.obj[[assay]]@scale.data) + # run ScType + es.max <- sctype_score(scRNAseqData = scRNAseqData_scaled, scaled = TRUE, gs = gs_list$gs_positive, gs2 = gs_list$gs_negative) + # merge by cluster + cL_results <- do.call("rbind", lapply(unique(annot.obj@meta.data$leiden_clusters), function(cl){ + es.max.cl = sort(rowSums(es.max[ ,rownames(annot.obj@meta.data[annot.obj@meta.data$leiden_clusters==cl, ])]), decreasing = !0) + head(data.frame(cluster = cl, type = names(es.max.cl), scores = es.max.cl, ncells = sum(annot.obj@meta.data$leiden_clusters==cl)), 10) + })) + sctype_scores <- cL_results %>% group_by(cluster) %>% top_n(n = 1, wt = scores) + # set low-confident (low ScType score) clusters to "unknown" + sctype_scores$type[as.numeric(as.character(sctype_scores$scores)) < sctype_scores$ncells/thres] <- "Unknown" + print(sctype_scores[,1:3]) + + annot.obj@meta.data$sctype_classification = "" + for(j in unique(sctype_scores$cluster)){ + cl_type = sctype_scores[sctype_scores$cluster==j,]; + annot.obj@meta.data$sctype_classification[annot.obj@meta.data$leiden_clusters == j] = as.character(cl_type$type[1]) + } + return (list(annot.obj, cL_results)) +} + +plot_modulescore <- function(xls, tissue, seu, sample.name){ + gs_list <- gene_sets_prepare(xls, tissue) #prepare gene sets + for (i in 1:length(gs_list$gs_positive)){ + seu <- AddModuleScore(object = seu, name = paste0(gsub(" ","",names(gs_list$gs_positive[i])),"_Features"), + features = list(gs_list$gs_positive[[i]])) + } + features.vec <- paste0(gsub(" ","",names(gs_list$gs_positive)),"_Features1") + Idents(seu) <- seu$leiden_clusters + DotPlot(seu, features = features.vec) + theme(axis.text.x = element_text(angle=45,hjust=1)) + + ggtitle(paste0(sample.name,": cell type module score")) + + scale_color_gradient2(low = scales::muted("blue"), mid = "whitesmoke", high = scales::muted("red"), midpoint = 0) + ggsave(file.path(out_loc,"plots",paste0(sample.name,"_features_dotplot.pdf")), width = 7, height = 7) +} + +run_annot <- function(ind.sam){ + seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.sam,".rds"))) + final.obj <- seu #so I can keep an object without scale.data (it is taking too much space to save scale.data) + seu <- ScaleData(seu, features = rownames(seu)) + ## relaxing the threshold of sctype_score larger than %ncells in a cluster, from 25% (default) to 10% + res <- running_scType(xls, tissue, seu, thres = 10) #cell type annotation + seu$lowConfidence_annot <- res[[1]]$sctype_classification #seurat object with 'sctype_classification' metadata added + res <- running_scType(xls, tissue, seu) + seu <- res[[1]] + #res[[2]] - A table with top 10 cell types with the highest scores for each cluster + write.table(res[[2]], file = file.path(out_loc,"results",paste0(ind.sam,"_sctype_top10_celltypes_perCluster.txt")), + row.names = F, sep = "\t", quote = F) + p1 <- DimPlot(seu, reduction = "Xumap_", group.by = "sctype_classification", cols = ct_color) + + ggtitle(paste0(ind.sam,": cell type")) + p2 <- DimPlot(seu, reduction = "Xumap_", group.by = "lowConfidence_annot", cols = ct_color) + + ggtitle(paste0(ind.sam,": low confidence annotation")) + p1 + p2 + ggsave(file.path(out_loc,"plots",paste0(ind.sam,"_celltype.pdf")), width = 10, height = 5) + + plot_modulescore(xls, tissue, seu, ind.sam) + + #using copykat for tumor cells identification + norm.cells <- colnames(seu)[which(seu$sctype_classification=="B")] + if (length(norm.cells) > 0){ #the sample has B cells annotated + copykat.test <- copykat(rawmat=seu@assays[["RNA"]]@counts, id.type="Ensemble", + ngene.chr=5, win.size=25, KS.cut=0.1, sam.name=ind.sam, + distance="euclidean", norm.cell.names=norm.cells, + output.seg="FALSE", plot.genes="TRUE", genome="hg20",n.cores=50) + idx <- match(colnames(seu), copykat.test$prediction$cell.names) + seu$copykat.pred <- copykat.test$prediction$copykat.pred[idx] + DimPlot(seu, reduction = "Xumap_", group.by = "copykat.pred") + + ggtitle(paste0(ind.sam,": copykat prediction")) + ggsave(file.path(out_loc,"plots",paste0(ind.sam,"_copykatPred.pdf")), width = 7, height = 7) + + voi <- c('leiden_clusters','sctype_classification','lowConfidence_annot','copykat.pred') + final.obj$copykat.pred <- seu$copykat.pred + }else{ + voi <- c('leiden_clusters','sctype_classification','lowConfidence_annot') + } + + write.table(data.frame(FetchData(seu, vars = voi)), sep = "\t", quote = F, + file = file.path(out_loc,"results",paste0(ind.sam,"_metadata.txt"))) + final.obj$sctype_classification <- seu$sctype_classification + final.obj$lowConfidence_annot <- seu$lowConfidence_annot + saveRDS(final.obj, file = file.path(out_loc,"results/rds",paste0(ind.sam,".rds"))) +} + + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") +data_loc <- file.path(project_root, "data/current",projectID) + +metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +sampleID <- unique(metadata$scpca_sample_id) +# DB file +db <- file.path(out_loc,"Azimuth_BM_level1.csv") +xls <- openxlsx::buildWorkbook(read.csv(db, header = T)) +tissue <- "Immune system" +ct_color <- c("darkorchid","skyblue2","dodgerblue2","gold","beige","sienna1","green4","navy", + "chocolate4","red","darkred","#6A3D9A","maroon","yellow4","grey35","black","lightpink","grey80") +names(ct_color) <- c("B","CD4 T","CD8 T","DC","HSPC","Mono","NK","Other T","Macrophage", + "Early Eryth","Late Eryth","Plasma","Platelet","Stromal","Blast","Cancer","Pre Eryth","Unknown") + +purrr::walk(sampleID, run_annot) From 0da0ec575a2d9219cbd7d709d2db37d2a17f2d3f Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Wed, 2 Oct 2024 17:17:36 +0000 Subject: [PATCH 02/18] update gitignore --- analyses/cell-type-ETP-ALL-03/.gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/analyses/cell-type-ETP-ALL-03/.gitignore b/analyses/cell-type-ETP-ALL-03/.gitignore index 418755f52..25e54d3a1 100644 --- a/analyses/cell-type-ETP-ALL-03/.gitignore +++ b/analyses/cell-type-ETP-ALL-03/.gitignore @@ -2,6 +2,10 @@ /results/* !/results/README.md +# Plots should not be committed +/plots/* +!/plots/.gitkeep + # Ignore the scratch directory (but keep it present) /scratch/* !/scratch/.gitkeep From fd023eb071db2a9e1112e620e70f29bf5d87d9db Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Wed, 2 Oct 2024 22:18:24 +0000 Subject: [PATCH 03/18] added marker file --- .../cell-type-ETP-ALL-03/Azimuth_BM_level1.csv | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv diff --git a/analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv b/analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv new file mode 100644 index 000000000..5f915875a --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv @@ -0,0 +1,18 @@ +tissueType,cellName,geneSymbolmore1,geneSymbolmore2,fullName,ontologyID +Immune system,B,"ENSG00000163534,ENSG00000132704,ENSG00000012124,ENSG00000138639,ENSG00000153064,ENSG00000156738,ENSG00000116191,ENSG00000104894,ENSG00000133789,ENSG00000105369",,B cell,CL_0000945 +Immune system,CD4 T,"ENSG00000172005,ENSG00000138795,ENSG00000168685,ENSG00000081059,ENSG00000227507,ENSG00000104660,ENSG00000198851,ENSG00000167286,ENSG00000160654,ENSG00000139193",,CD4 T cell,CL_0000624 +Immune system,CD8 T,"ENSG00000172116,ENSG00000153563,ENSG00000184613,ENSG00000167286,ENSG00000198851,ENSG00000160307,ENSG00000100450,ENSG00000160654,ENSG00000227191,ENSG00000271503",,CD8 T cell,CL_0000625 +Immune system,DC,"ENSG00000198178,ENSG00000115718,ENSG00000070031,ENSG00000169432,ENSG00000105251,ENSG00000155367,ENSG00000168913,ENSG00000132514,ENSG00000239961,ENSG00000163687",,Dendritic cell,CL_0000451 +Immune system,HSPC,"ENSG00000172995,ENSG00000186710,ENSG00000101200,ENSG00000163554,ENSG00000119888,ENSG00000188672,ENSG00000131016,ENSG00000112077,ENSG00000172247,ENSG00000170891",,Hematopoietic stem and progenitor cell,CL_0000037 +Immune system,Mono,"ENSG00000197353,ENSG00000110203,ENSG00000166523,ENSG00000104974,ENSG00000158825,ENSG00000162444,ENSG00000186074,ENSG00000171051,ENSG00000125810,ENSG00000014914",,Monocytes,CL_0000576 +Immune system,NK,"ENSG00000189430,ENSG00000198574,ENSG00000134545,ENSG00000150687,ENSG00000117281,ENSG00000156966,ENSG00000100385,ENSG00000115607,ENSG00000143184,ENSG00000150045",,Natural killer cell,CL_0000814 +Immune system,Other T,"ENSG00000144290,ENSG00000111796,ENSG00000168685,ENSG00000215788,ENSG00000069667,ENSG00000107742,ENSG00000178573,ENSG00000113088,ENSG00000152518,ENSG00000145220",,Other T cell,CL_0000084 +Immune system,Macrophage,"ENSG00000166211,ENSG00000121769,ENSG00000073754,ENSG00000275385,ENSG00000159189,ENSG00000173369,ENSG00000170323,ENSG00000173372,ENSG00000130203,ENSG00000250722",,Macrophage,CL_0000235 +Immune system,Early Eryth,"ENSG00000119865,ENSG00000179348,ENSG00000005961,ENSG00000106327,ENSG00000102145,ENSG00000105610,ENSG00000170891,ENSG00000135525,ENSG00000075618,ENSG00000130208",,Early Erythrocyte,CL_0000764 +Immune system,Late Eryth,"ENSG00000196188,ENSG00000112212,ENSG00000204010,ENSG00000188672,ENSG00000112077,ENSG00000163554,ENSG00000075340,ENSG00000119888,ENSG00000213934",,Late Erythrocyte,CL_0000764 +Immune system,Plasma,"ENSG00000115884,ENSG00000222037,ENSG00000211640,ENSG00000048462,ENSG00000211673,ENSG00000240505,ENSG00000211685,ENSG00000167476,ENSG00000143297,ENSG00000243466",,Plasma cell,CL_0000786 +Immune system,Platelet,"ENSG00000150681,ENSG00000187699,ENSG00000088726,ENSG00000169704,ENSG00000163737,ENSG00000163736,ENSG00000153071,ENSG00000113140,ENSG00000176783,ENSG00000124491",,Platelet,CL_0000233 +Immune system,Stromal,"ENSG00000115461,ENSG00000047457,ENSG00000091513,ENSG00000011465,ENSG00000139329,ENSG00000164692,ENSG00000147571,ENSG00000041982,ENSG00000152583,ENSG00000112175",,Stromal cell,CL_0000499 +Immune system,Blast,"ENSG00000002586,ENSG00000173762,ENSG00000124766,ENSG00000177606,ENSG00000117632,ENSG00000123416,ENSG00000167286",,Blast cell,CL_0000055 +Immune system,Cancer,"ENSG00000026508,ENSG00000119888,ENSG00000141736,ENSG00000086205,ENSG00000111057,ENSG00000007062",,Cancer cell,CL_0001064 +Immune system,Pre Eryth,"ENSG00000081237,ENSG00000170180,ENSG00000175792,ENSG00000072274,ENSG00000110195,ENSG00000135218,ENSG00000115232,ENSG00000244734,ENSG00000223609,ENSG00000133742",,Erythroid-like and erythroid precursor cell,CL_0000038 From 8bb093ff6cea2512707a6123a312c7916dba0e56 Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Thu, 3 Oct 2024 16:03:05 +0000 Subject: [PATCH 04/18] update readme --- analyses/cell-type-ETP-ALL-03/README.md | 86 ++++++++++++++----- .../cell-type-ETP-ALL-03/results/README.md | 15 +++- 2 files changed, 76 insertions(+), 25 deletions(-) diff --git a/analyses/cell-type-ETP-ALL-03/README.md b/analyses/cell-type-ETP-ALL-03/README.md index 638a0a4fc..8e8149804 100644 --- a/analyses/cell-type-ETP-ALL-03/README.md +++ b/analyses/cell-type-ETP-ALL-03/README.md @@ -1,45 +1,85 @@ -# Template analysis module +# ETP T-ALL Annotation (SCPCP000003) -This is a template analysis module. -It is intended to be used as a starting point for new analysis modules. -Please fill in the content below with information specific to this analysis module. +This analysis module will include codes to annotate cell types and tumor/normal status in ETP T-ALL from SCPCP000003 (n=30) present on the ScPCA portal. ## Description -Please provide a description of your module, including: +We first aim to annotate the cell types in ETP T-ALL, and use the annotated B cells in the sample as the "normal" cells to identify tumor cells, since T-ALL is caused by the clonal proliferation of immature T-cell []. -- What type of analysis is included? -- What methods or tools are used? +- We use the cell type marker (`Azimuth_BM_level1.csv`) from [Azimuth Human Bone Marrow reference](https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow). In total, there are 14 cell types: B, CD4T, CD8T, Other T, DC, Monocytes, Macrophages, NK, Early Erythrocytes, Late Erythrocytes, Plasma, Platelet, Stromal, and Hematopoietic Stem and Progenitor Cells (HSPC). Based on the exploratory analysis, we believe that most of the cells in these samples do not express adequate markers to be distinguished at finer cell type level (eg. naive vs memory, CD14 vs CD16 etc.), and majority of the cells should belong to T-cells. In addition, we include the marker genes for blast cell [[Bhasin et al. (2023)](https://www.nature.com/articles/s41598-023-39152-z)] as well as erythroid precursor and cancer cell in immune system [[ScType](https://sctype.app/database.php) database]. -If there are multiple steps in the module, please include an outline of the analysis steps and scripts used. +- Since ScType annotates cell types at cluster level using marker genes provided by user or from the built-in database, we employ [self-assembling manifold](https://github.com/atarashansky/self-assembling-manifold/tree/master) (SAM) algorithm, a soft feature selection strategy for better separation of homogeneous cell types. + +- After cell type annotation, we provide B cells as the normal cells in the sample, if there is any, to [CopyKat](https://github.com/navinlabcode/copykat), for identification of tumor cells. + +Here are the steps in the module: + +1. Generating a processed rds file for each sample using SAM (`scripts/00-01_processing_rds.R`) + +2. Annotating cell type using ScType and identifying tumor cells using CopyKat (`scripts/02-03_annotation.R`) ## Usage -Please provide instructions on how to run the analysis module. -What commands are needed to execute all steps in the analysis? +Before running Rscripts in R or Rstudio, we first need to prepare the input files as shown in the next section, and run the following codes in the terminal for installing required libraries: + +``` +#system packages installation +sudo apt install libglpk40 +sudo apt install libcurl4-openssl-dev #for Seurat +sudo apt-get install libxml2-dev libfontconfig1-dev libharfbuzz-dev libfribidi-dev libtiff5-dev #for devtools + +conda-lock install --name openscpca-cell-type-ETP-ALL-03 conda-lock.yml +Rscript -e "renv::restore()" +``` ## Input files -Please include a description of the required inputs to run the analysis module (e.g., processed `SingleCellExperiment` objects from the ScPCA Portal). -If the input to this module is dependent on running any other scripts (e.g., `download-data.py`) or on the output of another module, please state that. -If possible, include examples of specific commands used to obtain data. +The `scripts/00-01_processing_rds.R` requires the processed SingleCellExperiment objects (`_processed.rds`) and doublet-detection results (`_processed_scdblfinder.tsv`) from SCPCP000003. These files could be obtained from running the following codes: + +``` +#run in terminal +../../download-data.py --projects SCPCP000003 +../../download-results.py --projects SCPCP000003 --modules doublet-detection +``` + +As for the annotation, `scripts/02-03_annotation.R` requires cell type marker gene file, `Azimuth_BM_level1.csv`, as an input for ScType. This excel file contains a list of positive marker genes in Ensembl ID under `geneSymbolmore1` for each cell type; *TMEM56* and *CD235a* are not detected in our dataset, thus they are being removed as part of the markers for Late Eryth and Pre Eryth respectively. As of now, there is no negative marker genes provided under `geneSymbolmore2`. ## Output files -Please include a description of the output from your analysis, including: +Running `scripts/00-01_processing_rds.R` will generate two types of output: + +- `rds` objects in `scratch/` + +- umap plots showing leiden clustering in `plots/` -- What type of files are created? -- What are the contents of the output files? -- Where are the files stored? -- Are any intermediate files generated? -If so, where are they stored? +Running `scripts/02-03_annotation.R` will generate several outputs: + +- updated `rds` objects in `scratch/` + +- umap plots showing cell type and CopyKat prediction (if there is any) and dotplots showing the features added with `AddModuleScore()` in `plots/` + +- ScType results of top 10 possible cell types in a cluster (`_sctype_top10_celltypes_perCluster.txt`) and metadata file tabulating leiden cluster, cell type, low confidence cell type, and CopyKat prediction for each cell (`_metadata.txt`) in `results/` ## Software requirements -Please describe the environment system used to manage software dependencies for the module (e.g., `conda`, `renv`, `docker`). -Include the locations of any files used to set up the environment. +To run the analysis, execute the Rscript in R or Rstudio (version 4.4.0). The main libraries used are: + +- Seurat (version 5.1.0) + +- reticulate (version 1.39.0) + +- sam-algorithm (in python) + +- ScType + +- CopyKat + +The renv.lock file contains all packages and version information. All python libraries are installed in the conda environment `openscpca-cell-type-ETP-ALL-03`, and the python codes are executed in the same environment by running them in R via `reticulate`. To create and activate this environment from `.yml` file use: + +``` +conda-lock install --name openscpca-cell-type-ETP-ALL-03 conda-lock.yml +``` ## Computational resources -Please indicate the computational resources that are required to run the module. -If there are specific memory, CPU, and/or GPU requirements, please state that. +All the commands above are currently executed in the standard 4XL virtual machine via AWS Lightsail for Research, but it runs pretty slow for CopyKat with one computational core. diff --git a/analyses/cell-type-ETP-ALL-03/results/README.md b/analyses/cell-type-ETP-ALL-03/results/README.md index af51c690e..fdb40dd83 100644 --- a/analyses/cell-type-ETP-ALL-03/results/README.md +++ b/analyses/cell-type-ETP-ALL-03/results/README.md @@ -1,5 +1,16 @@ # Results directory instructions -Files in the results directory should not be directly committed to the repository. +Run the following codes to sync the results in a Lightsail virtual computer: -Instead, copy results files to an S3 bucket and add a link to the S3 location in this README file. +``` +cd /home/lightsail-user/OpenScPCA-analysis +scripts/sync-results.py cell-type-ETP-ALL-03 --bucket researcher-650251722463-us-east-2 +``` + +These are the generated outputs for each sample in the S3 bucket: + +- `rds` objects: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/results/rds` + +- metadata and ScType results: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/results/` + +- umap and dot plots: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/plots` From 67cab4e9e6840edce7fddf1831efa6abddc2d2a4 Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Thu, 3 Oct 2024 19:04:36 +0000 Subject: [PATCH 05/18] updated scripts for nonETP --- .../Azimuth_BM_level1.csv | 2 +- analyses/cell-type-ETP-ALL-03/README.md | 2 +- .../scripts/00-01_processing_rds.R | 79 ++--------------- .../scripts/02-03_annotation.R | 84 ++++++++++--------- 4 files changed, 53 insertions(+), 114 deletions(-) diff --git a/analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv b/analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv index 5f915875a..c31f31785 100644 --- a/analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv +++ b/analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv @@ -1,4 +1,4 @@ -tissueType,cellName,geneSymbolmore1,geneSymbolmore2,fullName,ontologyID +tissueType,cellName,ensembl_id_positive_marker,ensembl_id_negative_marker,fullName,ontologyID Immune system,B,"ENSG00000163534,ENSG00000132704,ENSG00000012124,ENSG00000138639,ENSG00000153064,ENSG00000156738,ENSG00000116191,ENSG00000104894,ENSG00000133789,ENSG00000105369",,B cell,CL_0000945 Immune system,CD4 T,"ENSG00000172005,ENSG00000138795,ENSG00000168685,ENSG00000081059,ENSG00000227507,ENSG00000104660,ENSG00000198851,ENSG00000167286,ENSG00000160654,ENSG00000139193",,CD4 T cell,CL_0000624 Immune system,CD8 T,"ENSG00000172116,ENSG00000153563,ENSG00000184613,ENSG00000167286,ENSG00000198851,ENSG00000160307,ENSG00000100450,ENSG00000160654,ENSG00000227191,ENSG00000271503",,CD8 T cell,CL_0000625 diff --git a/analyses/cell-type-ETP-ALL-03/README.md b/analyses/cell-type-ETP-ALL-03/README.md index 8e8149804..d713df3ab 100644 --- a/analyses/cell-type-ETP-ALL-03/README.md +++ b/analyses/cell-type-ETP-ALL-03/README.md @@ -42,7 +42,7 @@ The `scripts/00-01_processing_rds.R` requires the processed SingleCellExperiment ../../download-results.py --projects SCPCP000003 --modules doublet-detection ``` -As for the annotation, `scripts/02-03_annotation.R` requires cell type marker gene file, `Azimuth_BM_level1.csv`, as an input for ScType. This excel file contains a list of positive marker genes in Ensembl ID under `geneSymbolmore1` for each cell type; *TMEM56* and *CD235a* are not detected in our dataset, thus they are being removed as part of the markers for Late Eryth and Pre Eryth respectively. As of now, there is no negative marker genes provided under `geneSymbolmore2`. +As for the annotation, `scripts/02-03_annotation.R` requires cell type marker gene file, `Azimuth_BM_level1.csv`, as an input for ScType. This excel file contains a list of positive marker genes in Ensembl ID under `ensembl_id_positive_marker` for each cell type; *TMEM56* and *CD235a* are not detected in our dataset, thus they are being removed as part of the markers for Late Eryth and Pre Eryth respectively. As of now, there is no negative marker genes provided under `ensembl_id_negative_marker`. ## Output files diff --git a/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R b/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R index de07a8662..1790d450d 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R @@ -29,9 +29,9 @@ run_sam <- function(ind.sample, ind.library){ sam$preprocess_data() sam$run(distance = 'correlation') sam$clustering(method = "leiden") #leiden clustering is the default algorithm in SAM - sam$save_anndata(paste0("sam.",ind.sample,".h5ad")) + sam$save_anndata(paste0("sam.",ind.library,".h5ad")) - final.obj <- schard::h5ad2seurat(paste0("sam.",ind.sample,".h5ad")) + final.obj <- schard::h5ad2seurat(paste0("sam.",ind.library,".h5ad")) final.obj <- AddMetaData(final.obj, as.data.frame(colData(sce))) final.obj[["RNA"]]@counts <- counts(sce) final.obj[["RNA"]] <- AddMetaData(final.obj[["RNA"]], as.data.frame(rowData(sce))) @@ -42,68 +42,12 @@ run_sam <- function(ind.sample, ind.library){ final.obj[["ADT"]]@data <- logcounts(altExp(sce, "adt")) final.obj[["ADT"]] <- AddMetaData(final.obj[["ADT"]], as.data.frame(rowData(altExp(sce, "adt")))) - saveRDS(final.obj, file.path(out_loc,"results/rds",paste0(ind.sample,".rds"))) - unlink(paste0("sam.",ind.sample,".h5ad")) + saveRDS(final.obj, file.path(out_loc,"results/rds",paste0(ind.library,".rds"))) + unlink(paste0("sam.",ind.library,".h5ad")) DimPlot(final.obj, reduction = "Xumap_", group.by = "leiden_clusters", label = T) + - ggtitle(paste0(ind.sample,": leiden_clusters")) - ggsave(file.path(out_loc,"plots",paste0(ind.sample,"_leidenCluster.pdf")), width = 7, height = 7) -} - -run_multisam <- function(ind.sam, libraries){ - seu.list <- list() - rowData_RNA <- c() - rowData_ADT <- c() - for (i in 1:length(libraries)){ - seu.list[[i]] <- as.Seurat(readRDS(file.path(data_loc,ind.sam,paste0(libraries[i],"_processed.rds")))) - #reading in doublets information - doublets.data <- read.table(file = file.path(doublet_loc,ind.sam,paste0(libraries[i],"_processed_scdblfinder.tsv")), - sep = "\t", header = T) - idx <- match(colnames(seu.list[[i]]), doublets.data$barcodes) - seu.list[[i]]$doublet_class <- doublets.data$class[idx] - seu.list[[i]]$libraryID <- libraries[i] - if (i == 1){ - rowData_RNA <- as.data.frame(seu.list[[i]][["originalexp"]]@meta.features) - rowData_ADT <- as.data.frame(seu.list[[i]][["adt"]]@meta.features) - } - else{ - rowData_RNA <- cbind(rowData_RNA, as.data.frame(seu.list[[i]][["originalexp"]]@meta.features[,3:4])) - rowData_ADT <- cbind(rowData_ADT, as.data.frame(seu.list[[i]][["adt"]]@meta.features[,3:4])) - } - } - colnames(rowData_RNA)[3:ncol(rowData_RNA)] <- paste0(rep(libraries, each = 2),"-",colnames(rowData_RNA)[3:ncol(rowData_RNA)]) - colnames(rowData_ADT)[3:ncol(rowData_ADT)] <- paste0(rep(libraries, each = 2),"-",colnames(rowData_ADT)[3:ncol(rowData_ADT)]) - sample.combined <- merge(seu.list[[1]], y = seu.list[2:length(libraries)], add.cell.ids = libraries, project = ind.sam) - sample.combined$nCount_originalexp <- NULL - sample.combined$nFeature_originalexp <- NULL - DefaultAssay(sample.combined) <- "originalexp" - - #step 01 feature selection/dimensionality using SAM - sam = samalg$SAM(counts = c(r_to_py(t(sample.combined[["originalexp"]]@counts)), - r_to_py(as.array(rownames(sample.combined))), - r_to_py(as.array(colnames(sample.combined))))) - sam$preprocess_data() - sam$run(distance = 'correlation') - sam$clustering(method = "leiden") #leiden clustering is the default algorithm in SAM - sam$save_anndata(paste0("sam.",ind.sam,".h5ad")) - - final.obj <- schard::h5ad2seurat(paste0("sam.",ind.sam,".h5ad")) - final.obj <- AddMetaData(final.obj, as.data.frame(sample.combined@meta.data)) - final.obj[["RNA"]]@counts <- sample.combined[["originalexp"]]@counts - final.obj[["RNA"]] <- AddMetaData(final.obj[["RNA"]], rowData_RNA) - final.obj$nCount_RNA <- final.obj$sum - final.obj$nFeature_RNA <- final.obj$detected - # create a new assay to store ADT information - final.obj[["ADT"]] <- CreateAssayObject(counts = sample.combined[["adt"]]@counts) - final.obj[["ADT"]]@data <- sample.combined[["adt"]]@data - final.obj[["ADT"]] <- AddMetaData(final.obj[["ADT"]], rowData_ADT) - - saveRDS(final.obj, file.path(out_loc,"results/rds",paste0(ind.sam,".rds"))) - unlink(paste0("sam.",ind.sam,".h5ad")) - - DimPlot(final.obj, reduction = "Xumap_", group.by = "leiden_clusters", split.by = "libraryID", label = T) + - ggtitle(paste0(ind.sam,": leiden_clusters")) - ggsave(file.path(out_loc,"plots",paste0(ind.sam,"_leidenCluster.pdf")), width = 10, height = 5) + ggtitle(paste0(ind.library,": leiden_clusters")) + ggsave(file.path(out_loc,"plots",paste0(ind.library,"_leidenCluster.png")), width = 7, height = 7) } project_root <- rprojroot::find_root(rprojroot::is_git_root) @@ -111,6 +55,7 @@ projectID <- "SCPCP000003" out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") data_loc <- file.path(project_root, "data/current",projectID) doublet_loc <- file.path(project_root, "data/current/results/doublet-detection",projectID) +dir.create(file.path(out_loc, "results/rds"), showWarnings = FALSE) metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) metadata <- metadata[which(metadata$scpca_project_id == projectID & @@ -118,14 +63,4 @@ metadata <- metadata[which(metadata$scpca_project_id == projectID & sampleID <- metadata$scpca_sample_id libraryID <- metadata$scpca_library_id -## removing SCPCS000066 from the list of sampleID, because there are two libraries for this sample -special.case <- c("SCPCS000066") -special.library <- libraryID[which(sampleID==special.case)] -sampleID <- setdiff(sampleID, special.case) -libraryID <- setdiff(libraryID, special.library) - purrr::walk2(sampleID, libraryID, run_sam) - -run_multisam(special.case, special.library) - - diff --git a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R index 93fc80f2d..4b1c62cd0 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R @@ -6,23 +6,25 @@ #Based on the annotated B cells, we provide them as the normal cells for CopyKat to identify tumor cells. #The outputs are final rds file and two umap plots showing cell type and tumor/normal cells status. -lapply(c("dplyr","Seurat","HGNChelper","openxlsx"), library, character.only = T) +library(dplyr) +library(Seurat) +library(HGNChelper) library(copykat) library(ggplot2) # load gene set preparation function #source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/gene_sets_prepare.R") # load cell type annotation function -source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/sctype_score_.R") +source("https://github.com/IanevskiAleksandr/sc-type/raw/6db9eef49f185cf4d79bfec92a20fcf1edcccafb/R/sctype_score_.R") gene_sets_prepare <- function(path_to_db_file, cell_type){ - cell_markers = openxlsx::read.xlsx(path_to_db_file) + cell_markers = read.csv(path_to_db_file, header = T) cell_markers = cell_markers[cell_markers$tissueType == cell_type,] - cell_markers$geneSymbolmore1 = gsub(" ","",cell_markers$geneSymbolmore1); cell_markers$geneSymbolmore2 = gsub(" ","",cell_markers$geneSymbolmore2) + cell_markers$ensembl_id_positive_marker = gsub(" ","",cell_markers$ensembl_id_positive_marker); cell_markers$ensembl_id_negative_marker = gsub(" ","",cell_markers$ensembl_id_negative_marker) # correct gene symbols from the given DB (up-genes) - cell_markers$geneSymbolmore1 = sapply(1:nrow(cell_markers), function(i){ - markers_all = gsub(" ", "", unlist(strsplit(cell_markers$geneSymbolmore1[i],","))) + cell_markers$ensembl_id_positive_marker = sapply(1:nrow(cell_markers), function(i){ + markers_all = gsub(" ", "", unlist(strsplit(cell_markers$ensembl_id_positive_marker[i],","))) markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) markers_all = sort(markers_all) @@ -35,8 +37,8 @@ gene_sets_prepare <- function(path_to_db_file, cell_type){ }) # correct gene symbols from the given DB (down-genes) - cell_markers$geneSymbolmore2 = sapply(1:nrow(cell_markers), function(i){ - markers_all = gsub(" ", "", unlist(strsplit(cell_markers$geneSymbolmore2[i],","))) + cell_markers$ensembl_id_negative_marker = sapply(1:nrow(cell_markers), function(i){ + markers_all = gsub(" ", "", unlist(strsplit(cell_markers$ensembl_id_negative_marker[i],","))) markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) markers_all = sort(markers_all) @@ -48,25 +50,27 @@ gene_sets_prepare <- function(path_to_db_file, cell_type){ } }) - cell_markers$geneSymbolmore1 = gsub("///",",",cell_markers$geneSymbolmore1);cell_markers$geneSymbolmore1 = gsub(" ","",cell_markers$geneSymbolmore1) - cell_markers$geneSymbolmore2 = gsub("///",",",cell_markers$geneSymbolmore2);cell_markers$geneSymbolmore2 = gsub(" ","",cell_markers$geneSymbolmore2) + cell_markers$ensembl_id_positive_marker = gsub("///",",",cell_markers$ensembl_id_positive_marker);cell_markers$ensembl_id_positive_marker = gsub(" ","",cell_markers$ensembl_id_positive_marker) + cell_markers$ensembl_id_negative_marker = gsub("///",",",cell_markers$ensembl_id_negative_marker);cell_markers$ensembl_id_negative_marker = gsub(" ","",cell_markers$ensembl_id_negative_marker) - gs = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$geneSymbolmore1[j]),",")))); names(gs) = cell_markers$cellName - gs2 = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$geneSymbolmore2[j]),",")))); names(gs2) = cell_markers$cellName + gs = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$ensembl_id_positive_marker[j]),",")))); names(gs) = cell_markers$cellName + gs2 = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$ensembl_id_negative_marker[j]),",")))); names(gs2) = cell_markers$cellName list(gs_positive = gs, gs_negative = gs2) } -running_scType <- function(xls, tissue, annot.obj, assay = "RNA", thres = 4){ - gs_list <- gene_sets_prepare(xls, tissue) #prepare gene sets +running_scType <- function(gs_list, annot.obj, assay = "RNA", thres = 4){ # check Seurat object version (scRNA-seq matrix extracted differently in Seurat v4/v5) seurat_package_v5 <- isFALSE('counts' %in% names(attributes(annot.obj[[assay]]))); print(sprintf("Seurat object %s is used", ifelse(seurat_package_v5, "v5", "v4"))) # extract scaled scRNA-seq matrix scRNAseqData_scaled <- if (seurat_package_v5) as.matrix(annot.obj[[assay]]$scale.data) else as.matrix(annot.obj[[assay]]@scale.data) - # run ScType + # run ScType (es.max is a table with #cell types as row and #cells as column, recording the sctype score for each cell/celltype combination) es.max <- sctype_score(scRNAseqData = scRNAseqData_scaled, scaled = TRUE, gs = gs_list$gs_positive, gs2 = gs_list$gs_negative) - # merge by cluster + # merge by cluster [repetitively finding top 10 cell types with the highest total ScType score for each cluster] + ## es.max.cl - stores the descending value of total ScType score for each cell type in a particular cluster + ## The following line prints out the top 10 cell types with the highest total ScType score for a cluster, along with #cells in that cluster + ## which is then stored in cL_results (_sctype_top10_celltypes_perCluster.txt) - table with 4 columns (clusterID, cell type, total ScType score, #cells) cL_results <- do.call("rbind", lapply(unique(annot.obj@meta.data$leiden_clusters), function(cl){ es.max.cl = sort(rowSums(es.max[ ,rownames(annot.obj@meta.data[annot.obj@meta.data$leiden_clusters==cl, ])]), decreasing = !0) head(data.frame(cluster = cl, type = names(es.max.cl), scores = es.max.cl, ncells = sum(annot.obj@meta.data$leiden_clusters==cl)), 10) @@ -77,15 +81,14 @@ running_scType <- function(xls, tissue, annot.obj, assay = "RNA", thres = 4){ print(sctype_scores[,1:3]) annot.obj@meta.data$sctype_classification = "" - for(j in unique(sctype_scores$cluster)){ - cl_type = sctype_scores[sctype_scores$cluster==j,]; - annot.obj@meta.data$sctype_classification[annot.obj@meta.data$leiden_clusters == j] = as.character(cl_type$type[1]) + for(cluster_num in unique(sctype_scores$cluster)){ + cl_type = sctype_scores[sctype_scores$cluster==cluster_num,]; + annot.obj@meta.data$sctype_classification[annot.obj@meta.data$leiden_clusters == cluster_num] = as.character(cl_type$type[1]) } return (list(annot.obj, cL_results)) } -plot_modulescore <- function(xls, tissue, seu, sample.name){ - gs_list <- gene_sets_prepare(xls, tissue) #prepare gene sets +plot_modulescore <- function(gs_list, seu, sample.name){ for (i in 1:length(gs_list$gs_positive)){ seu <- AddModuleScore(object = seu, name = paste0(gsub(" ","",names(gs_list$gs_positive[i])),"_Features"), features = list(gs_list$gs_positive[[i]])) @@ -95,42 +98,44 @@ plot_modulescore <- function(xls, tissue, seu, sample.name){ DotPlot(seu, features = features.vec) + theme(axis.text.x = element_text(angle=45,hjust=1)) + ggtitle(paste0(sample.name,": cell type module score")) + scale_color_gradient2(low = scales::muted("blue"), mid = "whitesmoke", high = scales::muted("red"), midpoint = 0) - ggsave(file.path(out_loc,"plots",paste0(sample.name,"_features_dotplot.pdf")), width = 7, height = 7) + ggsave(file.path(out_loc,"plots",paste0(sample.name,"_features_dotplot.png")), width = 7, height = 7) } -run_annot <- function(ind.sam){ - seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.sam,".rds"))) +run_annot <- function(ind.lib){ + seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) final.obj <- seu #so I can keep an object without scale.data (it is taking too much space to save scale.data) seu <- ScaleData(seu, features = rownames(seu)) + gs_list <- gene_sets_prepare(db, tissue) #prepare gene sets ## relaxing the threshold of sctype_score larger than %ncells in a cluster, from 25% (default) to 10% - res <- running_scType(xls, tissue, seu, thres = 10) #cell type annotation + res <- running_scType(gs_list, seu, thres = 10) #cell type annotation seu$lowConfidence_annot <- res[[1]]$sctype_classification #seurat object with 'sctype_classification' metadata added - res <- running_scType(xls, tissue, seu) + res <- running_scType(gs_list, seu) seu <- res[[1]] #res[[2]] - A table with top 10 cell types with the highest scores for each cluster - write.table(res[[2]], file = file.path(out_loc,"results",paste0(ind.sam,"_sctype_top10_celltypes_perCluster.txt")), + write.table(res[[2]], file = file.path(out_loc,"results",paste0(ind.lib,"_sctype_top10_celltypes_perCluster.txt")), row.names = F, sep = "\t", quote = F) p1 <- DimPlot(seu, reduction = "Xumap_", group.by = "sctype_classification", cols = ct_color) + - ggtitle(paste0(ind.sam,": cell type")) + ggtitle(paste0(ind.lib,": cell type")) p2 <- DimPlot(seu, reduction = "Xumap_", group.by = "lowConfidence_annot", cols = ct_color) + - ggtitle(paste0(ind.sam,": low confidence annotation")) + ggtitle(paste0(ind.lib,": low confidence annotation")) p1 + p2 - ggsave(file.path(out_loc,"plots",paste0(ind.sam,"_celltype.pdf")), width = 10, height = 5) + ggsave(file.path(out_loc,"plots",paste0(ind.lib,"_celltype.png")), width = 10, height = 5) - plot_modulescore(xls, tissue, seu, ind.sam) + plot_modulescore(gs_list, seu, ind.lib) #using copykat for tumor cells identification norm.cells <- colnames(seu)[which(seu$sctype_classification=="B")] + n_cores <- parallel::detectCores() - 1 if (length(norm.cells) > 0){ #the sample has B cells annotated copykat.test <- copykat(rawmat=seu@assays[["RNA"]]@counts, id.type="Ensemble", - ngene.chr=5, win.size=25, KS.cut=0.1, sam.name=ind.sam, + ngene.chr=5, win.size=25, KS.cut=0.1, sam.name=ind.lib, distance="euclidean", norm.cell.names=norm.cells, - output.seg="FALSE", plot.genes="TRUE", genome="hg20",n.cores=50) + output.seg="FALSE", plot.genes="TRUE", genome="hg20",n.cores=n_cores) idx <- match(colnames(seu), copykat.test$prediction$cell.names) seu$copykat.pred <- copykat.test$prediction$copykat.pred[idx] DimPlot(seu, reduction = "Xumap_", group.by = "copykat.pred") + - ggtitle(paste0(ind.sam,": copykat prediction")) - ggsave(file.path(out_loc,"plots",paste0(ind.sam,"_copykatPred.pdf")), width = 7, height = 7) + ggtitle(paste0(ind.lib,": copykat prediction")) + ggsave(file.path(out_loc,"plots",paste0(ind.lib,"_copykatPred.png")), width = 7, height = 7) voi <- c('leiden_clusters','sctype_classification','lowConfidence_annot','copykat.pred') final.obj$copykat.pred <- seu$copykat.pred @@ -139,10 +144,10 @@ run_annot <- function(ind.sam){ } write.table(data.frame(FetchData(seu, vars = voi)), sep = "\t", quote = F, - file = file.path(out_loc,"results",paste0(ind.sam,"_metadata.txt"))) + file = file.path(out_loc,"results",paste0(ind.lib,"_metadata.txt"))) final.obj$sctype_classification <- seu$sctype_classification final.obj$lowConfidence_annot <- seu$lowConfidence_annot - saveRDS(final.obj, file = file.path(out_loc,"results/rds",paste0(ind.sam,".rds"))) + saveRDS(final.obj, file = file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) } @@ -154,14 +159,13 @@ data_loc <- file.path(project_root, "data/current",projectID) metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) metadata <- metadata[which(metadata$scpca_project_id == projectID & metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] -sampleID <- unique(metadata$scpca_sample_id) +libraryID <- metadata$scpca_library_id # DB file db <- file.path(out_loc,"Azimuth_BM_level1.csv") -xls <- openxlsx::buildWorkbook(read.csv(db, header = T)) tissue <- "Immune system" ct_color <- c("darkorchid","skyblue2","dodgerblue2","gold","beige","sienna1","green4","navy", "chocolate4","red","darkred","#6A3D9A","maroon","yellow4","grey35","black","lightpink","grey80") names(ct_color) <- c("B","CD4 T","CD8 T","DC","HSPC","Mono","NK","Other T","Macrophage", "Early Eryth","Late Eryth","Plasma","Platelet","Stromal","Blast","Cancer","Pre Eryth","Unknown") -purrr::walk(sampleID, run_annot) +purrr::walk(libraryID, run_annot) From 5f03ef8992068e81fecb4a678556ce05226dcef2 Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Thu, 3 Oct 2024 19:08:23 +0000 Subject: [PATCH 06/18] updated script for ETP --- .../Azimuth_BM_level1.csv | 2 +- analyses/cell-type-ETP-ALL-03/README.md | 2 +- .../scripts/00-01_processing_rds.R | 79 ++--------------- .../scripts/02-03_annotation.R | 84 ++++++++++--------- 4 files changed, 53 insertions(+), 114 deletions(-) diff --git a/analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv b/analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv index 5f915875a..c31f31785 100644 --- a/analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv +++ b/analyses/cell-type-ETP-ALL-03/Azimuth_BM_level1.csv @@ -1,4 +1,4 @@ -tissueType,cellName,geneSymbolmore1,geneSymbolmore2,fullName,ontologyID +tissueType,cellName,ensembl_id_positive_marker,ensembl_id_negative_marker,fullName,ontologyID Immune system,B,"ENSG00000163534,ENSG00000132704,ENSG00000012124,ENSG00000138639,ENSG00000153064,ENSG00000156738,ENSG00000116191,ENSG00000104894,ENSG00000133789,ENSG00000105369",,B cell,CL_0000945 Immune system,CD4 T,"ENSG00000172005,ENSG00000138795,ENSG00000168685,ENSG00000081059,ENSG00000227507,ENSG00000104660,ENSG00000198851,ENSG00000167286,ENSG00000160654,ENSG00000139193",,CD4 T cell,CL_0000624 Immune system,CD8 T,"ENSG00000172116,ENSG00000153563,ENSG00000184613,ENSG00000167286,ENSG00000198851,ENSG00000160307,ENSG00000100450,ENSG00000160654,ENSG00000227191,ENSG00000271503",,CD8 T cell,CL_0000625 diff --git a/analyses/cell-type-ETP-ALL-03/README.md b/analyses/cell-type-ETP-ALL-03/README.md index 8e8149804..d713df3ab 100644 --- a/analyses/cell-type-ETP-ALL-03/README.md +++ b/analyses/cell-type-ETP-ALL-03/README.md @@ -42,7 +42,7 @@ The `scripts/00-01_processing_rds.R` requires the processed SingleCellExperiment ../../download-results.py --projects SCPCP000003 --modules doublet-detection ``` -As for the annotation, `scripts/02-03_annotation.R` requires cell type marker gene file, `Azimuth_BM_level1.csv`, as an input for ScType. This excel file contains a list of positive marker genes in Ensembl ID under `geneSymbolmore1` for each cell type; *TMEM56* and *CD235a* are not detected in our dataset, thus they are being removed as part of the markers for Late Eryth and Pre Eryth respectively. As of now, there is no negative marker genes provided under `geneSymbolmore2`. +As for the annotation, `scripts/02-03_annotation.R` requires cell type marker gene file, `Azimuth_BM_level1.csv`, as an input for ScType. This excel file contains a list of positive marker genes in Ensembl ID under `ensembl_id_positive_marker` for each cell type; *TMEM56* and *CD235a* are not detected in our dataset, thus they are being removed as part of the markers for Late Eryth and Pre Eryth respectively. As of now, there is no negative marker genes provided under `ensembl_id_negative_marker`. ## Output files diff --git a/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R b/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R index de07a8662..1790d450d 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R @@ -29,9 +29,9 @@ run_sam <- function(ind.sample, ind.library){ sam$preprocess_data() sam$run(distance = 'correlation') sam$clustering(method = "leiden") #leiden clustering is the default algorithm in SAM - sam$save_anndata(paste0("sam.",ind.sample,".h5ad")) + sam$save_anndata(paste0("sam.",ind.library,".h5ad")) - final.obj <- schard::h5ad2seurat(paste0("sam.",ind.sample,".h5ad")) + final.obj <- schard::h5ad2seurat(paste0("sam.",ind.library,".h5ad")) final.obj <- AddMetaData(final.obj, as.data.frame(colData(sce))) final.obj[["RNA"]]@counts <- counts(sce) final.obj[["RNA"]] <- AddMetaData(final.obj[["RNA"]], as.data.frame(rowData(sce))) @@ -42,68 +42,12 @@ run_sam <- function(ind.sample, ind.library){ final.obj[["ADT"]]@data <- logcounts(altExp(sce, "adt")) final.obj[["ADT"]] <- AddMetaData(final.obj[["ADT"]], as.data.frame(rowData(altExp(sce, "adt")))) - saveRDS(final.obj, file.path(out_loc,"results/rds",paste0(ind.sample,".rds"))) - unlink(paste0("sam.",ind.sample,".h5ad")) + saveRDS(final.obj, file.path(out_loc,"results/rds",paste0(ind.library,".rds"))) + unlink(paste0("sam.",ind.library,".h5ad")) DimPlot(final.obj, reduction = "Xumap_", group.by = "leiden_clusters", label = T) + - ggtitle(paste0(ind.sample,": leiden_clusters")) - ggsave(file.path(out_loc,"plots",paste0(ind.sample,"_leidenCluster.pdf")), width = 7, height = 7) -} - -run_multisam <- function(ind.sam, libraries){ - seu.list <- list() - rowData_RNA <- c() - rowData_ADT <- c() - for (i in 1:length(libraries)){ - seu.list[[i]] <- as.Seurat(readRDS(file.path(data_loc,ind.sam,paste0(libraries[i],"_processed.rds")))) - #reading in doublets information - doublets.data <- read.table(file = file.path(doublet_loc,ind.sam,paste0(libraries[i],"_processed_scdblfinder.tsv")), - sep = "\t", header = T) - idx <- match(colnames(seu.list[[i]]), doublets.data$barcodes) - seu.list[[i]]$doublet_class <- doublets.data$class[idx] - seu.list[[i]]$libraryID <- libraries[i] - if (i == 1){ - rowData_RNA <- as.data.frame(seu.list[[i]][["originalexp"]]@meta.features) - rowData_ADT <- as.data.frame(seu.list[[i]][["adt"]]@meta.features) - } - else{ - rowData_RNA <- cbind(rowData_RNA, as.data.frame(seu.list[[i]][["originalexp"]]@meta.features[,3:4])) - rowData_ADT <- cbind(rowData_ADT, as.data.frame(seu.list[[i]][["adt"]]@meta.features[,3:4])) - } - } - colnames(rowData_RNA)[3:ncol(rowData_RNA)] <- paste0(rep(libraries, each = 2),"-",colnames(rowData_RNA)[3:ncol(rowData_RNA)]) - colnames(rowData_ADT)[3:ncol(rowData_ADT)] <- paste0(rep(libraries, each = 2),"-",colnames(rowData_ADT)[3:ncol(rowData_ADT)]) - sample.combined <- merge(seu.list[[1]], y = seu.list[2:length(libraries)], add.cell.ids = libraries, project = ind.sam) - sample.combined$nCount_originalexp <- NULL - sample.combined$nFeature_originalexp <- NULL - DefaultAssay(sample.combined) <- "originalexp" - - #step 01 feature selection/dimensionality using SAM - sam = samalg$SAM(counts = c(r_to_py(t(sample.combined[["originalexp"]]@counts)), - r_to_py(as.array(rownames(sample.combined))), - r_to_py(as.array(colnames(sample.combined))))) - sam$preprocess_data() - sam$run(distance = 'correlation') - sam$clustering(method = "leiden") #leiden clustering is the default algorithm in SAM - sam$save_anndata(paste0("sam.",ind.sam,".h5ad")) - - final.obj <- schard::h5ad2seurat(paste0("sam.",ind.sam,".h5ad")) - final.obj <- AddMetaData(final.obj, as.data.frame(sample.combined@meta.data)) - final.obj[["RNA"]]@counts <- sample.combined[["originalexp"]]@counts - final.obj[["RNA"]] <- AddMetaData(final.obj[["RNA"]], rowData_RNA) - final.obj$nCount_RNA <- final.obj$sum - final.obj$nFeature_RNA <- final.obj$detected - # create a new assay to store ADT information - final.obj[["ADT"]] <- CreateAssayObject(counts = sample.combined[["adt"]]@counts) - final.obj[["ADT"]]@data <- sample.combined[["adt"]]@data - final.obj[["ADT"]] <- AddMetaData(final.obj[["ADT"]], rowData_ADT) - - saveRDS(final.obj, file.path(out_loc,"results/rds",paste0(ind.sam,".rds"))) - unlink(paste0("sam.",ind.sam,".h5ad")) - - DimPlot(final.obj, reduction = "Xumap_", group.by = "leiden_clusters", split.by = "libraryID", label = T) + - ggtitle(paste0(ind.sam,": leiden_clusters")) - ggsave(file.path(out_loc,"plots",paste0(ind.sam,"_leidenCluster.pdf")), width = 10, height = 5) + ggtitle(paste0(ind.library,": leiden_clusters")) + ggsave(file.path(out_loc,"plots",paste0(ind.library,"_leidenCluster.png")), width = 7, height = 7) } project_root <- rprojroot::find_root(rprojroot::is_git_root) @@ -111,6 +55,7 @@ projectID <- "SCPCP000003" out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") data_loc <- file.path(project_root, "data/current",projectID) doublet_loc <- file.path(project_root, "data/current/results/doublet-detection",projectID) +dir.create(file.path(out_loc, "results/rds"), showWarnings = FALSE) metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) metadata <- metadata[which(metadata$scpca_project_id == projectID & @@ -118,14 +63,4 @@ metadata <- metadata[which(metadata$scpca_project_id == projectID & sampleID <- metadata$scpca_sample_id libraryID <- metadata$scpca_library_id -## removing SCPCS000066 from the list of sampleID, because there are two libraries for this sample -special.case <- c("SCPCS000066") -special.library <- libraryID[which(sampleID==special.case)] -sampleID <- setdiff(sampleID, special.case) -libraryID <- setdiff(libraryID, special.library) - purrr::walk2(sampleID, libraryID, run_sam) - -run_multisam(special.case, special.library) - - diff --git a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R index 93fc80f2d..4b1c62cd0 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R @@ -6,23 +6,25 @@ #Based on the annotated B cells, we provide them as the normal cells for CopyKat to identify tumor cells. #The outputs are final rds file and two umap plots showing cell type and tumor/normal cells status. -lapply(c("dplyr","Seurat","HGNChelper","openxlsx"), library, character.only = T) +library(dplyr) +library(Seurat) +library(HGNChelper) library(copykat) library(ggplot2) # load gene set preparation function #source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/gene_sets_prepare.R") # load cell type annotation function -source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/sctype_score_.R") +source("https://github.com/IanevskiAleksandr/sc-type/raw/6db9eef49f185cf4d79bfec92a20fcf1edcccafb/R/sctype_score_.R") gene_sets_prepare <- function(path_to_db_file, cell_type){ - cell_markers = openxlsx::read.xlsx(path_to_db_file) + cell_markers = read.csv(path_to_db_file, header = T) cell_markers = cell_markers[cell_markers$tissueType == cell_type,] - cell_markers$geneSymbolmore1 = gsub(" ","",cell_markers$geneSymbolmore1); cell_markers$geneSymbolmore2 = gsub(" ","",cell_markers$geneSymbolmore2) + cell_markers$ensembl_id_positive_marker = gsub(" ","",cell_markers$ensembl_id_positive_marker); cell_markers$ensembl_id_negative_marker = gsub(" ","",cell_markers$ensembl_id_negative_marker) # correct gene symbols from the given DB (up-genes) - cell_markers$geneSymbolmore1 = sapply(1:nrow(cell_markers), function(i){ - markers_all = gsub(" ", "", unlist(strsplit(cell_markers$geneSymbolmore1[i],","))) + cell_markers$ensembl_id_positive_marker = sapply(1:nrow(cell_markers), function(i){ + markers_all = gsub(" ", "", unlist(strsplit(cell_markers$ensembl_id_positive_marker[i],","))) markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) markers_all = sort(markers_all) @@ -35,8 +37,8 @@ gene_sets_prepare <- function(path_to_db_file, cell_type){ }) # correct gene symbols from the given DB (down-genes) - cell_markers$geneSymbolmore2 = sapply(1:nrow(cell_markers), function(i){ - markers_all = gsub(" ", "", unlist(strsplit(cell_markers$geneSymbolmore2[i],","))) + cell_markers$ensembl_id_negative_marker = sapply(1:nrow(cell_markers), function(i){ + markers_all = gsub(" ", "", unlist(strsplit(cell_markers$ensembl_id_negative_marker[i],","))) markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) markers_all = sort(markers_all) @@ -48,25 +50,27 @@ gene_sets_prepare <- function(path_to_db_file, cell_type){ } }) - cell_markers$geneSymbolmore1 = gsub("///",",",cell_markers$geneSymbolmore1);cell_markers$geneSymbolmore1 = gsub(" ","",cell_markers$geneSymbolmore1) - cell_markers$geneSymbolmore2 = gsub("///",",",cell_markers$geneSymbolmore2);cell_markers$geneSymbolmore2 = gsub(" ","",cell_markers$geneSymbolmore2) + cell_markers$ensembl_id_positive_marker = gsub("///",",",cell_markers$ensembl_id_positive_marker);cell_markers$ensembl_id_positive_marker = gsub(" ","",cell_markers$ensembl_id_positive_marker) + cell_markers$ensembl_id_negative_marker = gsub("///",",",cell_markers$ensembl_id_negative_marker);cell_markers$ensembl_id_negative_marker = gsub(" ","",cell_markers$ensembl_id_negative_marker) - gs = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$geneSymbolmore1[j]),",")))); names(gs) = cell_markers$cellName - gs2 = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$geneSymbolmore2[j]),",")))); names(gs2) = cell_markers$cellName + gs = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$ensembl_id_positive_marker[j]),",")))); names(gs) = cell_markers$cellName + gs2 = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$ensembl_id_negative_marker[j]),",")))); names(gs2) = cell_markers$cellName list(gs_positive = gs, gs_negative = gs2) } -running_scType <- function(xls, tissue, annot.obj, assay = "RNA", thres = 4){ - gs_list <- gene_sets_prepare(xls, tissue) #prepare gene sets +running_scType <- function(gs_list, annot.obj, assay = "RNA", thres = 4){ # check Seurat object version (scRNA-seq matrix extracted differently in Seurat v4/v5) seurat_package_v5 <- isFALSE('counts' %in% names(attributes(annot.obj[[assay]]))); print(sprintf("Seurat object %s is used", ifelse(seurat_package_v5, "v5", "v4"))) # extract scaled scRNA-seq matrix scRNAseqData_scaled <- if (seurat_package_v5) as.matrix(annot.obj[[assay]]$scale.data) else as.matrix(annot.obj[[assay]]@scale.data) - # run ScType + # run ScType (es.max is a table with #cell types as row and #cells as column, recording the sctype score for each cell/celltype combination) es.max <- sctype_score(scRNAseqData = scRNAseqData_scaled, scaled = TRUE, gs = gs_list$gs_positive, gs2 = gs_list$gs_negative) - # merge by cluster + # merge by cluster [repetitively finding top 10 cell types with the highest total ScType score for each cluster] + ## es.max.cl - stores the descending value of total ScType score for each cell type in a particular cluster + ## The following line prints out the top 10 cell types with the highest total ScType score for a cluster, along with #cells in that cluster + ## which is then stored in cL_results (_sctype_top10_celltypes_perCluster.txt) - table with 4 columns (clusterID, cell type, total ScType score, #cells) cL_results <- do.call("rbind", lapply(unique(annot.obj@meta.data$leiden_clusters), function(cl){ es.max.cl = sort(rowSums(es.max[ ,rownames(annot.obj@meta.data[annot.obj@meta.data$leiden_clusters==cl, ])]), decreasing = !0) head(data.frame(cluster = cl, type = names(es.max.cl), scores = es.max.cl, ncells = sum(annot.obj@meta.data$leiden_clusters==cl)), 10) @@ -77,15 +81,14 @@ running_scType <- function(xls, tissue, annot.obj, assay = "RNA", thres = 4){ print(sctype_scores[,1:3]) annot.obj@meta.data$sctype_classification = "" - for(j in unique(sctype_scores$cluster)){ - cl_type = sctype_scores[sctype_scores$cluster==j,]; - annot.obj@meta.data$sctype_classification[annot.obj@meta.data$leiden_clusters == j] = as.character(cl_type$type[1]) + for(cluster_num in unique(sctype_scores$cluster)){ + cl_type = sctype_scores[sctype_scores$cluster==cluster_num,]; + annot.obj@meta.data$sctype_classification[annot.obj@meta.data$leiden_clusters == cluster_num] = as.character(cl_type$type[1]) } return (list(annot.obj, cL_results)) } -plot_modulescore <- function(xls, tissue, seu, sample.name){ - gs_list <- gene_sets_prepare(xls, tissue) #prepare gene sets +plot_modulescore <- function(gs_list, seu, sample.name){ for (i in 1:length(gs_list$gs_positive)){ seu <- AddModuleScore(object = seu, name = paste0(gsub(" ","",names(gs_list$gs_positive[i])),"_Features"), features = list(gs_list$gs_positive[[i]])) @@ -95,42 +98,44 @@ plot_modulescore <- function(xls, tissue, seu, sample.name){ DotPlot(seu, features = features.vec) + theme(axis.text.x = element_text(angle=45,hjust=1)) + ggtitle(paste0(sample.name,": cell type module score")) + scale_color_gradient2(low = scales::muted("blue"), mid = "whitesmoke", high = scales::muted("red"), midpoint = 0) - ggsave(file.path(out_loc,"plots",paste0(sample.name,"_features_dotplot.pdf")), width = 7, height = 7) + ggsave(file.path(out_loc,"plots",paste0(sample.name,"_features_dotplot.png")), width = 7, height = 7) } -run_annot <- function(ind.sam){ - seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.sam,".rds"))) +run_annot <- function(ind.lib){ + seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) final.obj <- seu #so I can keep an object without scale.data (it is taking too much space to save scale.data) seu <- ScaleData(seu, features = rownames(seu)) + gs_list <- gene_sets_prepare(db, tissue) #prepare gene sets ## relaxing the threshold of sctype_score larger than %ncells in a cluster, from 25% (default) to 10% - res <- running_scType(xls, tissue, seu, thres = 10) #cell type annotation + res <- running_scType(gs_list, seu, thres = 10) #cell type annotation seu$lowConfidence_annot <- res[[1]]$sctype_classification #seurat object with 'sctype_classification' metadata added - res <- running_scType(xls, tissue, seu) + res <- running_scType(gs_list, seu) seu <- res[[1]] #res[[2]] - A table with top 10 cell types with the highest scores for each cluster - write.table(res[[2]], file = file.path(out_loc,"results",paste0(ind.sam,"_sctype_top10_celltypes_perCluster.txt")), + write.table(res[[2]], file = file.path(out_loc,"results",paste0(ind.lib,"_sctype_top10_celltypes_perCluster.txt")), row.names = F, sep = "\t", quote = F) p1 <- DimPlot(seu, reduction = "Xumap_", group.by = "sctype_classification", cols = ct_color) + - ggtitle(paste0(ind.sam,": cell type")) + ggtitle(paste0(ind.lib,": cell type")) p2 <- DimPlot(seu, reduction = "Xumap_", group.by = "lowConfidence_annot", cols = ct_color) + - ggtitle(paste0(ind.sam,": low confidence annotation")) + ggtitle(paste0(ind.lib,": low confidence annotation")) p1 + p2 - ggsave(file.path(out_loc,"plots",paste0(ind.sam,"_celltype.pdf")), width = 10, height = 5) + ggsave(file.path(out_loc,"plots",paste0(ind.lib,"_celltype.png")), width = 10, height = 5) - plot_modulescore(xls, tissue, seu, ind.sam) + plot_modulescore(gs_list, seu, ind.lib) #using copykat for tumor cells identification norm.cells <- colnames(seu)[which(seu$sctype_classification=="B")] + n_cores <- parallel::detectCores() - 1 if (length(norm.cells) > 0){ #the sample has B cells annotated copykat.test <- copykat(rawmat=seu@assays[["RNA"]]@counts, id.type="Ensemble", - ngene.chr=5, win.size=25, KS.cut=0.1, sam.name=ind.sam, + ngene.chr=5, win.size=25, KS.cut=0.1, sam.name=ind.lib, distance="euclidean", norm.cell.names=norm.cells, - output.seg="FALSE", plot.genes="TRUE", genome="hg20",n.cores=50) + output.seg="FALSE", plot.genes="TRUE", genome="hg20",n.cores=n_cores) idx <- match(colnames(seu), copykat.test$prediction$cell.names) seu$copykat.pred <- copykat.test$prediction$copykat.pred[idx] DimPlot(seu, reduction = "Xumap_", group.by = "copykat.pred") + - ggtitle(paste0(ind.sam,": copykat prediction")) - ggsave(file.path(out_loc,"plots",paste0(ind.sam,"_copykatPred.pdf")), width = 7, height = 7) + ggtitle(paste0(ind.lib,": copykat prediction")) + ggsave(file.path(out_loc,"plots",paste0(ind.lib,"_copykatPred.png")), width = 7, height = 7) voi <- c('leiden_clusters','sctype_classification','lowConfidence_annot','copykat.pred') final.obj$copykat.pred <- seu$copykat.pred @@ -139,10 +144,10 @@ run_annot <- function(ind.sam){ } write.table(data.frame(FetchData(seu, vars = voi)), sep = "\t", quote = F, - file = file.path(out_loc,"results",paste0(ind.sam,"_metadata.txt"))) + file = file.path(out_loc,"results",paste0(ind.lib,"_metadata.txt"))) final.obj$sctype_classification <- seu$sctype_classification final.obj$lowConfidence_annot <- seu$lowConfidence_annot - saveRDS(final.obj, file = file.path(out_loc,"results/rds",paste0(ind.sam,".rds"))) + saveRDS(final.obj, file = file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) } @@ -154,14 +159,13 @@ data_loc <- file.path(project_root, "data/current",projectID) metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) metadata <- metadata[which(metadata$scpca_project_id == projectID & metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] -sampleID <- unique(metadata$scpca_sample_id) +libraryID <- metadata$scpca_library_id # DB file db <- file.path(out_loc,"Azimuth_BM_level1.csv") -xls <- openxlsx::buildWorkbook(read.csv(db, header = T)) tissue <- "Immune system" ct_color <- c("darkorchid","skyblue2","dodgerblue2","gold","beige","sienna1","green4","navy", "chocolate4","red","darkred","#6A3D9A","maroon","yellow4","grey35","black","lightpink","grey80") names(ct_color) <- c("B","CD4 T","CD8 T","DC","HSPC","Mono","NK","Other T","Macrophage", "Early Eryth","Late Eryth","Plasma","Platelet","Stromal","Blast","Cancer","Pre Eryth","Unknown") -purrr::walk(sampleID, run_annot) +purrr::walk(libraryID, run_annot) From da692808e18fedcaaa50a5b97d9a0dcc84c30f70 Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Fri, 4 Oct 2024 15:00:27 +0000 Subject: [PATCH 07/18] updated for plotting --- analyses/cell-type-ETP-ALL-03/.gitignore | 4 ---- .../cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R | 2 +- analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R | 8 ++++---- 3 files changed, 5 insertions(+), 9 deletions(-) diff --git a/analyses/cell-type-ETP-ALL-03/.gitignore b/analyses/cell-type-ETP-ALL-03/.gitignore index 25e54d3a1..418755f52 100644 --- a/analyses/cell-type-ETP-ALL-03/.gitignore +++ b/analyses/cell-type-ETP-ALL-03/.gitignore @@ -2,10 +2,6 @@ /results/* !/results/README.md -# Plots should not be committed -/plots/* -!/plots/.gitkeep - # Ignore the scratch directory (but keep it present) /scratch/* !/scratch/.gitkeep diff --git a/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R b/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R index 1790d450d..02a5dac28 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R @@ -47,7 +47,7 @@ run_sam <- function(ind.sample, ind.library){ DimPlot(final.obj, reduction = "Xumap_", group.by = "leiden_clusters", label = T) + ggtitle(paste0(ind.library,": leiden_clusters")) - ggsave(file.path(out_loc,"plots",paste0(ind.library,"_leidenCluster.png")), width = 7, height = 7) + ggsave(file.path(out_loc,"plots",paste0(ind.library,"_leidenCluster.png")), width = 6, height = 6, dpi = 150) } project_root <- rprojroot::find_root(rprojroot::is_git_root) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R index 4b1c62cd0..30831c283 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R @@ -98,7 +98,7 @@ plot_modulescore <- function(gs_list, seu, sample.name){ DotPlot(seu, features = features.vec) + theme(axis.text.x = element_text(angle=45,hjust=1)) + ggtitle(paste0(sample.name,": cell type module score")) + scale_color_gradient2(low = scales::muted("blue"), mid = "whitesmoke", high = scales::muted("red"), midpoint = 0) - ggsave(file.path(out_loc,"plots",paste0(sample.name,"_features_dotplot.png")), width = 7, height = 7) + ggsave(file.path(out_loc,"plots",paste0(sample.name,"_features_dotplot.png")), width = 7, height = 7, bg = "white", dpi = 150) } run_annot <- function(ind.lib){ @@ -117,9 +117,9 @@ run_annot <- function(ind.lib){ p1 <- DimPlot(seu, reduction = "Xumap_", group.by = "sctype_classification", cols = ct_color) + ggtitle(paste0(ind.lib,": cell type")) p2 <- DimPlot(seu, reduction = "Xumap_", group.by = "lowConfidence_annot", cols = ct_color) + - ggtitle(paste0(ind.lib,": low confidence annotation")) + ggtitle("low confidence annotation") p1 + p2 - ggsave(file.path(out_loc,"plots",paste0(ind.lib,"_celltype.png")), width = 10, height = 5) + ggsave(file.path(out_loc,"plots",paste0(ind.lib,"_celltype.png")), width = 10, height = 4, dpi = 150) plot_modulescore(gs_list, seu, ind.lib) @@ -135,7 +135,7 @@ run_annot <- function(ind.lib){ seu$copykat.pred <- copykat.test$prediction$copykat.pred[idx] DimPlot(seu, reduction = "Xumap_", group.by = "copykat.pred") + ggtitle(paste0(ind.lib,": copykat prediction")) - ggsave(file.path(out_loc,"plots",paste0(ind.lib,"_copykatPred.png")), width = 7, height = 7) + ggsave(file.path(out_loc,"plots",paste0(ind.lib,"_copykatPred.png")), width = 6, height = 6, dpi = 150) voi <- c('leiden_clusters','sctype_classification','lowConfidence_annot','copykat.pred') final.obj$copykat.pred <- seu$copykat.pred From 7e3d4db25371033b6e128cbb2ba28ba7937b696c Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Fri, 4 Oct 2024 17:08:11 +0000 Subject: [PATCH 08/18] add multipanel R script --- .../scripts/multipanel_plot.R | 47 +++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 analyses/cell-type-ETP-ALL-03/scripts/multipanel_plot.R diff --git a/analyses/cell-type-ETP-ALL-03/scripts/multipanel_plot.R b/analyses/cell-type-ETP-ALL-03/scripts/multipanel_plot.R new file mode 100644 index 000000000..dba2922ec --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/scripts/multipanel_plot.R @@ -0,0 +1,47 @@ +#!/usr/bin/env Rscript + +#This script combines UMAP plots of all samples into one multi-panel plot + +library(Seurat) +library(ggplot2) + +multiplot <- function(meta.variable, n_row=8){ + if (meta.variable %in% c("sctype_classification","lowConfidence_annot")){ + clrs <- ct_color + }else{ + clrs <- NULL + } + plot.list <- list() + for (i in 1:length(libraryID)){ + tryCatch({ + plot.list[[i]] <- DimPlot(seu.list[[i]], reduction = "Xumap_", group.by = meta.variable, + label = T, cols = clrs, repel = T) + + ggtitle(libraryID[i]) + NoLegend() + theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) + }, error=function(e){}) + } + cowplot::plot_grid(plotlist = plot.list, nrow = n_row) + ggsave(file.path(out_loc,"plots",paste0("multipanels_",meta.variable,".png")), width = 20, height = 24, bg = "white") +} + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") +data_loc <- file.path(project_root, "data/current",projectID) + +metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +libraryID <- metadata$scpca_library_id + +ct_color <- c("darkorchid","skyblue2","dodgerblue2","gold","beige","sienna1","green4","navy", + "chocolate4","red","darkred","#6A3D9A","maroon","yellow4","grey35","black","lightpink","grey80") +names(ct_color) <- c("B","CD4 T","CD8 T","DC","HSPC","Mono","NK","Other T","Macrophage", + "Early Eryth","Late Eryth","Plasma","Platelet","Stromal","Blast","Cancer","Pre Eryth","Unknown") + +seu.list <- list() +for (i in 1:length(libraryID)){ + seu.list[[i]] <- readRDS(file.path(out_loc,"results/rds",paste0(libraryID[i],".rds"))) +} +voi <- c("leiden_clusters","sctype_classification","lowConfidence_annot","copykat.pred") + +purrr::walk(voi, multiplot) From 66e0a23dfa7fa9d42a158fd12e8dbece544cb90b Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Mon, 7 Oct 2024 19:48:03 +0000 Subject: [PATCH 09/18] updated scripts --- .../scripts/00-01_processing_rds.R | 4 --- .../scripts/02-03_annotation.R | 20 +++-------- .../scripts/multipanel_plot.R | 35 ++++++++++--------- 3 files changed, 23 insertions(+), 36 deletions(-) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R b/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R index 02a5dac28..ef3ccf42e 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/00-01_processing_rds.R @@ -44,10 +44,6 @@ run_sam <- function(ind.sample, ind.library){ saveRDS(final.obj, file.path(out_loc,"results/rds",paste0(ind.library,".rds"))) unlink(paste0("sam.",ind.library,".h5ad")) - - DimPlot(final.obj, reduction = "Xumap_", group.by = "leiden_clusters", label = T) + - ggtitle(paste0(ind.library,": leiden_clusters")) - ggsave(file.path(out_loc,"plots",paste0(ind.library,"_leidenCluster.png")), width = 6, height = 6, dpi = 150) } project_root <- rprojroot::find_root(rprojroot::is_git_root) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R index 30831c283..892545c2d 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R @@ -114,12 +114,6 @@ run_annot <- function(ind.lib){ #res[[2]] - A table with top 10 cell types with the highest scores for each cluster write.table(res[[2]], file = file.path(out_loc,"results",paste0(ind.lib,"_sctype_top10_celltypes_perCluster.txt")), row.names = F, sep = "\t", quote = F) - p1 <- DimPlot(seu, reduction = "Xumap_", group.by = "sctype_classification", cols = ct_color) + - ggtitle(paste0(ind.lib,": cell type")) - p2 <- DimPlot(seu, reduction = "Xumap_", group.by = "lowConfidence_annot", cols = ct_color) + - ggtitle("low confidence annotation") - p1 + p2 - ggsave(file.path(out_loc,"plots",paste0(ind.lib,"_celltype.png")), width = 10, height = 4, dpi = 150) plot_modulescore(gs_list, seu, ind.lib) @@ -133,17 +127,15 @@ run_annot <- function(ind.lib){ output.seg="FALSE", plot.genes="TRUE", genome="hg20",n.cores=n_cores) idx <- match(colnames(seu), copykat.test$prediction$cell.names) seu$copykat.pred <- copykat.test$prediction$copykat.pred[idx] - DimPlot(seu, reduction = "Xumap_", group.by = "copykat.pred") + - ggtitle(paste0(ind.lib,": copykat prediction")) - ggsave(file.path(out_loc,"plots",paste0(ind.lib,"_copykatPred.png")), width = 6, height = 6, dpi = 150) voi <- c('leiden_clusters','sctype_classification','lowConfidence_annot','copykat.pred') final.obj$copykat.pred <- seu$copykat.pred }else{ voi <- c('leiden_clusters','sctype_classification','lowConfidence_annot') } - - write.table(data.frame(FetchData(seu, vars = voi)), sep = "\t", quote = F, + + voi_df <- data.frame(FetchData(seu, vars = voi)) |> tibble::rownames_to_column(var = "barcode") + write.table(voi_df, sep = "\t", quote = F, row.names = F, file = file.path(out_loc,"results",paste0(ind.lib,"_metadata.txt"))) final.obj$sctype_classification <- seu$sctype_classification final.obj$lowConfidence_annot <- seu$lowConfidence_annot @@ -155,6 +147,8 @@ project_root <- rprojroot::find_root(rprojroot::is_git_root) projectID <- "SCPCP000003" out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") data_loc <- file.path(project_root, "data/current",projectID) +dir.create(file.path(out_loc, "results/copykat_output"), showWarnings = FALSE) +setwd(file.path(out_loc, "results/copykat_output")) metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) metadata <- metadata[which(metadata$scpca_project_id == projectID & @@ -163,9 +157,5 @@ libraryID <- metadata$scpca_library_id # DB file db <- file.path(out_loc,"Azimuth_BM_level1.csv") tissue <- "Immune system" -ct_color <- c("darkorchid","skyblue2","dodgerblue2","gold","beige","sienna1","green4","navy", - "chocolate4","red","darkred","#6A3D9A","maroon","yellow4","grey35","black","lightpink","grey80") -names(ct_color) <- c("B","CD4 T","CD8 T","DC","HSPC","Mono","NK","Other T","Macrophage", - "Early Eryth","Late Eryth","Plasma","Platelet","Stromal","Blast","Cancer","Pre Eryth","Unknown") purrr::walk(libraryID, run_annot) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/multipanel_plot.R b/analyses/cell-type-ETP-ALL-03/scripts/multipanel_plot.R index dba2922ec..dbdcce75f 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/multipanel_plot.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/multipanel_plot.R @@ -1,26 +1,27 @@ #!/usr/bin/env Rscript -#This script combines UMAP plots of all samples into one multi-panel plot +#This script combines multiple UMAP plots from an individual library into one multi-panel plot library(Seurat) library(ggplot2) -multiplot <- function(meta.variable, n_row=8){ - if (meta.variable %in% c("sctype_classification","lowConfidence_annot")){ - clrs <- ct_color - }else{ - clrs <- NULL - } +multiplot <- function(annot.obj, library.id, ct.colors, n.row = 2, + variables.to.plot = c("leiden_clusters","sctype_classification","lowConfidence_annot","copykat.pred")){ plot.list <- list() - for (i in 1:length(libraryID)){ + for (plot.type in variables.to.plot){ + if (plot.type %in% c("sctype_classification","lowConfidence_annot")){ + clrs <- ct.colors + } else{ + clrs <- NULL + } tryCatch({ - plot.list[[i]] <- DimPlot(seu.list[[i]], reduction = "Xumap_", group.by = meta.variable, - label = T, cols = clrs, repel = T) + - ggtitle(libraryID[i]) + NoLegend() + theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) + plot.list[[plot.type]] <- DimPlot(annot.obj, reduction = "Xumap_", group.by = plot.type, + label = T, cols = clrs, repel = T) + NoLegend() }, error=function(e){}) } - cowplot::plot_grid(plotlist = plot.list, nrow = n_row) - ggsave(file.path(out_loc,"plots",paste0("multipanels_",meta.variable,".png")), width = 20, height = 24, bg = "white") + cowplot::plot_grid(plotlist = plot.list, nrow = n.row) + + cowplot::draw_figure_label(library.id, position = "top", size = 18, fontface = "bold") + ggsave(file.path(out_loc,"plots",paste0("multipanels_",library.id,".png")), width = 12, height = 12, bg = "white", dpi = 150) } project_root <- rprojroot::find_root(rprojroot::is_git_root) @@ -39,9 +40,9 @@ names(ct_color) <- c("B","CD4 T","CD8 T","DC","HSPC","Mono","NK","Other T","Macr "Early Eryth","Late Eryth","Plasma","Platelet","Stromal","Blast","Cancer","Pre Eryth","Unknown") seu.list <- list() -for (i in 1:length(libraryID)){ - seu.list[[i]] <- readRDS(file.path(out_loc,"results/rds",paste0(libraryID[i],".rds"))) +for (lib_iter in 1:length(libraryID)){ + seu.list[[lib_iter]] <- readRDS(file.path(out_loc,"results/rds",paste0(libraryID[lib_iter],".rds"))) + names(seu.list)[lib_iter] <- libraryID[lib_iter] } -voi <- c("leiden_clusters","sctype_classification","lowConfidence_annot","copykat.pred") -purrr::walk(voi, multiplot) +purrr::walk2(seu.list, names(seu.list), ~ multiplot (annot.obj = .x, library.id = .y, ct.colors = ct_color)) From e6d45fd769596ab4e5572b234e5c4e6cce97310e Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Tue, 8 Oct 2024 20:56:06 +0000 Subject: [PATCH 10/18] store the module score in seu object --- analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R index 892545c2d..537a6ec52 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R @@ -99,6 +99,7 @@ plot_modulescore <- function(gs_list, seu, sample.name){ ggtitle(paste0(sample.name,": cell type module score")) + scale_color_gradient2(low = scales::muted("blue"), mid = "whitesmoke", high = scales::muted("red"), midpoint = 0) ggsave(file.path(out_loc,"plots",paste0(sample.name,"_features_dotplot.png")), width = 7, height = 7, bg = "white", dpi = 150) + return (seu) } run_annot <- function(ind.lib){ @@ -115,7 +116,7 @@ run_annot <- function(ind.lib){ write.table(res[[2]], file = file.path(out_loc,"results",paste0(ind.lib,"_sctype_top10_celltypes_perCluster.txt")), row.names = F, sep = "\t", quote = F) - plot_modulescore(gs_list, seu, ind.lib) + seu <- plot_modulescore(gs_list, seu, ind.lib) #using copykat for tumor cells identification norm.cells <- colnames(seu)[which(seu$sctype_classification=="B")] From 17ed0005143c6e1bce544c90b3f6475cf73c97ce Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Tue, 8 Oct 2024 21:02:00 +0000 Subject: [PATCH 11/18] update renv.lock --- analyses/cell-type-ETP-ALL-03/renv.lock | 48 ++++++++++++------------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/analyses/cell-type-ETP-ALL-03/renv.lock b/analyses/cell-type-ETP-ALL-03/renv.lock index 8ac7b7729..607a8b2de 100644 --- a/analyses/cell-type-ETP-ALL-03/renv.lock +++ b/analyses/cell-type-ETP-ALL-03/renv.lock @@ -163,6 +163,19 @@ ], "Hash": "a3c822ef3c124828e25e7a9611beeb50" }, + "HGNChelper": { + "Package": "HGNChelper", + "Version": "0.8.14", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "methods", + "splitstackshape", + "utils" + ], + "Hash": "ed5a67b4c7ae01edd46809f1387adbee" + }, "IRanges": { "Package": "IRanges", "Version": "2.38.1", @@ -1680,23 +1693,6 @@ ], "Hash": "c62edf62de70cadf40553e10c739049d" }, - "openxlsx": { - "Package": "openxlsx", - "Version": "4.2.7", - "Source": "Repository", - "Repository": "CRAN", - "Requirements": [ - "R", - "Rcpp", - "grDevices", - "methods", - "stats", - "stringi", - "utils", - "zip" - ], - "Hash": "4eb6bb5d6b95b2e3177f0dd1e7b9ca0e" - }, "parallelDist": { "Package": "parallelDist", "Version": "0.2.6", @@ -2329,6 +2325,17 @@ ], "Hash": "d580c24751e31aae523216b214a008dd" }, + "splitstackshape": { + "Package": "splitstackshape", + "Version": "1.4.8", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "data.table" + ], + "Hash": "69b26ceb9f7976f347049b4d470c2d65" + }, "stringi": { "Package": "stringi", "Version": "1.8.4", @@ -2553,13 +2560,6 @@ "Repository": "CRAN", "Hash": "51dab85c6c98e50a18d7551e9d49f76c" }, - "zip": { - "Package": "zip", - "Version": "2.3.1", - "Source": "Repository", - "Repository": "CRAN", - "Hash": "fcc4bd8e6da2d2011eb64a5e5cc685ab" - }, "zlibbioc": { "Package": "zlibbioc", "Version": "1.50.0", From 7ef2c9d1c89f09b6d39297d3826fbec8cffb279d Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Wed, 16 Oct 2024 16:08:14 +0000 Subject: [PATCH 12/18] edit script for re-running CopyKat on specific B cells (normal) --- analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R index 537a6ec52..5b007aa0d 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R @@ -119,7 +119,14 @@ run_annot <- function(ind.lib){ seu <- plot_modulescore(gs_list, seu, ind.lib) #using copykat for tumor cells identification - norm.cells <- colnames(seu)[which(seu$sctype_classification=="B")] + special.lib <- c("SCPCL000055","SCPCL000066","SCPCL000709","SCPCL000696") + special.norm <- c(15,11,13,18) ## from manual checking of B cells location (should be separated if possible) + if (ind.lib %in% special.lib){ + norm.cells <- colnames(seu)[which(seu$leiden_clusters == special.norm[match(ind.lib,special.lib)])] + }else{ + norm.cells <- colnames(seu)[which(seu$sctype_classification=="B")] + } + n_cores <- parallel::detectCores() - 1 if (length(norm.cells) > 0){ #the sample has B cells annotated copykat.test <- copykat(rawmat=seu@assays[["RNA"]]@counts, id.type="Ensemble", From 1687171281aed9e56467b8aa87618b14630f840d Mon Sep 17 00:00:00 2001 From: UTSouthwesternDSSR Date: Wed, 16 Oct 2024 17:26:00 +0000 Subject: [PATCH 13/18] added plots --- .../plots/SCPCL000055_features_dotplot.png | Bin 0 -> 168867 bytes .../plots/SCPCL000056_features_dotplot.png | Bin 0 -> 168060 bytes .../plots/SCPCL000057_features_dotplot.png | Bin 0 -> 166328 bytes .../plots/SCPCL000058_features_dotplot.png | Bin 0 -> 161091 bytes .../plots/SCPCL000059_features_dotplot.png | Bin 0 -> 185833 bytes .../plots/SCPCL000060_features_dotplot.png | Bin 0 -> 154321 bytes .../plots/SCPCL000061_features_dotplot.png | Bin 0 -> 153192 bytes .../plots/SCPCL000062_features_dotplot.png | Bin 0 -> 164845 bytes .../plots/SCPCL000063_features_dotplot.png | Bin 0 -> 164045 bytes .../plots/SCPCL000064_features_dotplot.png | Bin 0 -> 160140 bytes .../plots/SCPCL000065_features_dotplot.png | Bin 0 -> 156078 bytes 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