inst/extdata/workflows/rnaseq/systemPipeRNAseq.R
In tgirke/systemPipeRdata: systemPipeRdata: Workflow templates and sample data
## pre code {
## white-space: pre !important;
## overflow-x: scroll !important;
## word-break: keep-all !important;
## word-wrap: initial !important;
## }
## ----style, echo = FALSE, results = 'asis'----------------
BiocStyle::markdown()
options(width = 60, max.print = 1000)
knitr::opts_chunk$set(
eval = as.logical(Sys.getenv("KNITR_EVAL", "TRUE")),
cache = as.logical(Sys.getenv("KNITR_CACHE", "TRUE")),
tidy.opts = list(width.cutoff = 60), tidy = TRUE)
## ----setup, echo=FALSE, messages=FALSE, warnings=FALSE----
suppressPackageStartupMessages({
library(systemPipeR)
})
## ----genNew_wf, eval=FALSE--------------------------------
## systemPipeRdata::genWorkenvir(workflow = "rnaseq", mydirname = "rnaseq")
## setwd("rnaseq")
## ----load_targets_file, eval=TRUE-------------------------
targetspath <- system.file("extdata", "targetsPE.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment.char = "#")
targets[1:4,-c(5,6)]
## ----create_workflow, message=FALSE, eval=FALSE-----------
## library(systemPipeR)
## sal <- SPRproject()
## sal
## ----load_SPR, message=FALSE, eval=FALSE, spr=TRUE--------
## cat(crayon::blue$bold("To use this workflow, following R packages are expected:\n"))
## cat(c("'GenomicFeatures", "BiocParallel", "DESeq2",
## "ape", "edgeR", "biomaRt", "pheatmap","ggplot2'\n"), sep = "', '")
## ###pre-end
## appendStep(sal) <- LineWise(code = {
## library(systemPipeR)
## }, 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.yml",
## dir_path = system.file("extdata/cwl", package = "systemPipeR"),
## inputvars = c(
## FileName1 = "_FASTQ_PATH1_",
## FileName2 = "_FASTQ_PATH2_",
## SampleName = "_SampleName_"
## ),
## dependency = c("load_SPR"))
## ----custom_preprocessing_function, eval=FALSE------------
## appendStep(sal) <- LineWise(
## code = {
## filterFct <- function(fq, cutoff = 20, Nexceptions = 0) {
## qcount <- rowSums(as(quality(fq), "matrix") <= cutoff, na.rm = TRUE)
## # Retains reads where Phred scores are >= cutoff with N exceptions
## fq[qcount <= Nexceptions]
## }
## save(list = ls(), file = "param/customFCT.RData")
## },
## step_name = "custom_preprocessing_function",
## dependency = "preprocessing"
## )
## ----editing_preprocessing, message=FALSE, eval=FALSE-----
## yamlinput(sal, "preprocessing")$Fct
## yamlinput(sal, "preprocessing", "Fct") <- "'filterFct(fq, cutoff=20, Nexceptions=0)'"
## yamlinput(sal, "preprocessing")$Fct ## check the new function
## cmdlist(sal, "preprocessing", targets = 1) ## check if the command line was updated with success
## ----trimming, eval=FALSE, spr=TRUE-----------------------
## appendStep(sal) <- SYSargsList(
## step_name = "trimming",
## targets = "targetsPE.txt",
## wf_file = "trimmomatic/trimmomatic-pe.cwl", input_file = "trimmomatic/trimmomatic-pe.yml",
## dir_path = system.file("extdata/cwl", package = "systemPipeR"),
## inputvars=c(FileName1="_FASTQ_PATH1_", FileName2="_FASTQ_PATH2_", SampleName="_SampleName_"),
## dependency = "load_SPR",
## run_step = "optional")
## ----fastq_report, eval=FALSE, message=FALSE, spr=TRUE----
## appendStep(sal) <- LineWise(code = {
## fastq <- getColumn(sal, step = "preprocessing", "targetsWF", column = 1)
## fqlist <- seeFastq(fastq = fastq, 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 ="preprocessing",
## wf_file = "workflow-hisat2/workflow_hisat2-pe.cwl",
## input_file = "workflow-hisat2/workflow_hisat2-pe.yml",
## dir_path = "param/cwl",
## inputvars = c(preprocessReads_1 = "_FASTQ_PATH1_", preprocessReads_2 = "_FASTQ_PATH2_",
## SampleName = "_SampleName_"),
## rm_targets_col = c("FileName1", "FileName2"),
## dependency = c("preprocessing", "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 = "preprocessing", "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"
## )
## ----create_db, eval=FALSE, spr=TRUE----------------------
## appendStep(sal) <- LineWise(
## code = {
## library(GenomicFeatures)
## txdb <- suppressWarnings(makeTxDbFromGFF(file="data/tair10.gff", format="gff", dataSource="TAIR", organism="Arabidopsis thaliana"))
## saveDb(txdb, file="./data/tair10.sqlite")
## },
## step_name = "create_db",
## dependency = "hisat2_mapping")
## ----read_counting, eval=FALSE, spr=TRUE------------------
## appendStep(sal) <- LineWise(
## code = {
## library(GenomicFeatures); library(BiocParallel)
## txdb <- loadDb("./data/tair10.sqlite")
## outpaths <- getColumn(sal, step = "hisat2_mapping", "outfiles", column = "samtools_sort_bam")
## eByg <- exonsBy(txdb, by = c("gene"))
## bfl <- BamFileList(outpaths, yieldSize = 50000, index = character())
## multicoreParam <- MulticoreParam(workers = 4); 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 = "create_db")
## ----sample_tree, eval=FALSE, spr=TRUE--------------------
## appendStep(sal) <- LineWise(
## code = {
## library(DESeq2, quietly=TRUE); library(ape, warn.conflicts=FALSE)
## ## 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 = {
## library(edgeR)
## 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 = {
## library("biomaRt")
## 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 = {
## library("biomaRt")
## # 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 = {
## 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$Down; names(down) <- paste(names(down), "_down", sep="")
## DEGlist <- c(up_down, up, down)
## DEGlist <- DEGlist[sapply(DEGlist, length) > 0]
## BatchResult <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="all", id_type="gene", CLSZ=2, cutoff=0.9, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL)
## m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="https://plants.ensembl.org")
## goslimvec <- as.character(getBM(attributes=c("goslim_goa_accession"), mart=m)[,1])
## BatchResultslim <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="slim", id_type="gene", myslimv=goslimvec, CLSZ=10, cutoff=0.01, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL)
## write.table(BatchResultslim, "results/GOBatchSlim.xls", row.names=FALSE, quote=FALSE, sep="\t")
## },
## step_name = "go_enrich",
## dependency = "get_go_annot")
## ----go_plot, eval=FALSE, spr=TRUE------------------------
## appendStep(sal) <- LineWise(
## code = {
## gos <- BatchResultslim[grep("M6-V6_up_down", BatchResultslim$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")
## ----heatmap, eval=FALSE, spr=TRUE------------------------
## appendStep(sal) <- LineWise(
## code = {
## library(pheatmap)
## geneids <- unique(as.character(unlist(DEG_list[[1]])))
## y <- assay(rlog(dds))[geneids, ]
## png("results/heatmap1.png")
## pheatmap(y, scale="row", clustering_distance_rows="correlation", clustering_distance_cols="correlation")
## dev.off()
## },
## step_name = "heatmap",
## dependency = "go_enrich")
## ----sessionInfo, eval=FALSE, spr=TRUE--------------------
## appendStep(sal) <- LineWise(
## code = {
## sessionInfo()
## },
## step_name = "sessionInfo",
## dependency = "heatmap")
## ----runWF, eval=FALSE------------------------------------
## sal <- runWF(sal)
## ----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)
## ----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$bold("Tools and modules required by this workflow are:\n"))
cat(c("trimmomatic/0.39", "samtools/1.14", "hisat2/2.1.0"), sep = "\n")
}
## ----report_session_info, eval=TRUE-----------------------
sessionInfo()
tgirke/systemPipeRdata documentation built on Oct. 19, 2023, 9:20 p.m.
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## pre code {
## white-space: pre !important;
## overflow-x: scroll !important;
## word-break: keep-all !important;
## word-wrap: initial !important;
## }
## ----style, echo = FALSE, results = 'asis'----------------
BiocStyle::markdown()
options(width = 60, max.print = 1000)
knitr::opts_chunk$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, messages=FALSE, warnings=FALSE----
suppressPackageStartupMessages({
library(systemPipeR)
})
## ----genNew_wf, eval=FALSE--------------------------------
## systemPipeRdata::genWorkenvir(workflow = "rnaseq", mydirname = "rnaseq")
## setwd("rnaseq")
## ----load_targets_file, eval=TRUE-------------------------
targetspath <- system.file("extdata", "targetsPE.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment.char = "#")
targets[1:4,-c(5,6)]
## ----create_workflow, message=FALSE, eval=FALSE-----------
## library(systemPipeR)
## sal <- SPRproject()
## sal
## ----load_SPR, message=FALSE, eval=FALSE, spr=TRUE--------
## cat(crayon::blue$bold("To use this workflow, following R packages are expected:\n"))
## cat(c("'GenomicFeatures", "BiocParallel", "DESeq2",
## "ape", "edgeR", "biomaRt", "pheatmap","ggplot2'\n"), sep = "', '")
## ###pre-end
## appendStep(sal) <- LineWise(code = {
## library(systemPipeR)
## }, step_name = "load_SPR")
## ----preprocessing, message=FALSE, eval=FALSE, spr=TRUE----
## appendStep(sal) <- SYSargsList(
## step_name = "preprocessing",
## targets = "targetsPE.txt", dir = TRUE,
## wf_file = "preprocessReads/preprocessReads-pe.cwl",
## input_file = "preprocessReads/preprocessReads-pe.yml",
## dir_path = system.file("extdata/cwl", package = "systemPipeR"),
## inputvars = c(
## FileName1 = "_FASTQ_PATH1_",
## FileName2 = "_FASTQ_PATH2_",
## SampleName = "_SampleName_"
## ),
## dependency = c("load_SPR"))
## ----custom_preprocessing_function, eval=FALSE------------
## appendStep(sal) <- LineWise(
## code = {
## filterFct <- function(fq, cutoff = 20, Nexceptions = 0) {
## qcount <- rowSums(as(quality(fq), "matrix") <= cutoff, na.rm = TRUE)
## # Retains reads where Phred scores are >= cutoff with N exceptions
## fq[qcount <= Nexceptions]
## }
## save(list = ls(), file = "param/customFCT.RData")
## },
## step_name = "custom_preprocessing_function",
## dependency = "preprocessing"
## )
## ----editing_preprocessing, message=FALSE, eval=FALSE-----
## yamlinput(sal, "preprocessing")$Fct
## yamlinput(sal, "preprocessing", "Fct") <- "'filterFct(fq, cutoff=20, Nexceptions=0)'"
## yamlinput(sal, "preprocessing")$Fct ## check the new function
## cmdlist(sal, "preprocessing", targets = 1) ## check if the command line was updated with success
## ----trimming, eval=FALSE, spr=TRUE-----------------------
## appendStep(sal) <- SYSargsList(
## step_name = "trimming",
## targets = "targetsPE.txt",
## wf_file = "trimmomatic/trimmomatic-pe.cwl", input_file = "trimmomatic/trimmomatic-pe.yml",
## dir_path = system.file("extdata/cwl", package = "systemPipeR"),
## inputvars=c(FileName1="_FASTQ_PATH1_", FileName2="_FASTQ_PATH2_", SampleName="_SampleName_"),
## dependency = "load_SPR",
## run_step = "optional")
## ----fastq_report, eval=FALSE, message=FALSE, spr=TRUE----
## appendStep(sal) <- LineWise(code = {
## fastq <- getColumn(sal, step = "preprocessing", "targetsWF", column = 1)
## fqlist <- seeFastq(fastq = fastq, 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 ="preprocessing",
## wf_file = "workflow-hisat2/workflow_hisat2-pe.cwl",
## input_file = "workflow-hisat2/workflow_hisat2-pe.yml",
## dir_path = "param/cwl",
## inputvars = c(preprocessReads_1 = "_FASTQ_PATH1_", preprocessReads_2 = "_FASTQ_PATH2_",
## SampleName = "_SampleName_"),
## rm_targets_col = c("FileName1", "FileName2"),
## dependency = c("preprocessing", "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 = "preprocessing", "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"
## )
## ----create_db, eval=FALSE, spr=TRUE----------------------
## appendStep(sal) <- LineWise(
## code = {
## library(GenomicFeatures)
## txdb <- suppressWarnings(makeTxDbFromGFF(file="data/tair10.gff", format="gff", dataSource="TAIR", organism="Arabidopsis thaliana"))
## saveDb(txdb, file="./data/tair10.sqlite")
## },
## step_name = "create_db",
## dependency = "hisat2_mapping")
## ----read_counting, eval=FALSE, spr=TRUE------------------
## appendStep(sal) <- LineWise(
## code = {
## library(GenomicFeatures); library(BiocParallel)
## txdb <- loadDb("./data/tair10.sqlite")
## outpaths <- getColumn(sal, step = "hisat2_mapping", "outfiles", column = "samtools_sort_bam")
## eByg <- exonsBy(txdb, by = c("gene"))
## bfl <- BamFileList(outpaths, yieldSize = 50000, index = character())
## multicoreParam <- MulticoreParam(workers = 4); 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 = "create_db")
## ----sample_tree, eval=FALSE, spr=TRUE--------------------
## appendStep(sal) <- LineWise(
## code = {
## library(DESeq2, quietly=TRUE); library(ape, warn.conflicts=FALSE)
## ## 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 = {
## library(edgeR)
## 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 = {
## library("biomaRt")
## 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$Down[6:9], type="vennsets")
## png("results/vennplot.png")
## vennPlot(list(vennsetup, vennsetdown), mymain="", mysub="", colmode=2, ccol=c("blue", "red"))
## dev.off()
## },
## step_name = "venn_diagram",
## dependency = "filter_degs")
## ----get_go_annot, eval=FALSE, spr=TRUE-------------------
## appendStep(sal) <- LineWise(
## code = {
## library("biomaRt")
## # listMarts() # To choose BioMart database
## # listMarts(host="plants.ensembl.org")
## m <- useMart("plants_mart", host="https://plants.ensembl.org")
## #listDatasets(m)
## m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="https://plants.ensembl.org")
## # listAttributes(m) # Choose data types you want to download
## go <- getBM(attributes=c("go_id", "tair_locus", "namespace_1003"), mart=m)
## go <- go[go[,3]!="",]; go[,3] <- as.character(go[,3])
## go[go[,3]=="molecular_function", 3] <- "F"; go[go[,3]=="biological_process", 3] <- "P"; go[go[,3]=="cellular_component", 3] <- "C"
## go[1:4,]
## if(!dir.exists("./data/GO")) dir.create("./data/GO")
## write.table(go, "data/GO/GOannotationsBiomart_mod.txt", quote=FALSE, row.names=FALSE, col.names=FALSE, sep="\t")
## catdb <- makeCATdb(myfile="data/GO/GOannotationsBiomart_mod.txt", lib=NULL, org="", colno=c(1,2,3), idconv=NULL)
## save(catdb, file="data/GO/catdb.RData")
## },
## step_name = "get_go_annot",
## dependency = "filter_degs")
## ----go_enrich, eval=FALSE, spr=TRUE----------------------
## appendStep(sal) <- LineWise(
## code = {
## library("biomaRt")
## load("data/GO/catdb.RData")
## DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=50), plot=FALSE)
## up_down <- DEG_list$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)
## 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)
## write.table(BatchResultslim, "results/GOBatchSlim.xls", row.names=FALSE, quote=FALSE, sep="\t")
## },
## 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$CLID), ]
## gos <- BatchResultslim
## png("results/GOslimbarplotMF.png", height=8, width=10)
## goBarplot(gos, gocat="MF")
## goBarplot(gos, gocat="BP")
## goBarplot(gos, gocat="CC")
## dev.off()
## },
## step_name = "go_plot",
## dependency = "go_enrich")
## ----heatmap, eval=FALSE, spr=TRUE------------------------
## appendStep(sal) <- LineWise(
## code = {
## library(pheatmap)
## geneids <- unique(as.character(unlist(DEG_list[[1]])))
## y <- assay(rlog(dds))[geneids, ]
## png("results/heatmap1.png")
## pheatmap(y, scale="row", clustering_distance_rows="correlation", clustering_distance_cols="correlation")
## dev.off()
## },
## step_name = "heatmap",
## dependency = "go_enrich")
## ----sessionInfo, eval=FALSE, spr=TRUE--------------------
## appendStep(sal) <- LineWise(
## code = {
## sessionInfo()
## },
## step_name = "sessionInfo",
## dependency = "heatmap")
## ----runWF, eval=FALSE------------------------------------
## sal <- runWF(sal)
## ----runWF_cluster, eval=FALSE----------------------------
## # wall time in mins, memory in MB
## resources <- list(conffile=".batchtools.conf.R",
## template="batchtools.slurm.tmpl",
## Njobs=18,
## walltime=120,
## ntasks=1,
## ncpus=4,
## memory=1024,
## partition = "short"
## )
## sal <- addResources(sal, c("hisat2_mapping"), resources = resources)
## sal <- runWF(sal)
## ----plotWF, eval=FALSE-----------------------------------
## plotWF(sal, rstudio = TRUE)
## ----statusWF, eval=FALSE---------------------------------
## sal
## statusWF(sal)
## ----logsWF, eval=FALSE-----------------------------------
## sal <- renderLogs(sal)
## ----list_tools-------------------------------------------
if(file.exists(file.path(".SPRproject", "SYSargsList.yml"))) {
local({
sal <- systemPipeR::SPRproject(resume = TRUE)
systemPipeR::listCmdTools(sal)
systemPipeR::listCmdModules(sal)
})
} else {
cat(crayon::blue$bold("Tools and modules required by this workflow are:\n"))
cat(c("trimmomatic/0.39", "samtools/1.14", "hisat2/2.1.0"), sep = "\n")
}
## ----report_session_info, eval=TRUE-----------------------
sessionInfo()
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