## ----vignetteSetup, echo=FALSE, message=FALSE, warning = FALSE---------------- ## Track time spent on making the vignette startTime <- Sys.time() ## Bib setup library("RefManageR") ## Write bibliography information bib <- c( R = citation(), AnnotationDbi = RefManageR::BibEntry( bibtype = "manual", key = "AnnotationDbi", author = "Hervé Pagès and Marc Carlson and Seth Falcon and Nianhua Li", title = "AnnotationDbi: Annotation Database Interface", year = 2017, doi = "10.18129/B9.bioc.AnnotationDbi" ), BiocParallel = citation("BiocParallel"), BiocStyle = citation("BiocStyle"), derfinder = citation("derfinder")[1], DESeq2 = citation("DESeq2"), sessioninfo = citation("sessioninfo"), downloader = citation("downloader"), EnsDb.Hsapiens.v79 = citation("EnsDb.Hsapiens.v79"), GEOquery = citation("GEOquery"), GenomeInfoDb = RefManageR::BibEntry( bibtype = "manual", key = "GenomeInfoDb", author = "Sonali Arora and Martin Morgan and Marc Carlson and H. Pagès", title = "GenomeInfoDb: Utilities for manipulating chromosome and other 'seqname' identifiers", year = 2017, doi = "10.18129/B9.bioc.GenomeInfoDb" ), GenomicFeatures = citation("GenomicFeatures"), GenomicRanges = citation("GenomicRanges"), IRanges = citation("IRanges"), knitr = citation("knitr")[3], org.Hs.eg.db = citation("org.Hs.eg.db"), RCurl = citation("RCurl"), recount = citation("recount")[1], recountworkflow = citation("recount")[2], phenopredict = citation("recount")[3], regionReport = citation("regionReport"), rentrez = citation("rentrez"), RefManageR = citation("RefManageR")[1], rmarkdown = citation("rmarkdown")[1], rtracklayer = citation("rtracklayer"), S4Vectors = RefManageR::BibEntry( bibtype = "manual", key = "S4Vectors", author = "Hervé Pagès and Michael Lawrence and Patrick Aboyoun", title = "S4Vectors: S4 implementation of vector-like and list-like objects", year = 2017, doi = "10.18129/B9.bioc.S4Vectors" ), SummarizedExperiment = RefManageR::BibEntry( bibtype = "manual", key = "SummarizedExperiment", author = "Martin Morgan and Valerie Obenchain and Jim Hester and Hervé Pagès", title = "SummarizedExperiment: SummarizedExperiment container", year = 2017, doi = "10.18129/B9.bioc.SummarizedExperiment" ), testthat = citation("testthat") ) ## ----'installDer', eval = FALSE----------------------------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) { # install.packages("BiocManager") # } # # BiocManager::install("recount") # # ## Check that you have a valid Bioconductor installation # BiocManager::valid() ## ----'citation'--------------------------------------------------------------- ## Citation info citation("recount") ## ----'ultraQuick', eval = FALSE----------------------------------------------- # ## Load library # library("recount") # # ## Find a project of interest # project_info <- abstract_search("GSE32465") # # ## Download the gene-level RangedSummarizedExperiment data # download_study(project_info$project) # # ## Load the data # load(file.path(project_info$project, "rse_gene.Rdata")) # # ## Browse the project at SRA # browse_study(project_info$project) # # ## View GEO ids # colData(rse_gene)$geo_accession # # ## Extract the sample characteristics # geochar <- lapply(split(colData(rse_gene), seq_len(nrow(colData(rse_gene)))), geo_characteristics) # # ## Note that the information for this study is a little inconsistent, so we # ## have to fix it. # geochar <- do.call(rbind, lapply(geochar, function(x) { # if ("cells" %in% colnames(x)) { # colnames(x)[colnames(x) == "cells"] <- "cell.line" # return(x) # } else { # return(x) # } # })) # # ## We can now define some sample information to use # sample_info <- data.frame( # run = colData(rse_gene)$run, # group = ifelse(grepl("uninduced", colData(rse_gene)$title), "uninduced", "induced"), # gene_target = sapply(colData(rse_gene)$title, function(x) { # strsplit(strsplit( # x, # "targeting " # )[[1]][2], " gene")[[1]][1] # }), # cell.line = geochar$cell.line # ) # # ## Scale counts by taking into account the total coverage per sample # rse <- scale_counts(rse_gene) # # ## Add sample information for DE analysis # colData(rse)$group <- sample_info$group # colData(rse)$gene_target <- sample_info$gene_target # # ## Perform differential expression analysis with DESeq2 # library("DESeq2") # # ## Specify design and switch to DESeq2 format # dds <- DESeqDataSet(rse, ~ gene_target + group) # # ## Perform DE analysis # dds <- DESeq(dds, test = "LRT", reduced = ~gene_target, fitType = "local") # res <- results(dds) # # ## Explore results # plotMA(res, main = "DESeq2 results for SRP009615") # # ## Make a report with the results # library("regionReport") # DESeq2Report(dds, # res = res, project = "SRP009615", # intgroup = c("group", "gene_target"), outdir = ".", # output = "SRP009615-results" # ) ## ----'er_analysis', eval = FALSE---------------------------------------------- # ## Define expressed regions for study SRP009615, only for chromosome Y # regions <- expressed_regions("SRP009615", "chrY", # cutoff = 5L, # maxClusterGap = 3000L # ) # # ## Compute coverage matrix for study SRP009615, only for chromosome Y # system.time(rse_ER <- coverage_matrix("SRP009615", "chrY", regions)) # # ## Round the coverage matrix to integers # covMat <- round(assays(rse_ER)$counts, 0) # # ## Get phenotype data for study SRP009615 # pheno <- colData(rse_ER) # # ## Complete the phenotype table with the data we got from GEO # m <- match(pheno$run, sample_info$run) # pheno <- cbind(pheno, sample_info[m, 2:3]) # # ## Build a DESeqDataSet # dds_ers <- DESeqDataSetFromMatrix( # countData = covMat, colData = pheno, # design = ~ gene_target + group # ) # # ## Perform differential expression analysis with DESeq2 at the ER-level # dds_ers <- DESeq(dds_ers, # test = "LRT", reduced = ~gene_target, # fitType = "local" # ) # res_ers <- results(dds_ers) # # ## Explore results # plotMA(res_ers, main = "DESeq2 results for SRP009615 (ER-level, chrY)") # # ## Create a more extensive exploratory report # DESeq2Report(dds_ers, # res = res_ers, # project = "SRP009615 (ER-level, chrY)", # intgroup = c("group", "gene_target"), outdir = ".", # output = "SRP009615-results-ER-level-chrY" # ) ## ----'install', eval = FALSE-------------------------------------------------- # install.packages("BiocManager") # BiocManager::install("recount") ## ----'start', message=FALSE--------------------------------------------------- ## Load recount R package library("recount") ## ----'search_abstract'-------------------------------------------------------- ## Find a project of interest project_info <- abstract_search("GSE32465") ## Explore info project_info ## ----'download'--------------------------------------------------------------- ## Download the gene-level RangedSummarizedExperiment data download_study(project_info$project) ## Load the data load(file.path(project_info$project, "rse_gene.Rdata")) ## Delete it if you don't need it anymore unlink(project_info$project, recursive = TRUE) ## ----'explore_rse'------------------------------------------------------------ rse_gene ## This is the sample phenotype data provided by the recount project colData(rse_gene) ## At the gene level, the row data includes the gene Gencode ids, the gene ## symbols and the sum of the disjoint exons widths, which can be used for ## taking into account the gene length. rowData(rse_gene) ## At the exon level, you can get the gene Gencode ids from the names of: # rowRanges(rse_exon) ## ----'browse'----------------------------------------------------------------- ## Browse the project at SRA browse_study(project_info$project) ## ----'sample_info', warning = FALSE------------------------------------------- ## View GEO ids colData(rse_gene)$geo_accession ## Extract the sample characteristics geochar <- lapply(split(colData(rse_gene), seq_len(nrow(colData(rse_gene)))), geo_characteristics) ## Note that the information for this study is a little inconsistent, so we ## have to fix it. geochar <- do.call(rbind, lapply(geochar, function(x) { if ("cells" %in% colnames(x)) { colnames(x)[colnames(x) == "cells"] <- "cell.line" return(x) } else { return(x) } })) ## We can now define some sample information to use sample_info <- data.frame( run = colData(rse_gene)$run, group = ifelse(grepl("uninduced", colData(rse_gene)$title), "uninduced", "induced"), gene_target = sapply(colData(rse_gene)$title, function(x) { strsplit(strsplit( x, "targeting " )[[1]][2], " gene")[[1]][1] }), cell.line = geochar$cell.line ) ## ----'scale_counts'----------------------------------------------------------- ## Scale counts by taking into account the total coverage per sample rse <- scale_counts(rse_gene) ##### Details about counts ##### ## Scale counts to 40 million mapped reads. Not all mapped reads are in exonic ## sequence, so the total is not necessarily 40 million. colSums(assays(rse)$counts) / 1e6 ## Compute read counts rse_read_counts <- read_counts(rse_gene) ## Difference between read counts and number of reads downloaded by Rail-RNA colSums(assays(rse_read_counts)$counts) / 1e6 - colData(rse_read_counts)$reads_downloaded / 1e6 ## Check the help page for read_counts() for more details ## ----'add_sample_info'-------------------------------------------------------- ## Add sample information for DE analysis colData(rse)$group <- sample_info$group colData(rse)$gene_target <- sample_info$gene_target ## ----'de_analysis'------------------------------------------------------------ ## Perform differential expression analysis with DESeq2 library("DESeq2") ## Specify design and switch to DESeq2 format dds <- DESeqDataSet(rse, ~ gene_target + group) ## Perform DE analysis dds <- DESeq(dds, test = "LRT", reduced = ~gene_target, fitType = "local") res <- results(dds) ## ----'maplot', fig.cap = "MA plot for group differences adjusted by gene target for SRP009615 using gene-level data."---- ## Explore results plotMA(res, main = "DESeq2 results for SRP009615") ## ----'make_report', eval = FALSE---------------------------------------------- # ## Make a report with the results # library("regionReport") # report <- DESeq2Report(dds, # res = res, project = "SRP009615", # intgroup = c("group", "gene_target"), outdir = ".", # output = "SRP009615-results", nBest = 10, nBestFeatures = 2 # ) ## ----'make_report_real', echo = FALSE, results = 'hide'--------------------------------------------------------------- library("regionReport") ## Load some packages used by regionReport library("DT") library("edgeR") library("ggplot2") library("RColorBrewer") ## Make it so that the report will be available as a vignette original <- readLines(system.file("DESeq2Exploration", "DESeq2Exploration.Rmd", package = "regionReport" )) vignetteInfo <- c( "vignette: >", " %\\VignetteEngine{knitr::rmarkdown}", " %\\VignetteIndexEntry{Basic DESeq2 results exploration}", " %\\VignetteEncoding{UTF-8}" ) new <- c(original[1:16], vignetteInfo, original[17:length(original)]) writeLines(new, "SRP009615-results-template.Rmd") ## Now create the report report <- DESeq2Report(dds, res = res, project = "SRP009615", intgroup = c("group", "gene_target"), outdir = ".", output = "SRP009615-results", device = "png", template = "SRP009615-results-template.Rmd", nBest = 10, nBestFeatures = 2 ) ## Clean up file.remove("SRP009615-results-template.Rmd") ## ----'geneSymbols'---------------------------------------------------------------------------------------------------- ## Load required library library("org.Hs.eg.db") ## Extract Gencode gene ids gencode <- gsub("\\..*", "", names(recount_genes)) ## Find the gene information we are interested in gene_info <- select(org.Hs.eg.db, gencode, c( "ENTREZID", "GENENAME", "SYMBOL", "ENSEMBL" ), "ENSEMBL") ## Explore part of the results dim(gene_info) head(gene_info) ## ----'define_ers', eval = .Platform$OS.type != 'windows'-------------------------------------------------------------- ## Define expressed regions for study SRP009615, only for chromosome Y regions <- expressed_regions("SRP009615", "chrY", cutoff = 5L, maxClusterGap = 3000L ) ## Briefly explore the resulting regions regions ## ----'compute_covMat', eval = .Platform$OS.type != 'windows'---------------------------------------------------------- ## Compute coverage matrix for study SRP009615, only for chromosome Y system.time(rse_ER <- coverage_matrix("SRP009615", "chrY", regions)) ## Explore the RSE a bit dim(rse_ER) rse_ER ## ----'to_integer', eval = .Platform$OS.type != 'windows'-------------------------------------------------------------- ## Round the coverage matrix to integers covMat <- round(assays(rse_ER)$counts, 0) ## ----'phenoData', eval = .Platform$OS.type != 'windows'--------------------------------------------------------------- ## Get phenotype data for study SRP009615 pheno <- colData(rse_ER) ## Complete the phenotype table with the data we got from GEO m <- match(pheno$run, sample_info$run) pheno <- cbind(pheno, sample_info[m, 2:3]) ## Explore the phenotype data a little bit head(pheno) ## ----'ers_dds', eval = .Platform$OS.type != 'windows'----------------------------------------------------------------- ## Build a DESeqDataSet dds_ers <- DESeqDataSetFromMatrix( countData = covMat, colData = pheno, design = ~ gene_target + group ) ## ----'de_analysis_ers', eval = .Platform$OS.type != 'windows'--------------------------------------------------------- ## Perform differential expression analysis with DESeq2 at the ER-level dds_ers <- DESeq(dds_ers, test = "LRT", reduced = ~gene_target, fitType = "local" ) res_ers <- results(dds_ers) ## ----'maploters', eval = .Platform$OS.type != 'windows', fig.cap = "MA plot for group differences adjusted by gene target for SRP009615 using ER-level data (just chrY)."---- ## Explore results plotMA(res_ers, main = "DESeq2 results for SRP009615 (ER-level, chrY)") ## ----'report2', eval = FALSE------------------------------------------------------------------------------------------ # ## Create the report # report2 <- DESeq2Report(dds_ers, # res = res_ers, # project = "SRP009615 (ER-level, chrY)", # intgroup = c("group", "gene_target"), outdir = ".", # output = "SRP009615-results-ER-level-chrY" # ) ## ---- 'gene_annotation'----------------------------------------------------------------------------------------------- ## Gene annotation information rowRanges(rse_gene_SRP009615) ## Also accessible via rowData(rse_gene_SRP009615) ## ---- 'exon_annotation'----------------------------------------------------------------------------------------------- ## Get the rse_exon object for study SRP009615 download_study("SRP009615", type = "rse-exon") load(file.path("SRP009615", "rse_exon.Rdata")) ## Delete it if you don't need it anymore unlink("SRP009615", recursive = TRUE) ## Annotation information rowRanges(rse_exon) ## Add a gene_id column rowRanges(rse_exon)$gene_id <- rownames(rse_exon) ## Example subset rse_exon_subset <- subset(rse_exon, subset = gene_id == "ENSG00000000003.14") rowRanges(rse_exon_subset) ## ---- eval = .Platform$OS.type != 'windows'--------------------------------------------------------------------------- ## Get the disjoint exons based on EnsDb.Hsapiens.v79 which matches hg38 exons <- reproduce_ranges("exon", db = "EnsDb.Hsapiens.v79") ## Change the chromosome names to match those used in the BigWig files library("GenomeInfoDb") seqlevelsStyle(exons) <- "UCSC" ## Get the count matrix at the exon level for chrY exons_chrY <- keepSeqlevels(exons, "chrY", pruning.mode = "coarse") exonRSE <- coverage_matrix("SRP009615", "chrY", unlist(exons_chrY), chunksize = 3000 ) exonMatrix <- assays(exonRSE)$counts dim(exonMatrix) head(exonMatrix) ## Summary the information at the gene level for chrY exon_gene <- rep(names(exons_chrY), elementNROWS(exons_chrY)) geneMatrix <- do.call(rbind, lapply(split( as.data.frame(exonMatrix), exon_gene ), colSums)) dim(geneMatrix) head(geneMatrix) ## ----'gene_fusions'--------------------------------------------------------------------------------------------------- library("recount") ## Download and load RSE object for SRP009615 study download_study("SRP009615", type = "rse-jx") load(file.path("SRP009615", "rse_jx.Rdata")) ## Delete it if you don't need it anymore unlink("SRP009615", recursive = TRUE) ## Exon-exon junctions by class table(rowRanges(rse_jx)$class) ## Potential gene fusions by chromosome fusions <- rowRanges(rse_jx)[rowRanges(rse_jx)$class == "fusion"] fusions_by_chr <- sort(table(seqnames(fusions)), decreasing = TRUE) fusions_by_chr[fusions_by_chr > 0] ## Genes with the most fusions head(sort(table(unlist(fusions$symbol_proposed)), decreasing = TRUE)) ## ----snaptron--------------------------------------------------------------------------------------------------------- library("GenomicRanges") junctions <- GRanges(seqnames = "chr2", IRanges( start = c(28971711, 29555082, 29754983), end = c(29462418, 29923339, 29917715) )) snaptron_query(junctions) ## ----'rse-fc example'------------------------------------------------------------------------------------------------- ## Example use of download_study() ## for downloading the FANTOM-CAT `rse_fc` ## file for a given study download_study("DRP000366", type = "rse-fc", download = FALSE) ## ----'recount-brain'-------------------------------------------------------------------------------------------------- ## recount-brain citation details citation("recount")[5] ## ----'downloadAll'---------------------------------------------------------------------------------------------------- ## Get data.frame with all the URLs library("recount") dim(recount_url) ## Explore URLs head(recount_url$url) ## ----'actuallyDownload', eval = FALSE--------------------------------------------------------------------------------- # ## Download all files (will take a while! It's over 8 TB of data) # sapply(unique(recount_url$project), download_study, type = "all") ## ----Figure1, out.width="100%", fig.align="center", fig.cap = "SIgn up to SciServer", echo = FALSE-------------------- knitr::include_graphics("https://raw.githubusercontent.com/leekgroup/recount-website/master/sciserver_demo/register.png") ## ----Figure2, out.width="100%", fig.align="center", fig.cap = "Click on SciServer Compute,", echo = FALSE------------- knitr::include_graphics("https://raw.githubusercontent.com/leekgroup/recount-website/master/sciserver_demo/sciserver-compute.png") ## ----Figure3, out.width="100%", fig.align="center", fig.cap = "Select the appropriate image and load the recount public volume.", echo = FALSE---- knitr::include_graphics("https://raw.githubusercontent.com/leekgroup/recount-website/master/sciserver_demo/new-container.png") ## ----Figure4, out.width="100%", fig.align="center", fig.cap = "Create a R notebook.", echo = FALSE-------------------- knitr::include_graphics("https://raw.githubusercontent.com/leekgroup/recount-website/master/sciserver_demo/new-R.png") ## ----Figure5, out.width="100%", fig.align="center", fig.cap = "Install recount and DESeq2 on SciServer.", echo = FALSE---- knitr::include_graphics("https://raw.githubusercontent.com/leekgroup/recount-website/master/sciserver_demo/demo1.png") ## ----Figure6, out.width="100%", fig.align="center", fig.cap = "recount files are available locally so remmeber to use the local data when working on SciServer.", echo = FALSE---- knitr::include_graphics("https://raw.githubusercontent.com/leekgroup/recount-website/master/sciserver_demo/demo2.png") ## ----'ER-sciserver', eval = FALSE------------------------------------------------------------------------------------- # ## Expressed-regions SciServer example # regions <- expressed_regions("SRP009615", "chrY", # cutoff = 5L, # maxClusterGap = 3000L, outdir = file.path(scipath, "SRP009615") # ) # regions ## ----createVignette, eval=FALSE--------------------------------------------------------------------------------------- # ## Create the vignette # library("rmarkdown") # system.time(render("recount-quickstart.Rmd", "BiocStyle::html_document")) # # ## Extract the R code # library("knitr") # knit("recount-quickstart.Rmd", tangle = TRUE) ## ----reproduce1, echo=FALSE------------------------------------------------------------------------------------------- ## Date the vignette was generated Sys.time() ## ----reproduce2, echo=FALSE------------------------------------------------------------------------------------------- ## Processing time in seconds totalTime <- diff(c(startTime, Sys.time())) round(totalTime, digits = 3) ## ----reproduce3, echo=FALSE------------------------------------------------------------------------------------------- ## Session info library("sessioninfo") options(width = 120) session_info() ## ----'datasetup', echo = FALSE, eval = FALSE-------------------------------------------------------------------------- # ## Code for re-creating the data distributed in this package # # ## Genes/exons # library("recount") # recount_both <- reproduce_ranges("both", db = "Gencode.v25") # recount_exons <- recount_both$exon # save(recount_exons, file = "../data/recount_exons.RData", compress = "xz") # recount_genes <- recount_both$gene # save(recount_genes, file = "../data/recount_genes.RData", compress = "xz") # # ## URL table # load("../../recount-website/fileinfo/upload_table.Rdata") # recount_url <- upload_table # ## Create URLs # is.bw <- grepl("[.]bw$", recount_url$file_name) # recount_url$url <- NA # recount_url$url[!is.bw] <- paste0( # "http://duffel.rail.bio/recount/", # recount_url$project[!is.bw], "/", recount_url$file_name[!is.bw] # ) # recount_url$url[!is.bw & recount_url$version2] <- # paste0( # "http://duffel.rail.bio/recount/v2/", # recount_url$project[!is.bw & recount_url$version2], "/", # recount_url$file_name[!is.bw & recount_url$version2] # ) # recount_url$url[is.bw] <- paste0( # "http://duffel.rail.bio/recount/", # recount_url$project[is.bw], "/bw/", recount_url$file_name[is.bw] # ) # save(recount_url, file = "../data/recount_url.RData", compress = "xz") # # ## Add FC-RC2 data # load("../data/recount_url.RData") # load("../../recount-website/fileinfo/fc_rc_url_table.Rdata") # recount_url <- rbind(recount_url, fc_rc_url_table) # rownames(recount_url) <- NULL # save(recount_url, file = "../data/recount_url.RData", compress = "xz") # # # ## Abstract info # load("../../recount-website/website/meta_web.Rdata") # recount_abstract <- meta_web[, c("number_samples", "species", "abstract")] # recount_abstract$project <- gsub('.*">|', "", meta_web$accession) # Encoding(recount_abstract$abstract) <- "latin1" # recount_abstract$abstract <- iconv(recount_abstract$abstract, "latin1", "UTF-8") # save(recount_abstract, # file = "../data/recount_abstract.RData", # compress = "bzip2" # ) # # ## Example rse_gene file # system("scp e:/dcl01/leek/data/recount-website/rse/rse_sra/SRP009615/rse_gene.Rdata .") # load("rse_gene.Rdata") # rse_gene_SRP009615 <- rse_gene # save(rse_gene_SRP009615, # file = "../data/rse_gene_SRP009615.RData", # compress = "xz" # ) # unlink("rse_gene.Rdata") ## ----'cleanup', echo = FALSE, results = 'hide'------------------------------------------------------------------------ ## Clean up unlink("SRP009615", recursive = TRUE) ## ----vignetteBiblio, results = 'asis', echo = FALSE, warning = FALSE, message = FALSE--------------------------------- ## Print bibliography PrintBibliography(bib, .opts = list(hyperlink = "to.doc", style = "html"))