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欧洲男女做受视频网站,北京seo地址,手机网站预约表单,辽宁省建设监理协会网站差异基因散点图绘制教程本期教程小杜的生信笔记，自2021年11月开始做的知识分享，主要内容是R语言绘图教程、转录组上游分析、转录组下游分析等内容。凡事在社群同学，可免费获得自2021年11月份至今全部教程，教程配备事例数据和相…

差异基因散点图绘制教程

本期教程

小杜的生信笔记，自2021年11月开始做的知识分享，主要内容是R语言绘图教程、转录组上游分析、转录组下游分析等内容。凡事在社群同学，可免费获得自2021年11月份至今全部教程，教程配备事例数据和相关代码，我们会持续更新中。

往期教程部分内容

绘图教程

导入所需R包

if (!require('ggplot2')) install.packages('ggplot2'); library('ggplot2')
if (!require('DESeq2')) install.packages('DESeq2'); library('DESeq2')
if (!require('biomaRt')) install.packages('biomaRt'); library('biomaRt')
if (!require('GEOquery')) install.packages('GEOquery'); library('GEOquery')
if (!require('vsn')) install.packages('vsn'); library('vsn')
if (!require('pheatmap')) install.packages('pheatmap'); library('pheatmap')

导入数据

cts <- read.csv("Input.data.csv",header = T, row.names = 1)

差异分析

##'@分组
sample_type = gsub(".*P7ma","Quiescent",gsub(".*P6ma","Cycling",colnames(cts)));
coldata = data.frame(row.names=colnames(cts), phenotype = sample_type ); #'@DESeq2差异分析
ddsMF <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ phenotype)
ddsMF <- DESeq(ddsMF)
resultsNames(ddsMF) #'get normalized counts'@标准化数据
nds <- DESeqTransform( SummarizedExperiment(log2(counts(ddsMF, normalized=TRUE) + 1), colData=colData(ddsMF)) ) # this gives log2(norm_cts + 1)
vsd <- vst(ddsMF, blind=FALSE) # variance stabilization

创建data.frame数据矩阵

quiescent = rownames(coldata)[coldata$phenotype=="Quiescent"]
cycling = rownames(coldata)[coldata$phenotype=="Cycling"]
data_cts_mets_avg = data.frame("quiescent"=rowMeans(assay(vsd)[, quiescent],na.rm=T),"cycling"=rowMeans(assay(vsd)[, cycling],na.rm=T))

绘图

##'@显著性差异基因
upCQ = resCQ[!is.na(resCQ$padj) & resCQ$padj < 0.1 & resCQ$log2FoldChange > 1,]
dwCQ = resCQ[!is.na(resCQ$padj) & resCQ$padj < 0.1 & resCQ$log2FoldChange < -1,]
dim(upCQ)
dim(dwCQ)plot(data_cts_mets_avg$quiescent, data_cts_mets_avg$cycling, pch=20, xlab="mRNA expression quiescent cells (Normalized counts)", ylab="mRNA expression cycling cells  (Normalized counts)",cex.lab=1.3,col=rgb(0.6,0.6,0.6,0.5));
abline(0,1,lty=2,col="black");
points(data_cts_mets_avg[rownames(upCQ),"quiescent"], data_cts_mets_avg[rownames(upCQ),"cycling"],pch=20,col=colors()[c(35)])
points(data_cts_mets_avg[rownames(dwCQ),"quiescent"], data_cts_mets_avg[rownames(dwCQ),"cycling"],pch=20,col=colors()[c(125)])
legend("topleft",legend=c("Up in Cycling","Down in Cycling"),col=colors()[c(35,125)],pch=20,inset=0.01,bty="n",cex=1.2)