# #——风格,回声= FALSE,结果= '飞机 '-------------------------------------------------------- BiocStyle:减价()选项(宽度= 100,max.print = 1000) knitr:: opts_chunk设置(eval = as.logical (Sys美元。采用“KNITR_EVAL”,“真正的”)),缓存= as.logical (Sys。## getv(“KNITR_CACHE”，“TRUE”))##设置，echo=FALSE，消息=FALSE，warnings=FALSE-------------------------------------------- suppressPackageStartupMessages({library(csaw) library(edgeR) library(genome ranges) library(ChIPseeker) library(genfilter) library(txdb . haspiens . ucsc .hg19. knowngene) library(org. hsas .eg.db) library(clusterProfiler)}) ##——配置测试 ------------------------------------------------------------------------------- stopifnot (getRversion () > = ' 3.2 ' & & getRversion () < 3.3, BiocInstaller: biocVersion() = = 3.2” " ) ## ---- null-p,缓存= TRUE --------------------------------------------------------------------------- ## 100000 t空下,n = 6 n < - 6;m <- matrix(rnorm(n * 100000)， ncol=n) P <- genfilter::rowttests(m, factor(rep(1:2, each=3)))$ P。值分位数(P c(。001 . 01 . 05)嘘(P = 20 ) ## ---- eval = FALSE ---------------------------------------------------------------------------------- # 装饰图案(“ChIPseeker ") ## ---- csaw-setup ----------------------------------------------------------------------------------- 文件< -本地({acc < - c (es_1 =“SRR074398 es_2 =“SRR074399 tn_1 =“SRR074417”,tn_2 = " SRR074418”)data.frame(治疗=子 ("_.*", "", 名称(acc)),复制=子(“。*_"， "， names(acc))， sra=sprintf("%s。Sra "， acc)， fastq=sprintf("% s.s fastq.gz"， acc)， bam=sprintf("% s.s fastq.gz.subread. zip "， bam=sprintf("% s.s fastq.gz.subread. zip "， acc)，BAM”,acc), row.names = acc, stringsAsFactors = FALSE ) }) ## ---- csaw-setwd eval = FALSE ----------------------------------------------------------------------- # setwd(“~ / UseBioconductor-data / ChIPSeq / NFYA ") ## ---- csaw-reduction,eval = FALSE ------------------------------------------------------------------- # 库(csaw) #库(GenomicRanges) #碎片弹。Len <- 110 #系统。time({ # data <- windowCounts(files$bam, width=10, ext=frag.len) # }) # 156 seconds # acc <- sub(".fastq.*", "", data$bam.files) # colData(data) <- cbind(files[acc,], colData(data)) ## ----load-csaw------------------------------------------------------------------------------------ data <- readRDS("csaw-data.Rds") ## ----csaw-filter---------------------------------------------------------------------------------- library(edgeR) # for aveLogCPM() keep <- aveLogCPM(assay(data)) >= -1 data <- data[keep,] ## ----csaw-normalize, eval=FALSE------------------------------------------------------------------- # system.time({ # binned <- windowCounts(files$bam, bin=TRUE, width=10000) # }) #139 second # normfacs <- normalize(binned) ## ----csaw-normacs-load---------------------------------------------------------------------------- normfacs <- readRDS("csaw-normfacs.Rds") ## ----csaw-experimental-design--------------------------------------------------------------------- design <- model.matrix(~Treatment, colData(data)) ## ----csaw-de-------------------------------------------------------------------------------------- y <- asDGEList(data, norm.factors=normfacs) y <- estimateDisp(y, design) fit <- glmQLFit(y, design, robust=TRUE) results <- glmQLFTest(fit, contrast=c(0, 1)) head(results$table) ## ----csaw-merge-windows--------------------------------------------------------------------------- merged <- mergeWindows(rowRanges(data), tol=1000L) ## ----csaw-combine-merged-tests-------------------------------------------------------------------- tabcom <- combineTests(merged$id, results$table) head(tabcom) ## ----csaw-grangeslist----------------------------------------------------------------------------- gr <- rowRanges(data) mcols(gr) <- as(results$table, "DataFrame") grl <- split(gr, merged$id) mcols(grl) <- as(tabcom, "DataFrame") ## ----sessionInfo---------------------------------------------------------------------------------- sessionInfo()