## ----style, echo = FALSE, results = 'asis'---------------- BiocStyle::markdown() options(width=60, max.print=1000) knitr::opts_chunk$set( eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")), cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE")), tidy.opts=list(width.cutoff=60), tidy=TRUE) ## ----setup_libraries, echo=FALSE, message=FALSE, warning=FALSE---- suppressPackageStartupMessages({ library(systemPipeR) library(BiocParallel) library(Biostrings) library(Rsamtools) library(GenomicRanges) library(ggplot2) library(GenomicAlignments) library(ShortRead) library(ape) library(batchtools) }) ## ----load_systempiper, eval=TRUE, message=FALSE----------- library(systemPipeR) ## ----load_targets, eval=TRUE------------------------------ targetspath <- system.file("extdata", "targetsPE.txt", package="systemPipeR") targets <- read.delim(targetspath, comment.char = "#")[,1:4] targets ## ----construct_SYSargs2_trim-se, eval=FALSE--------------- # dir_path <- system.file("extdata/cwl/preprocessReads/trim-pe", package="systemPipeR") # trim <- loadWorkflow(targets=targetspath, wf_file="trim-pe.cwl", input_file="trim-pe.yml", dir_path=dir_path) # trim <- renderWF(trim, inputvars=c(FileName1="_FASTQ_PATH1_", FileName2="_FASTQ_PATH2_", SampleName="_SampleName_")) # trim # output(trim)[1:2] ## ----preprocessing, eval=FALSE---------------------------- # preprocessReads(args=trim, Fct="trimLRPatterns(Rpattern='GCCCGGGTAA', # subject=fq)", batchsize=100000, overwrite=TRUE, compress=TRUE) # writeTargetsout(x=trim, file="targets_trim.txt", step = 1, # new_col = c("FileName1", "FileName2"), new_col_output_index = c(1, 2), overwrite = TRUE) ## ----fastq_report, eval=FALSE----------------------------- # fqlist <- seeFastq(fastq=infile1(trim), batchsize=10000, klength=8) # pdf("./results/fastqReport.pdf", height=18, width=4*length(fqlist)) # seeFastqPlot(fqlist) # dev.off() ## ----hisat_index, eval=FALSE------------------------------ # dir_path <- system.file("extdata/cwl/hisat2/hisat2-idx", package="systemPipeR") # idx <- loadWorkflow(targets=NULL, wf_file="hisat2-index.cwl", input_file="hisat2-index.yml", dir_path=dir_path) # idx <- renderWF(idx) # idx # cmdlist(idx) # # ## Run # runCommandline(idx, make_bam = FALSE) ## ----hisat_SYSargs2_object, eval=TRUE--------------------- dir_path <- system.file("extdata/cwl/hisat2/hisat2-pe", package="systemPipeR") args <- loadWorkflow(targets=targetspath, wf_file="hisat2-mapping-pe.cwl", input_file="hisat2-mapping-pe.yml", dir_path=dir_path) args <- renderWF(args, inputvars=c(FileName1="_FASTQ_PATH1_", FileName2="_FASTQ_PATH2_", SampleName="_SampleName_")) args cmdlist(args)[1:2] output(args)[1:2] ## ----hisat_SYSargs2_singleM, eval=FALSE------------------- # ## Run single Machine # args <- runCommandline(args) ## ----hisat2_clusterRun, eval=FALSE------------------------ # library(batchtools) # resources <- list(walltime=120, ntasks=1, ncpus=4, memory=1024) # reg <- clusterRun(args, FUN = runCommandline, more.args = list(args=args, make_bam=TRUE, dir=FALSE), # conffile = ".batchtools.conf.R", template = "batchtools.slurm.tmpl", # Njobs=18, runid="01", resourceList=resources) # getStatus(reg=reg) # waitForJobs(reg=reg) # args <- output_update(args, dir=FALSE, replace=TRUE, extension=c(".sam", ".bam")) ## Updates the output(args) to the right location in the subfolders # output(args) ## ----check_files_exist, eval=FALSE------------------------ # outpaths <- subsetWF(args , slot="output", subset=1, index=1) # file.exists(outpaths) ## ----align_stats, eval=FALSE------------------------------ # read_statsDF <- alignStats(args=args) # write.table(read_statsDF, "results/alignStats.xls", row.names=FALSE, quote=FALSE, sep="\t") ## ----align_stats_view, eval=TRUE-------------------------- read.table(system.file("extdata", "alignStats.xls", package="systemPipeR"), header=TRUE)[1:4,] ## ----bam_urls, eval=FALSE--------------------------------- # symLink2bam(sysargs=args, htmldir=c("~/.html/", "somedir/"), # urlbase="http://cluster.hpcc.ucr.edu/~tgirke/", # urlfile="./results/IGVurl.txt") ## ----read_counting1, eval=FALSE--------------------------- # library("GenomicFeatures"); library(BiocParallel) # txdb <- makeTxDbFromGFF(file="data/tair10.gff", format="gff", dataSource="TAIR", organism="Arabidopsis thaliana") # saveDb(txdb, file="./data/tair10.sqlite") # txdb <- loadDb("./data/tair10.sqlite") # outpaths <- subsetWF(args, slot="output", subset=1, index=1) # (align <- readGAlignments(outpaths[1])) # Demonstrates how to read bam file into R # eByg <- exonsBy(txdb, by=c("gene")) # bfl <- BamFileList(outpaths, yieldSize=50000, index=character()) # multicoreParam <- MulticoreParam(workers=2); register(multicoreParam); registered() # counteByg <- bplapply(bfl, function(x) summarizeOverlaps(eByg, x, mode="Union", # ignore.strand=TRUE, # inter.feature=FALSE, # singleEnd=TRUE)) # countDFeByg <- sapply(seq(along=counteByg), function(x) assays(counteByg[[x]])$counts) # rownames(countDFeByg) <- names(rowRanges(counteByg[[1]])); colnames(countDFeByg) <- names(bfl) # rpkmDFeByg <- apply(countDFeByg, 2, function(x) returnRPKM(counts=x, ranges=eByg)) # write.table(countDFeByg, "results/countDFeByg.xls", col.names=NA, quote=FALSE, sep="\t") # write.table(rpkmDFeByg, "results/rpkmDFeByg.xls", col.names=NA, quote=FALSE, sep="\t") ## ----view_counts, eval=FALSE------------------------------ # read.delim("results/countDFeByg.xls", row.names=1, check.names=FALSE)[1:4,1:5] ## ----view_rpkm, eval=FALSE-------------------------------- # read.delim("results/rpkmDFeByg.xls", row.names=1, check.names=FALSE)[1:4,1:4] ## ----sample_tree, eval=FALSE------------------------------ # library(DESeq2, quietly=TRUE); library(ape, warn.conflicts=FALSE) # countDF <- as.matrix(read.table("./results/countDFeByg.xls")) # colData <- data.frame(row.names=targets.as.df(targets(args))$SampleName, condition=targets.as.df(targets(args))$Factor) # dds <- DESeqDataSetFromMatrix(countData = countDF, colData = colData, design = ~ condition) # d <- cor(assay(rlog(dds)), method="spearman") # hc <- hclust(dist(1-d)) # pdf("results/sample_tree.pdf") # plot.phylo(as.phylo(hc), type="p", edge.col="blue", edge.width=2, show.node.label=TRUE, no.margin=TRUE) # dev.off() ## ----run_edger, eval=FALSE-------------------------------- # library(edgeR) # countDF <- read.delim("results/countDFeByg.xls", row.names=1, check.names=FALSE) # targets <- read.delim("targetsPE.txt", comment="#") # cmp <- readComp(file="targetsPE.txt", format="matrix", delim="-") # edgeDF <- run_edgeR(countDF=countDF, targets=targets, cmp=cmp[[1]], independent=FALSE, mdsplot="") ## ----custom_annot, eval=FALSE----------------------------- # library("biomaRt") # m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="plants.ensembl.org") # desc <- getBM(attributes=c("tair_locus", "description"), mart=m) # desc <- desc[!duplicated(desc[,1]),] # descv <- as.character(desc[,2]); names(descv) <- as.character(desc[,1]) # edgeDF <- data.frame(edgeDF, Desc=descv[rownames(edgeDF)], check.names=FALSE) # write.table(edgeDF, "./results/edgeRglm_allcomp.xls", quote=FALSE, sep="\t", col.names = NA) ## ----filter_degs, eval=FALSE------------------------------ # edgeDF <- read.delim("results/edgeRglm_allcomp.xls", row.names=1, check.names=FALSE) # pdf("results/DEGcounts.pdf") # DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=20)) # dev.off() # write.table(DEG_list$Summary, "./results/DEGcounts.xls", quote=FALSE, sep="\t", row.names=FALSE) ## ----venn_diagram, eval=FALSE----------------------------- # vennsetup <- overLapper(DEG_list$Up[6:9], type="vennsets") # vennsetdown <- overLapper(DEG_list$Down[6:9], type="vennsets") # pdf("results/vennplot.pdf") # vennPlot(list(vennsetup, vennsetdown), mymain="", mysub="", colmode=2, ccol=c("blue", "red")) # dev.off() ## ----get_go_annot, eval=FALSE----------------------------- # library("biomaRt") # listMarts() # To choose BioMart database # listMarts(host="plants.ensembl.org") # m <- useMart("plants_mart", host="plants.ensembl.org") # listDatasets(m) # m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="plants.ensembl.org") # listAttributes(m) # Choose data types you want to download # go <- getBM(attributes=c("go_id", "tair_locus", "namespace_1003"), mart=m) # go <- go[go[,3]!="",]; go[,3] <- as.character(go[,3]) # go[go[,3]=="molecular_function", 3] <- "F"; go[go[,3]=="biological_process", 3] <- "P"; go[go[,3]=="cellular_component", 3] <- "C" # go[1:4,] # dir.create("./data/GO") # write.table(go, "data/GO/GOannotationsBiomart_mod.txt", quote=FALSE, row.names=FALSE, col.names=FALSE, sep="\t") # catdb <- makeCATdb(myfile="data/GO/GOannotationsBiomart_mod.txt", lib=NULL, org="", colno=c(1,2,3), idconv=NULL) # save(catdb, file="data/GO/catdb.RData") ## ----go_enrich, eval=FALSE-------------------------------- # library("biomaRt") # load("data/GO/catdb.RData") # DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=50), plot=FALSE) # up_down <- DEG_list$UporDown; names(up_down) <- paste(names(up_down), "_up_down", sep="") # up <- DEG_list$Up; names(up) <- paste(names(up), "_up", sep="") # down <- DEG_list$Down; names(down) <- paste(names(down), "_down", sep="") # DEGlist <- c(up_down, up, down) # DEGlist <- DEGlist[sapply(DEGlist, length) > 0] # BatchResult <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="all", id_type="gene", CLSZ=2, cutoff=0.9, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL) # library("biomaRt") # m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="plants.ensembl.org") # goslimvec <- as.character(getBM(attributes=c("goslim_goa_accession"), mart=m)[,1]) # BatchResultslim <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="slim", id_type="gene", myslimv=goslimvec, CLSZ=10, cutoff=0.01, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL) ## ----go_plot, eval=FALSE---------------------------------- # gos <- BatchResultslim[grep("M6-V6_up_down", BatchResultslim$CLID), ] # gos <- BatchResultslim # pdf("GOslimbarplotMF.pdf", height=8, width=10) # goBarplot(gos, gocat="MF") # dev.off() # goBarplot(gos, gocat="BP") # goBarplot(gos, gocat="CC") ## ----heatmap, eval=FALSE---------------------------------- # library(pheatmap) # geneids <- unique(as.character(unlist(DEG_list[[1]]))) # y <- assay(rlog(dds))[geneids, ] # pdf("heatmap1.pdf") # pheatmap(y, scale="row", clustering_distance_rows="correlation", clustering_distance_cols="correlation") # dev.off() ## ----sessionInfo------------------------------------------ sessionInfo()