Analysis of single cell RNA-seq data

Posted on 2019-06-06 | In single_cell |

1 Tabula Muris

1.1 介绍

他们将高通量但低覆盖率的10倍数据与低通量高覆盖率的数据结合起来，FACS-sortedcell+Smartseq2。

1.22 数据下载

下载FACS data

热图太丑怎么办(pheatmap的使用)

Posted on 2019-06-06 | In R |

准备一个表达谱矩阵，横轴为100个基因，纵轴为208个样本，如下：

首先安装pheatmap

Ven图、聚类分析、相关性分析

Posted on 2019-06-06 | In R |

绘制Ven图

#载入相应的安装包
library(VennDiagram)
#进入到需要绘制Ven图的文件夹
setwd("/data/YJ/YanNa/stringtie/trs/Pt/diff10/LPt")
#filelist=list("Pt15","Pt17","Pt19","Pt2-","Pt20","Pt22","Pt3","Pt5","Pt6","Pt8","Pt9")
#将需要绘制Ven图的样本的rowname汇集到一个list中
filelist=list("LPt16","LPt24")
for (Pt in filelist){
  files=list.files(path="/data/YJ/YanNa/stringtie/trs/Pt/diff10/LPt",pattern = Pt)
  trslist=list()
  image_name=paste(Pt,"_Ven",".tiff",sep="")
  for (i in files){
    base=strsplit(i,"_")[[1]][1]
    trslist[base]=read.table(i,header = T,colClasses = c("character","NULL","NULL"))
  }
  #设置颜色
  num=148
  col=c()
  for (i in 1:length(trslist)){
    col[i]=colors()[num]
    num=num+150
  }
  #绘制Ven图
  venn.diagram(trslist,filename = image_name,col="transparent",fil=col,alpha=0.5,resolution = 500)