## ----style-knitr, eval=TRUE, echo=FALSE, results="asis"--------------------------------- BiocStyle::latex(use.unsrturl=FALSE) ## ----setup, include=FALSE, cache=FALSE------------------------------------------------------- library(knitr) # set global chunk options for knitr opts_chunk$set(comment=NA, warning=FALSE, message=FALSE, fig.path='figure/systemPipeR-') options(formatR.arrow=TRUE, width=95) unlink("test.db") ## ----eval=TRUE------------------------------------------------------------------------------- library(systemPipeR) ## ----eval=FALSE------------------------------------------------------------------------------ # library(systemPipeRdata) # genWorkenvir(workflow="ribseq") # setwd("riboseq") ## ----eval=FALSE------------------------------------------------------------------------------ # source("systemPipeRIBOseq_Fct.R") ## ----eval=TRUE------------------------------------------------------------------------------- targetspath <- system.file("extdata", "targets.txt", package="systemPipeR") targets <- read.delim(targetspath, comment.char = "#")[,1:4] targets ## ----eval=FALSE, messages=FALSE, warning=FALSE, cache=TRUE----------------------------------- # args <- systemArgs(sysma="param/trim.param", mytargets="targets.txt") # fctpath <- system.file("extdata", "custom_Fct.R", package="systemPipeR") # source(fctpath) # iterTrim <- ".iterTrimbatch1(fq, pattern='ACACGTCT', internalmatch=FALSE, minpatternlength=6, # Nnumber=1, polyhomo=50, minreadlength=16, maxreadlength=100)" # preprocessReads(args=args, Fct=iterTrim, batchsize=100000, overwrite=TRUE, compress=TRUE) # writeTargetsout(x=args, file="targets_trim.txt", overwrite=TRUE) ## ----eval=FALSE------------------------------------------------------------------------------ # args <- systemArgs(sysma="param/tophat.param", mytargets="targets_trim.txt") # fqlist <- seeFastq(fastq=infile1(args), batchsize=100000, klength=8) # pdf("./results/fastqReport.pdf", height=18, width=4*length(fqlist)) # seeFastqPlot(fqlist) # dev.off() ## ----eval=FALSE------------------------------------------------------------------------------ # args <- systemArgs(sysma="param/tophat.param", mytargets="targets.txt") # sysargs(args)[1] # Command-line parameters for first FASTQ file ## ----eval=FALSE------------------------------------------------------------------------------ # moduleload(modules(args)) # system("bowtie2-build ./data/tair10.fasta ./data/tair10.fasta") # resources <- list(walltime="20:00:00", nodes=paste0("1:ppn=", cores(args)), memory="10gb") # reg <- clusterRun(args, conffile=".BatchJobs.R", template="torque.tmpl", Njobs=18, runid="01", # resourceList=resources) # waitForJobs(reg) ## ----eval=FALSE------------------------------------------------------------------------------ # file.exists(outpaths(args)) ## ----eval=FALSE------------------------------------------------------------------------------ # read_statsDF <- alignStats(args=args) # write.table(read_statsDF, "results/alignStats.xls", row.names=FALSE, quote=FALSE, sep="\t") ## ----eval=TRUE------------------------------------------------------------------------------- read.table(system.file("extdata", "alignStats.xls", package="systemPipeR"), header=TRUE)[1:4,] ## ----eval=FALSE------------------------------------------------------------------------------ # symLink2bam(sysargs=args, htmldir=c("~/.html/", "somedir/"), # urlbase="http://biocluster.ucr.edu/~tgirke/", # urlfile="./results/IGVurl.txt") ## ----eval=FALSE------------------------------------------------------------------------------ # library(GenomicFeatures) # file <- system.file("extdata/annotation", "tair10.gff", package="systemPipeRdata") # txdb <- makeTxDbFromGFF(file=file, format="gff3", organism="Arabidopsis") # feat <- genFeatures(txdb, featuretype="all", reduce_ranges=TRUE, upstream=1000, downstream=0, # verbose=TRUE) ## ----eval=FALSE------------------------------------------------------------------------------ # library(ggplot2); library(grid) # fc <- featuretypeCounts(bfl=BamFileList(outpaths(args), yieldSize=50000), grl=feat, # singleEnd=TRUE, readlength=NULL, type="data.frame") # p <- plotfeaturetypeCounts(x=fc, graphicsfile="results/featureCounts.pdf", graphicsformat="pdf", # scales="fixed", anyreadlength=TRUE, scale_length_val=NULL) ## ----eval=FALSE------------------------------------------------------------------------------ # fc2 <- featuretypeCounts(bfl=BamFileList(outpaths(args), yieldSize=50000), grl=feat, # singleEnd=TRUE, readlength=c(74:76,99:102), type="data.frame") # p2 <- plotfeaturetypeCounts(x=fc2, graphicsfile="results/featureCounts2.pdf", graphicsformat="pdf", # scales="fixed", anyreadlength=FALSE, scale_length_val=NULL) ## ----eval=FALSE------------------------------------------------------------------------------ # library(systemPipeRdata); library(GenomicFeatures); library(rtracklayer) # gff <- system.file("extdata/annotation", "tair10.gff", package="systemPipeRdata") # txdb <- makeTxDbFromGFF(file=gff, format="gff3", organism="Arabidopsis") # futr <- fiveUTRsByTranscript(txdb, use.names=TRUE) # genome <- system.file("extdata/annotation", "tair10.fasta", package="systemPipeRdata") # dna <- extractTranscriptSeqs(FaFile(genome), futr) # uorf <- predORF(dna, n="all", mode="orf", longest_disjoint=TRUE, strand="sense") ## ----eval=FALSE------------------------------------------------------------------------------ # grl_scaled <- scaleRanges(subject=futr, query=uorf, type="uORF", verbose=TRUE) # export.gff3(unlist(grl_scaled), "uorf.gff") ## ----eval=FALSE------------------------------------------------------------------------------ # translate(unlist(getSeq(FaFile(genome), grl_scaled[[7]]))) ## ----eval=FALSE------------------------------------------------------------------------------ # feat <- genFeatures(txdb, featuretype="all", reduce_ranges=FALSE) # feat <- c(feat, GRangesList("uORF"=unlist(grl_scaled))) ## ----eval=FALSE------------------------------------------------------------------------------ # feat <- genFeatures(txdb, featuretype="intergenic", reduce_ranges=TRUE) # intergenic <- feat$intergenic # strand(intergenic) <- "+" # dna <- getSeq(FaFile(genome), intergenic) # names(dna) <- mcols(intergenic)$feature_by # sorf <- predORF(dna, n="all", mode="orf", longest_disjoint=TRUE, strand="both") # sorf <- sorf[width(sorf) > 60] # Remove sORFs below length cutoff, here 60bp # intergenic <- split(intergenic, mcols(intergenic)$feature_by) # grl_scaled_intergenic <- scaleRanges(subject=intergenic, query=sorf, type="sORF", verbose=TRUE) # export.gff3(unlist(grl_scaled_intergenic), "sorf.gff") # translate(getSeq(FaFile(genome), unlist(grl_scaled_intergenic))) ## ----eval=FALSE------------------------------------------------------------------------------ # grl <- cdsBy(txdb, "tx", use.names=TRUE) # fcov <- featureCoverage(bfl=BamFileList(outpaths(args)[1:2]), grl=grl[1:4], resizereads=NULL, # readlengthrange=NULL, Nbins=20, method=mean, fixedmatrix=FALSE, # resizefeatures=TRUE, upstream=20, downstream=20, # outfile="results/featureCoverage.xls", overwrite=TRUE) ## ----eval=FALSE------------------------------------------------------------------------------ # fcov <- featureCoverage(bfl=BamFileList(outpaths(args)[1:4]), grl=grl[1:12], resizereads=NULL, # readlengthrange=NULL, Nbins=NULL, method=mean, fixedmatrix=TRUE, # resizefeatures=TRUE, upstream=20, downstream=20, # outfile="results/featureCoverage.xls", overwrite=TRUE) # plotfeatureCoverage(covMA=fcov, method=mean, scales="fixed", extendylim=2, scale_count_val=10^6) ## ----eval=FALSE------------------------------------------------------------------------------ # library(ggplot2); library(grid) # fcov <- featureCoverage(bfl=BamFileList(outpaths(args)[1:2]), grl=grl[1:4], resizereads=NULL, # readlengthrange=NULL, Nbins=20, method=mean, fixedmatrix=TRUE, # resizefeatures=TRUE, upstream=20, downstream=20, # outfile="results/featureCoverage.xls", overwrite=TRUE) # pdf("./results/featurePlot.pdf", height=12, width=24) # plotfeatureCoverage(covMA=fcov, method=mean, scales="fixed", extendylim=2, scale_count_val=10^6) # dev.off() ## ----eval=FALSE------------------------------------------------------------------------------ # fcov <- featureCoverage(bfl=BamFileList(outpaths(args)[1:2]), grl=grl[1:4], resizereads=NULL, # readlengthrange=NULL, Nbins=NULL, method=mean, fixedmatrix=FALSE, # resizefeatures=TRUE, upstream=20, downstream=20) # plotfeatureCoverage(covMA=fcov, method=mean, scales="fixed", scale_count_val=10^6) ## ----eval=FALSE------------------------------------------------------------------------------ # library("GenomicFeatures"); library(BiocParallel) # txdb <- loadDb("./data/tair10.sqlite") # eByg <- exonsBy(txdb, by=c("gene")) # bfl <- BamFileList(outpaths(args), yieldSize=50000, index=character()) # multicoreParam <- MulticoreParam(workers=8); 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]])$counts) # rownames(countDFeByg) <- names(rowRanges(counteByg[[1]])); colnames(countDFeByg) <- names(bfl) # rpkmDFeByg <- apply(countDFeByg, 2, function(x) returnRPKM(counts=x, ranges=eByg)) # write.table(countDFeByg, "results/countDFeByg.xls", col.names=NA, quote=FALSE, sep="\t") # write.table(rpkmDFeByg, "results/rpkmDFeByg.xls", col.names=NA, quote=FALSE, sep="\t") ## ----eval=FALSE------------------------------------------------------------------------------ # read.delim("results/countDFeByg.xls", row.names=1, check.names=FALSE)[1:4,1:5] ## ----eval=FALSE------------------------------------------------------------------------------ # read.delim("results/rpkmDFeByg.xls", row.names=1, check.names=FALSE)[1:4,1:4] ## ----eval=FALSE------------------------------------------------------------------------------ # library(DESeq2, quietly=TRUE); library(ape, warn.conflicts=FALSE) # countDF <- as.matrix(read.table("./results/countDFeByg.xls")) # colData <- data.frame(row.names=targetsin(args)$SampleName, condition=targetsin(args)$Factor) # dds <- DESeqDataSetFromMatrix(countData = countDF, colData = colData, design = ~ condition) # d <- cor(assay(rlog(dds)), method="spearman") # hc <- hclust(dist(1-d)) # pdf("results/sample_tree.pdf") # plot.phylo(as.phylo(hc), type="p", edge.col="blue", edge.width=2, show.node.label=TRUE, # no.margin=TRUE) # dev.off() ## ----eval=FALSE------------------------------------------------------------------------------ # library(edgeR) # countDF <- read.delim("results/countDFeByg.xls", row.names=1, check.names=FALSE) # targets <- read.delim("targets.txt", comment="#") # cmp <- readComp(file="targets.txt", format="matrix", delim="-") # edgeDF <- run_edgeR(countDF=countDF, targets=targets, cmp=cmp[[1]], independent=FALSE, mdsplot="") ## ----eval=FALSE------------------------------------------------------------------------------ # desc <- read.delim("data/desc.xls") # desc <- desc[!duplicated(desc[,1]),] # descv <- as.character(desc[,2]); names(descv) <- as.character(desc[,1]) # edgeDF <- data.frame(edgeDF, Desc=descv[rownames(edgeDF)], check.names=FALSE) # write.table(edgeDF, "./results/edgeRglm_allcomp.xls", quote=FALSE, sep="\t", col.names = NA) ## ----eval=FALSE------------------------------------------------------------------------------ # edgeDF <- read.delim("results/edgeRglm_allcomp.xls", row.names=1, check.names=FALSE) # pdf("results/DEGcounts.pdf") # DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=1)) # dev.off() # write.table(DEG_list$Summary, "./results/DEGcounts.xls", quote=FALSE, sep="\t", row.names=FALSE) ## ----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() ## ----eval=FALSE------------------------------------------------------------------------------ # library("biomaRt") # listMarts() # To choose BioMart database # m <- useMart("ENSEMBL_MART_PLANT"); listDatasets(m) # m <- useMart("ENSEMBL_MART_PLANT", dataset="athaliana_eg_gene") # listAttributes(m) # Choose data types you want to download # go <- getBM(attributes=c("go_accession", "tair_locus", "go_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") ## ----eval=FALSE------------------------------------------------------------------------------ # 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$Down; names(down) <- paste(names(down), "_down", sep="") # DEGlist <- c(up_down, up, down) # DEGlist <- DEGlist[sapply(DEGlist, length) > 0] # BatchResult <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="all", id_type="gene", # CLSZ=2, cutoff=0.9, gocats=c("MF", "BP", "CC"), # recordSpecGO=NULL) # library("biomaRt"); m <- useMart("ENSEMBL_MART_PLANT", dataset="athaliana_eg_gene") # goslimvec <- as.character(getBM(attributes=c("goslim_goa_accession"), mart=m)[,1]) # BatchResultslim <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="slim", id_type="gene", # myslimv=goslimvec, CLSZ=10, cutoff=0.01, # gocats=c("MF", "BP", "CC"), recordSpecGO=NULL) ## ----eval=FALSE------------------------------------------------------------------------------ # gos <- BatchResultslim[grep("M6-V6_up_down", BatchResultslim$CLID), ] # gos <- BatchResultslim # pdf("GOslimbarplotMF.pdf", height=8, width=10); goBarplot(gos, gocat="MF"); dev.off() # goBarplot(gos, gocat="BP") # goBarplot(gos, gocat="CC") ## ----eval=TRUE------------------------------------------------------------------------------- library(DESeq2) targetspath <- system.file("extdata", "targetsPE.txt", package="systemPipeR") parampath <- system.file("extdata", "tophat.param", package="systemPipeR") countDFeBygpath <- system.file("extdata", "countDFeByg.xls", package="systemPipeR") args <- suppressWarnings(systemArgs(sysma=parampath, mytargets=targetspath)) countDFeByg <- read.delim(countDFeBygpath, row.names=1) coldata <- DataFrame(assay=factor(rep(c("Ribo","mRNA"), each=4)), condition=factor(rep(as.character(targetsin(args)$Factor[1:4]), 2)), row.names=as.character(targetsin(args)$SampleName)[1:8]) coldata ## ----eval=TRUE------------------------------------------------------------------------------- dds <- DESeqDataSetFromMatrix(countData=as.matrix(countDFeByg[,rownames(coldata)]), colData = coldata, design = ~ assay + condition + assay:condition) # model.matrix(~ assay + condition + assay:condition, coldata) # Corresponding design matrix dds <- DESeq(dds, test="LRT", reduced = ~ assay + condition) res <- DESeq2::results(dds) head(res[order(res$padj),],4) # write.table(res, file="transleff.xls", quote=FALSE, col.names = NA, sep="\t") ## ----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, results='asis'------------------------------------------------------------- toLatex(sessionInfo())