4.筛选高变基因(top1000) rv <- genefilter::rowVars(data)select <- order(rv, decreasing = TRUE)[seq_len(1000)]pca_data <- cbind(t(log10(data[select,]+1)),group) 5.进行主成分分析 expr_pca <- prcomp(pca_data[,1:1000],scale = T,center = T) 6.可视化——碎石图 fviz_screeplot(...
dds2 <- DESeqDataSetFromMatrix(countmatrix,colData=table2,design =~condition) dds2 <- dds[rowSums(counts(dds2)) > 1,] head(dds2) rld1<- rlog(dds2) plotPCA(rld1, intgroup=c( "name","condition")) library(ggplot2) data1 <- plotPCA(rld1, intgroup=c("condition","name"), retu...
normCout<-function(filtered){filtered<-as.matrix(filtered)df<-data.frame(condition=colnames(filtered))sample<-unique(df$condition)[2]dds<-DESeqDataSetFromMatrix(filtered,colData=df,design=~condition)dds<-estimateSizeFactors(dds)dds_DE<-DESeq(dds)res_DE<-results(dds_DE,alpha=0.05,contrast=c("...
4.筛选高变基因(top1000) rv <- genefilter::rowVars(data)select <- order(rv, decreasing = TRUE)[seq_len(1000)]pca_data <- cbind(t(log10(data[select,]+1)),group) 5.进行主成分分析 expr_pca <- prcomp(pca_data[,1:1000],scale = T,center = T) 6.可视化——碎石图 fviz_screeplot(...
RNA-seq下游分析之 PCA图_欧阳火火的博客-CSDN博客 rm(list=ls())mydata <- read.table("C:/Users/gao/Desktop/all.id.txt",header =TRUE,quote='\t',skip = 1)smpleNames <- c('mesc_1','mesc_1','mesc_2','mesc_2','mesc_3','mesc_3','mesc_4','mesc_4','mesc_5','...