本期数据挖掘任务来自于Paper:Tumor Evolution and Drug Response in Patient-Derived Organoid Models of Bladder Cancer
要重复的图表是:Figures:
PCA
ssGSEA
其实,是很简单的处理:
差异分析
后提取top1000进行PCA,要表达的意思是,tumor和TCGA的tumor能聚在一起;a
.GEOquery拿不到矩阵;b
.网页下载的矩阵是DESeq2-normalized counts;c
.英文不翻之DESeq2 doesn’t actually use normalized counts, rather it uses the raw counts and models the normalization inside the Generalized Linear Model (GLM). These normalized counts will be useful for downstream visualization of results, but cannot be used as input to DESeq2 or any other tools that peform differential expression analysis which use the negative binomial model.
###一些常规的设置
rm(list = ls())#清空环境变量
options(stringsAsFactors = F)##字符不作为因子读入
#####数据下载
library(GEOquery)
h<-'GSE103990.Rdata'
####getGPL获得平台的注释信息,但下载速度会慢很多
####而且注释文件格式大多不如bioconductor包好用
if(!file.exists(h)){
gset<-getGEO('GSE103990',destdir='.',
AnnotGPL=F,
getGPL=F)
save(gset,file=h)
}
load('GSE103990.Rdata')
ex<- exprs(gset[[1]])
pd <- pData(gset[[1]])
#system('nohup wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE103nnn/GSE103990/suppl/GSE103990_Normalized_counts.txt.gz &')
counts_nor <- read.table('GSE103990_Normalized_counts.txt.gz')
save(counts_nor,pd,file='Nor.Rdata')
rm(list = ls())
options(stringsAsFactors = F)
load('Nor.Rdata')
rownames(counts_nor)<- substr(rownames(counts_nor),1,15)
exprSet<- floor(counts_nor)
pd <- pd[match(colnames(exprSet),pd$description.1),]
group_list <- ifelse(grepl('org',pd$title),'org','tumor')
suppressMessages(library(DESeq2))
#### 第一步,构建DESeq2的DESeq对象
colData <- data.frame(row.names=colnames(exprSet),group_list=group_list)
dds <- DESeqDataSetFromMatrix(countData = exprSet,colData = colData,
design = ~ group_list)
#### 第二步,进行差异表达分析
dds2 <- DESeq(dds)
res <- results(dds2,contrast=c("group_list","org","tumor"))
resOrdered <- res[order(res$padj),]
DEG <- as.data.frame(resOrdered)
DESeq2_DEG = na.omit(DEG)
nrDEG=DESeq2_DEG[,c(2,6)]
colnames(nrDEG)=c('log2FoldChange','pvalue')
save(nrDEG, DESeq2_DEG, file = "DEG.Rdata")
rm(list=ls())
library(edgeR)
load('Nor.Rdata')
load('BLC.Rdata')
load('DEG.Rdata')
rownames(counts_nor)<- substr(rownames(counts_nor),1,15)
nor_BLCA <- edgeR::cpm(expr_BLC,log=T)
nor_paper <- edgeR::cpm(floor(counts_nor),log=T)
inter_gene<- intersect(rownames(nor_BLCA),rownames(nor_paper))
choose_gene <- rownames(nrDEG)[abs(nrDEG$log2FoldChange)>1.5&nrDEG$pvalue<0.01]
nrDEG <- nrDEG[order(abs(nrDEG$log2FoldChange),nrDEG$pvalue,decreasing = T),]
final_choose<- rownames(nrDEG)[rownames(nrDEG)%in%inter_gene][1:1000]
nr_paper <- nor_paper[final_choose,]
nr_BLC <- nor_BLCA[final_choose,]
nr_pca <- cbind(nr_paper,nr_BLC)
group_list <- ifelse(grepl('TCGA',colnames(nr_pca)),'TCGA',ifelse(grepl('org',pd$title),'org','tumor'))
library("FactoMineR")
library("factoextra")
####
df.pca <- PCA(t(nr_pca), graph = FALSE)
fviz_pca_ind(df.pca,
geom.ind = "point",
col.ind = group_list,
addEllipses = F,
legend.title = "Groups")
如果对GSEA中的细节感兴趣,看https://www.jianshu.com/p/be8fe1318850
rm(list=ls())
load('DEG.Rdata')
library(org.Hs.eg.db)
library(clusterProfiler)
gene <- bitr(rownames(nrDEG), fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = org.Hs.eg.db)
gene$logfc <- nrDEG$log2FoldChange[match(gene$ENSEMBL,rownames(nrDEG))]
geneList=nrDEG$log2FoldChange
names(geneList)=gene$ENTREZID
geneList=sort(geneList,decreasing = T)
head(geneList)
library(clusterProfiler)
kk_gse <- gseKEGG(geneList = geneList,
organism = 'hsa',
nPerm = 1000,
minGSSize = 10,
pvalueCutoff = 0.9,
verbose = FALSE)
paper_choose <- c('Cell adhesion molecules (CAMs)',
'Cell cycle',
'ErbB signaling pathway')
a <- list()
for (i in 1:3){
a[[i]]<- gseaplot2(kk_gse,
geneSetID = kk_gse@result$ID[kk_gse@result$Description==paper_choose[i]],
pvalue_table = T)
}
a
image.png
image.png
ErbB
Cell cycle
有趣的是,基本上没有重复出来,所以里面留有一个彩蛋!