--- title: 【迁移】Counts矩阵的标准化方法:TMM和VST、RLOG urlname: Counts-ju-zhen-de-biao-zhun-hua-fang-fa--TMM-he-VST-RLOG date: 2022-07-27 20:10:38 tags: ['TMM', 'VST', 'RLOG'] --- * [TMM](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-3-r25):The Trimmed Mean of M value by edgeR * [VST](http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html):The variance stabilizing transformation by DESeq2 * [RLOG](http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html):The regularized-logarithm transformation by DESeq2 Counts矩阵来源于[STAR匹配得到的结果](/STAR--yi-jian-jiao-ben):`df <- read.csv('GSE123379.csv', row.names = 1)` ## 安装补充包 * [conda activate tcga](/cong-cha-yi-ji-yin-dao-RRA-ju-he) * conda install -c bioconda bioconductor-edger -y ## TMM方法 ```R f_counts2TMM <- function(countsMatrix){ require(edgeR) TMM <- DGEList(counts = countsMatrix) TMM <- calcNormFactors(TMM, method = 'TMM') cpm(TMM, normalized.lib.sizes = TRUE, log=F) } countsMatrix <- df[-(1:3)] TMM <- f_counts2TMM(countsMatrix) TMM ``` ## VST方法 ```R f_counts2VST <- function(countsMatrix){ require(DESeq2) conditions <- factor(rep("Control",ncol(countsMatrix))) colData_b <- data.frame(row.names = colnames(countsMatrix), conditions) dds <- DESeqDataSetFromMatrix(countData = countsMatrix, colData = colData_b, design = ~ 1) vsd <- vst(object=dds, blind=T) assay(vsd) } countsMatrix <- df[-(1:3)] VST <- f_counts2VST(countsMatrix) VST ``` ## RLOG方法 ```R f_counts2RLOG <- function(countsMatrix){ require(DESeq2) conditions <- factor(rep("Control",ncol(countsMatrix))) colData_b <- data.frame(row.names = colnames(countsMatrix), conditions) dds <- DESeqDataSetFromMatrix(countData = countsMatrix, colData = colData_b, design = ~ 1) rld <- rlog(object=dds, blind=T) assay(rld) } countsMatrix <- df[-(1:3)] RLOG <- f_counts2RLOG(countsMatrix) RLOG ```