# #——fig.align =“中心”,回声= FALSE ---------------------------------------- 选项(rmarkdown.html_vignette。check_title =FALSE) ## ----loadingPackages, message=FALSE, fig.cap="从苏丹数据转换计数的日志直方图。这说明了数量非常小的大量基因以及观察到的数量的巨大异质性。"---- library(HTSFilter) library(edgeR) library(DESeq2) data("sultan") hist(log(exprs(sultan)+1)， col="grey"， breaks=25, main=""， xlab=" log(counts+1)") pData(sultan) dim(sultan) ## ----matrix, figure cap="全局Jaccard索引为sultan数据。对TMM归一化后的各种阈值进行指数计算，黄土曲线(蓝线)叠加，基于数据的阈值(红叉和红虚线)为11.764。"---- mat <- exprs(sultan) conds <- as.character(pData(sultan)$cell.line) ##仅25个测试阈值以减少计算时间filter <- HTSFilter(mat, conds, s.min=1, s.m max=200, s.m len=25) mat <- filter$filteredData dim(mat) dim(filter$removedData) ## ----refilter, fig.cap="HTSFilter对预过滤的苏丹数据进行过滤。(左)对TMM归一化后的各种阈值计算预过滤数据上的全局Jaccard指数，叠加黄土曲线(蓝线)，基于数据的阈值(红叉和红虚线)等于1.64。(右)应用HTSFilter后重新过滤的数据直方图。”——par (mfrow = c(1、2)、3月= c(4 4 2 2)过滤器。2 < - HTSFilter(垫、电导率,s.len = 25)暗(filter.2 removedData美元)嘘(日志(filter.2 filteredData + 1美元),坳=“灰色”,减免= 25日主要= " xlab = "日志(计数+ 1 )") ## ---- dge ---------------------------------------------------------------------- dge < - DGEList(数量= exprs(苏丹),组=电导率)dge < - calcNormFactors (dge) dge < - estimateDisp (dge)等< -准确(dge)等< - HTSFilter (et, DGEList = dge s.len = 25,情节= FALSE)美元filteredData暗(et)类(et) topTags (et) # #——dgetoptags --------------------------------------------------------------- topTags (et) # #——dge2 --------------------------------------------------------------------- 设计< - model.matrix(~电导率)dge < - DGEList(数量= exprs(苏丹),组=电导率)dge < - calcNormFactors (dge) dge < - estimateDisp (dge、设计)符合< - glmFit (dge、设计)轻轨交通< - glmLRT(健康,系数= 2)轻轨车< - HTSFilter轻轨车,DGEGLM =健康,s.len = 25, plot=FALSE)$filteredData dim(lrt) class(lrt) ## ----dge2toptags-------------------------------------------------------------- topTags(lrt) ## ----cds2--------------------------------------------------------------------- conds <- gsub(" ", ".", conds) dds <- DESeqDataSetFromMatrix(countData = exprs(sultan), colData = data.frame(cell.line = factor(conds)), design = ~ cell.line) dds <- DESeq(dds) filter <- HTSFilter(dds, s.len=25, plot=FALSE)$filteredData class(filter) dim(filter) res <- results(filter, independentFiltering=FALSE) head(res) ## ----ses2, eval=FALSE--------------------------------------------------------- # library(EDASeq) # ses <- newSeqExpressionSet(exprs(sultan), # phenoData=pData(sultan)) # ses.norm <- betweenLaneNormalization(ses, which="full") ## ----ses3, eval=FALSE--------------------------------------------------------- # filter <- HTSFilter(counts(ses.norm), conds, s.len=25, norm="none", # plot=FALSE) # head(filter$on) # table(filter$on) ## ----sessionInfo-------------------------------------------------------------- sessionInfo()