## ----style, echo = FALSE, results = 'asis'---------------- BiocStyle::markdown() options(width=60, max.print=1000) knitr::opts_chunk$set( eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")), cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE")), tidy.opts=list(width.cutoff=60), tidy=TRUE) ## ----setup, echo=FALSE, message=FALSE, warning=FALSE, eval=FALSE---- # suppressPackageStartupMessages({ # library(systemPipeR) # }) ## ----load_systempiper, eval=TRUE, message=FALSE, warning=FALSE---- library(systemPipeR) ## ----genNew_wf, eval=FALSE-------------------------------- # systemPipeRdata::genWorkenvir(workflow = "riboseq", mydirname = "riboseq") # setwd("riboseq") ## ----load_targets, eval=TRUE------------------------------ targetspath <- system.file("extdata", "targetsPE.txt", package="systemPipeR") targets <- read.delim(targetspath, comment.char = "#")[,1:4] targets ## ----create_workflow, message=FALSE, eval=FALSE----------- # library(systemPipeR) # sal <- SPRproject() # sal ## ----load_SPR, message=FALSE, eval=FALSE, spr=TRUE-------- # cat(crayon::blue$bold("To use this workflow, following R packages are expected:\n")) # cat(c("'rtracklayer", "GenomicFeatures", "grid", "BiocParallel", "DESeq2", # "ape", "edgeR", "biomaRt", "BBmisc", "pheatmap","ggplot2'\n"), sep = "', '") # ###pre-end # appendStep(sal) <- LineWise( # code = { # library(systemPipeR) # library(rtracklayer) # library(GenomicFeatures) # library(ggplot2) # library(grid) # library(DESeq2, quietly=TRUE) # library(ape, warn.conflicts=FALSE) # library(edgeR) # library(biomaRt) # library(BBmisc) # Defines suppressAll() # library(pheatmap) # library(BiocParallel) # }, step_name = "load_SPR") ## ----preprocessing, message=FALSE, eval=FALSE, spr=TRUE---- # appendStep(sal) <- SYSargsList( # step_name = "preprocessing", # targets = "targetsPE.txt", dir = TRUE, # wf_file = "preprocessReads/preprocessReads-pe.cwl", # input_file = "preprocessReads/preprocessReads-pe_riboseq.yml", # dir_path = system.file("extdata/cwl", package = "systemPipeR"), # inputvars = c( # FileName1 = "_FASTQ_PATH1_", # FileName2 = "_FASTQ_PATH2_", # SampleName = "_SampleName_" # ), # dependency = c("load_SPR")) ## ----preprocessing_check, message=FALSE, eval=FALSE------- # yamlinput(sal, step="preprocessing")$Fct # # [1] "'trimbatch(fq, pattern=\"ACACGTCT\", internalmatch=FALSE, minpatternlength=6, Nnumber=1, polyhomo=50, minreadlength=16, maxreadlength=101)'" # cmdlist(sal, step = "preprocessing", targets = 1 ) ## ----fastq_report, eval=FALSE, message=FALSE, spr=TRUE---- # appendStep(sal) <- LineWise( # code = { # fq_files <- getColumn(sal, "preprocessing", "targetsWF", column = 1) # fqlist <- seeFastq(fastq = fq_files, batchsize = 10000, klength = 8) # png("./results/fastqReport.png", height = 162, width = 288 * length(fqlist)) # seeFastqPlot(fqlist) # dev.off() # }, # step_name = "fastq_report", # dependency = "preprocessing" # ) ## ----hisat2_index, eval=FALSE, spr=TRUE------------------- # appendStep(sal) <- SYSargsList( # step_name = "hisat2_index", # dir = FALSE, # targets=NULL, # wf_file = "hisat2/hisat2-index.cwl", # input_file="hisat2/hisat2-index.yml", # dir_path="param/cwl", # dependency = "load_SPR" # ) ## ----hisat2_mapping, eval=FALSE, spr=TRUE----------------- # appendStep(sal) <- SYSargsList( # step_name = "hisat2_mapping", # dir = TRUE, # targets ="targetsPE.txt", # wf_file = "workflow-hisat2/workflow_hisat2-pe.cwl", # input_file = "workflow-hisat2/workflow_hisat2-pe.yml", # dir_path = "param/cwl", # inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", # SampleName = "_SampleName_"), # dependency = c("hisat2_index") # ) ## ----bowtie2_alignment, eval=FALSE------------------------ # cmdlist(sal, step="hisat2_mapping", targets=1) ## ----align_stats, eval=FALSE, spr=TRUE-------------------- # appendStep(sal) <- LineWise( # code = { # fqpaths <- getColumn(sal, step = "hisat2_mapping", "targetsWF", column = "FileName1") # bampaths <- getColumn(sal, step = "hisat2_mapping", "outfiles", column = "samtools_sort_bam") # read_statsDF <- alignStats(args = bampaths, fqpaths = fqpaths, pairEnd = TRUE) # write.table(read_statsDF, "results/alignStats.xls", row.names=FALSE, quote=FALSE, sep="\t") # }, # step_name = "align_stats", # dependency = "hisat2_mapping") ## ----bam_IGV, eval=FALSE, spr=TRUE------------------------ # appendStep(sal) <- LineWise( # code = { # bampaths <- getColumn(sal, step = "hisat2_mapping", "outfiles", # column = "samtools_sort_bam") # symLink2bam( # sysargs = bampaths, htmldir = c("~/.html/", "somedir/"), # urlbase = "http://cluster.hpcc.ucr.edu/~tgirke/", # urlfile = "./results/IGVurl.txt") # }, # step_name = "bam_IGV", # dependency = "hisat2_mapping", # run_step = "optional" # ) ## ----genFeatures, eval=FALSE, spr=TRUE-------------------- # appendStep(sal) <- LineWise( # code = { # txdb <- suppressWarnings(makeTxDbFromGFF(file="data/tair10.gff", format="gff3", dataSource="TAIR", organism="Arabidopsis thaliana")) # feat <- genFeatures(txdb, featuretype="all", reduce_ranges=TRUE, upstream=1000, # downstream=0, verbose=TRUE) # }, # step_name = "genFeatures", # dependency = "hisat2_mapping", # run_step = "mandatory" # ) ## ----featuretypeCounts, eval=FALSE, spr=TRUE-------------- # appendStep(sal) <- LineWise( # code = { # outpaths <- getColumn(sal, step = "hisat2_mapping", "outfiles", column = "samtools_sort_bam") # fc <- featuretypeCounts(bfl=BamFileList(outpaths, yieldSize=50000), grl=feat, # singleEnd=FALSE, readlength=NULL, type="data.frame") # p <- plotfeaturetypeCounts(x=fc, graphicsfile="results/featureCounts.png", # graphicsformat="png", scales="fixed", anyreadlength=TRUE, # scale_length_val=NULL) # }, # step_name = "featuretypeCounts", # dependency = "genFeatures", # run_step = "mandatory" # ) ## ----featuretypeCounts_length, eval=FALSE, spr=TRUE------- # appendStep(sal) <- LineWise( # code = { # fc2 <- featuretypeCounts(bfl=BamFileList(outpaths, yieldSize=50000), grl=feat, # singleEnd=TRUE, readlength=c(74:76,99:102), type="data.frame") # p2 <- plotfeaturetypeCounts(x=fc2, graphicsfile="results/featureCounts2.png", # graphicsformat="png", scales="fixed", anyreadlength=FALSE, # scale_length_val=NULL) # }, # step_name = "featuretypeCounts_length", # dependency = "featuretypeCounts", # run_step = "mandatory" # ) ## ----pred_ORF, eval=FALSE, spr=TRUE----------------------- # appendStep(sal) <- LineWise( # code = { # txdb <- suppressWarnings(makeTxDbFromGFF(file="data/tair10.gff", format="gff3", organism="Arabidopsis")) # futr <- fiveUTRsByTranscript(txdb, use.names=TRUE) # dna <- extractTranscriptSeqs(FaFile("data/tair10.fasta"), futr) # uorf <- predORF(dna, n="all", mode="orf", longest_disjoint=TRUE, strand="sense") # }, # step_name = "pred_ORF", # dependency = "featuretypeCounts_length" # ) ## ----scale_ranges, eval=FALSE, spr=TRUE------------------- # appendStep(sal) <- LineWise( # code = { # grl_scaled <- scaleRanges(subject=futr, query=uorf, type="uORF", verbose=TRUE) # export.gff3(unlist(grl_scaled), "results/uorf.gff") # }, # step_name = "scale_ranges", # dependency = "pred_ORF" # ) ## ----translate, eval=FALSE, spr=TRUE---------------------- # appendStep(sal) <- LineWise( # code = { # translate(unlist(getSeq(FaFile("data/tair10.fasta"), grl_scaled[[7]]))) # }, # step_name = "translate", # dependency = "scale_ranges" # ) ## ----add_features, eval=FALSE, spr=TRUE------------------- # appendStep(sal) <- LineWise( # code = { # feat <- genFeatures(txdb, featuretype="all", reduce_ranges=FALSE) # feat <- c(feat, GRangesList("uORF"=unlist(grl_scaled))) # }, # step_name = "add_features", # dependency = c("genFeatures", "scale_ranges") # ) ## ----pred_sORFs, eval=FALSE, spr=TRUE--------------------- # appendStep(sal) <- LineWise( # code = { # feat <- genFeatures(txdb, featuretype="intergenic", reduce_ranges=TRUE) # intergenic <- feat$intergenic # strand(intergenic) <- "+" # dna <- getSeq(FaFile("data/tair10.fasta"), intergenic) # names(dna) <- mcols(intergenic)$feature_by # sorf <- suppressWarnings(predORF(dna, n="all", mode="orf", longest_disjoint=TRUE, strand="both")) # sorf <- sorf[width(sorf) > 60] # Remove sORFs below length cutoff, here 60bp # intergenic <- split(intergenic, mcols(intergenic)$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("data/tair10.fasta"), unlist(grl_scaled_intergenic))) # }, # step_name = "pred_sORFs", # dependency = c("add_features") # ) ## ----binned_CDS_coverage, eval=FALSE, spr=TRUE------------ # appendStep(sal) <- LineWise( # code = { # grl <- cdsBy(txdb, "tx", use.names=TRUE) # fcov <- featureCoverage(bfl=BamFileList(outpaths[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) # }, # step_name = "binned_CDS_coverage", # dependency = c("add_features") # ) ## ----coverage_upstream_downstream, eval=FALSE, spr=TRUE---- # appendStep(sal) <- LineWise( # code = { # fcov <- featureCoverage(bfl=BamFileList(outpaths[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) # png("./results/coverage_upstream_downstream.png", height=12, width=24, units="in", res=72) # plotfeatureCoverage(covMA=fcov, method=mean, scales="fixed", extendylim=2, # scale_count_val=10^6) # dev.off() # }, # step_name = "coverage_upstream_downstream", # dependency = c("binned_CDS_coverage") # ) ## ----coverage_combined, eval=FALSE, spr=TRUE-------------- # appendStep(sal) <- LineWise( # code = { # fcov <- featureCoverage(bfl=BamFileList(outpaths[1:4]), 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) # png("./results/featurePlot.png", height=12, width=24, units="in", res=72) # plotfeatureCoverage(covMA=fcov, method=mean, scales="fixed", extendylim=2, # scale_count_val=10^6) # dev.off() # }, # step_name = "coverage_combined", # dependency = c("binned_CDS_coverage", "coverage_upstream_downstream") # ) ## ----coverage_nuc_level, eval=FALSE, spr=TRUE------------- # appendStep(sal) <- LineWise( # code = { # fcov <- featureCoverage(bfl=BamFileList(outpaths[1:2]), grl=grl[1], # resizereads=NULL, readlengthrange=NULL, Nbins=NULL, method=mean, # fixedmatrix=FALSE, resizefeatures=TRUE, upstream=20, # downstream=20, outfile=NULL) # }, # step_name = "coverage_nuc_level", # dependency = c("coverage_combined") # ) ## ----read_counting, eval=FALSE, spr=TRUE------------------ # appendStep(sal) <- LineWise( # code = { # txdb <- loadDb("./data/tair10.sqlite") # eByg <- exonsBy(txdb, by=c("gene")) # bfl <- BamFileList(outpaths, 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=FALSE, # BPPARAM = multicoreParam)) # countDFeByg <- sapply(seq(along=counteByg), function(x) assays(counteByg[[x]])$counts) # rownames(countDFeByg) <- names(rowRanges(counteByg[[1]])) # colnames(countDFeByg) <- names(bfl) # rpkmDFeByg <- apply(countDFeByg, 2, function(x) returnRPKM(counts=x, ranges=eByg)) # write.table(countDFeByg, "results/countDFeByg.xls", col.names=NA, quote=FALSE, sep="\t") # write.table(rpkmDFeByg, "results/rpkmDFeByg.xls", col.names=NA, quote=FALSE, sep="\t") # ## Creating a SummarizedExperiment object # colData <- data.frame(row.names=SampleName(sal, "hisat2_mapping"), # condition=getColumn(sal, "hisat2_mapping", position = "targetsWF", column = "Factor")) # colData$condition <- factor(colData$condition) # countDF_se <- SummarizedExperiment::SummarizedExperiment(assays = countDFeByg, # colData = colData) # ## Add results as SummarizedExperiment to the workflow object # SE(sal, "read_counting") <- countDF_se # }, # step_name = "read_counting", # dependency = c("featuretypeCounts") # ) ## ----read_counting_view, eval=TRUE------------------------ read.delim(system.file("extdata/countDFeByg.xls", package = "systemPipeR"), row.names=1, check.names=FALSE)[1:4,1:5] ## ----read_rpkm_view, eval=FALSE--------------------------- # read.delim(system.file("extdata/rpkmDFeByg.xls", package = "systemPipeR"), # row.names=1, check.names=FALSE)[1:4,1:5] ## ----sample_tree, eval=FALSE, eval=FALSE, spr=TRUE-------- # appendStep(sal) <- LineWise( # code = { # ## Extracting SummarizedExperiment object # se <- SE(sal, "read_counting") # dds <- DESeqDataSet(se, design = ~ condition) # d <- cor(assay(rlog(dds)), method="spearman") # hc <- hclust(dist(1-d)) # png("results/sample_tree.png") # plot.phylo(as.phylo(hc), type="p", edge.col="blue", edge.width=2, show.node.label=TRUE, no.margin=TRUE) # dev.off() # }, # step_name = "sample_tree", # dependency = "read_counting") ## ----run_edgeR, eval=FALSE, spr=TRUE---------------------- # appendStep(sal) <- LineWise( # code = { # countDF <- read.delim("results/countDFeByg.xls", row.names=1, check.names=FALSE) # cmp <- readComp(stepsWF(sal)[['hisat2_mapping']], format="matrix", delim="-") # edgeDF <- run_edgeR(countDF=countDF, targets=targetsWF(sal)[['hisat2_mapping']], cmp=cmp[[1]], independent=FALSE, mdsplot="") # }, # step_name = "run_edgeR", # dependency = "read_counting") ## ----custom_annot, eval=FALSE, spr=TRUE------------------- # appendStep(sal) <- LineWise( # code = { # m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="https://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) # }, # step_name = "custom_annot", # dependency = "run_edgeR") ## ----filter_degs, eval=FALSE, spr=TRUE-------------------- # appendStep(sal) <- LineWise( # code = { # edgeDF <- read.delim("results/edgeRglm_allcomp.xls", row.names=1, check.names=FALSE) # png("results/DEGcounts.png") # 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) # }, # step_name = "filter_degs", # dependency = "custom_annot") ## ----venn_diagram, eval=FALSE, spr=TRUE------------------- # appendStep(sal) <- LineWise( # code = { # vennsetup <- overLapper(DEG_list$Up[6:9], type="vennsets") # vennsetdown <- overLapper(DEG_list$Down[6:9], type="vennsets") # png("results/vennplot.png") # vennPlot(list(vennsetup, vennsetdown), mymain="", mysub="", colmode=2, ccol=c("blue", "red")) # dev.off() # }, # step_name = "venn_diagram", # dependency = "filter_degs") ## ----get_go_annot, eval=FALSE, spr=TRUE------------------- # appendStep(sal) <- LineWise( # code = { # # listMarts() # To choose BioMart database # # listMarts(host="plants.ensembl.org") # # m <- useMart("plants_mart", host="https://plants.ensembl.org") # #listDatasets(m) # m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="https://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,] # if(!dir.exists("./data/GO")) 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") # }, # step_name = "get_go_annot", # dependency = "filter_degs") ## ----go_enrich, eval=FALSE, spr=TRUE---------------------- # appendStep(sal) <- LineWise( # code = { # 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$Down; names(down) <- paste(names(down), "_down", sep="") # DEGlist <- c(up_down, up, down) # DEGlist <- DEGlist[sapply(DEGlist, length) > 0] # BatchResult <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="all", id_type="gene", CLSZ=2, cutoff=0.9, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL) # m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="https://plants.ensembl.org") # goslimvec <- as.character(getBM(attributes=c("goslim_goa_accession"), mart=m)[,1]) # BatchResultslim <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="slim", id_type="gene", myslimv=goslimvec, CLSZ=10, cutoff=0.01, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL) # }, # step_name = "go_enrich", # dependency = "get_go_annot") ## ----go_plot, eval=FALSE, spr=TRUE------------------------ # appendStep(sal) <- LineWise( # code = { # gos <- BatchResultslim[grep("M6-V6_up_down", BatchResultslim$CLID), ] # gos <- BatchResultslim # png("results/GOslimbarplotMF.png", height=8, width=10) # goBarplot(gos, gocat="MF") # goBarplot(gos, gocat="BP") # goBarplot(gos, gocat="CC") # dev.off() # }, # step_name = "go_plot", # dependency = "go_enrich") ## ----diff_loading, eval=FALSE, spr=TRUE------------------- # appendStep(sal) <- LineWise( # code = { # countDFeByg <- read.delim("results/countDFeByg.xls", row.names=1, check.names=FALSE) # coldata <- S4Vectors::DataFrame(assay=factor(rep(c("Ribo","mRNA"), each=4)), # condition= factor(rep(as.character(targetsWF(sal)[['hisat2_mapping']]$Factor[1:4]), 2)), # row.names=as.character(targetsWF(sal)[['hisat2_mapping']]$SampleName)[1:8]) # coldata # }, # step_name = "diff_loading", # dependency = "go_plot") ## ----diff_translational_eff, eval=FALSE, spr=TRUE--------- # appendStep(sal) <- LineWise( # code = { # dds <- DESeq2::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 <- DESeq2::DESeq(dds, test="LRT", reduced = ~ assay + condition) # res <- DESeq2::results(dds) # head(res[order(res$padj),],4) # write.table(res, file="transleff.xls", quote=FALSE, col.names = NA, sep="\t") # }, # step_name = "diff_translational_eff", # dependency = "diff_loading") ## ----heatmap, eval=FALSE, spr=TRUE------------------------ # appendStep(sal) <- LineWise( # code = { # geneids <- unique(as.character(unlist(DEG_list[[1]]))) # y <- assay(rlog(dds))[geneids, ] # y <- y[rowSums(y[])>0,] # png("results/heatmap1.png") # pheatmap(y, scale="row", clustering_distance_rows="correlation", clustering_distance_cols="correlation") # dev.off() # }, # step_name = "heatmap", # dependency = "diff_translational_eff") ## ----sessionInfo, eval=FALSE, spr=TRUE-------------------- # appendStep(sal) <- LineWise( # code = { # sessionInfo() # }, # step_name = "sessionInfo", # dependency = "heatmap") ## ----runWF, eval=FALSE------------------------------------ # sal <- runWF(sal, run_step = "mandatory") ## ----runWF_cluster, eval=FALSE---------------------------- # # wall time in mins, memory in MB # resources <- list(conffile=".batchtools.conf.R", # template="batchtools.slurm.tmpl", # Njobs=18, # walltime=120, # ntasks=1, # ncpus=4, # memory=1024, # partition = "short" # ) # sal <- addResources(sal, c("hisat2_mapping"), resources = resources) # sal <- runWF(sal, run_step = "mandatory") ## ----plotWF, eval=FALSE----------------------------------- # plotWF(sal, rstudio = TRUE) ## ----statusWF, eval=FALSE--------------------------------- # sal # statusWF(sal) ## ----logsWF, eval=FALSE----------------------------------- # sal <- renderLogs(sal) ## ----list_tools------------------------------------------- if(file.exists(file.path(".SPRproject", "SYSargsList.yml"))) { local({ sal <- systemPipeR::SPRproject(resume = TRUE) systemPipeR::listCmdTools(sal) systemPipeR::listCmdModules(sal) }) } else { cat(crayon::blue$bold("Tools and modules required by this workflow are:\n")) cat(c("hisat2/2.1.0", "samtools/1.14"), sep = "\n") } ## ----sessionInfo_final, eval=TRUE------------------------- sessionInfo()