作业 1
根据R包org.Hs.eg.db找到下面ensembl 基因ID 对应的基因名(symbol)
A<- read.table("xl.txt")
library(org.Hs.eg.db)
g2s=toTable(org.Hs.egSYMBOL)
g2e=toTable(org.Hs.egENSEMBL)
a<-A$V1
for (i in 1:6) {
A$V1[i]<-strsplit(a,'[.]')[[i]][1]}
names(A)[1]<-"ensembl_id"
A<-merge(A,g2e,by=("ensembl_id"))
A<-merge(A,g2s,by=("gene_id"))
运行结果
作业2
根据R包hgu133a.db找到下面探针对应的基因名(symbol)
B<-read.table("e2.txt")
library(hgu133a.db)
ids=toTable(hgu133aSYMBOL)
head(ids)
names(B)[1]<-"probe_id"
merge(B,ids,by=("probe_id"))
运行结果
probe_id symbol
1 1053_at RFC2
2 117_at HSPA6
3 121_at PAX8
4 1255_g_at GUCA1A
5 1316_at THRA
6 1320_at PTPN21
7 1405_i_at CCL5
8 1431_at CYP2E1
9 1438_at EPHB3
10 1487_at ESRRA
11 1494_f_at CYP2A6
12 1598_g_at GAS6
13 160020_at MMP14
14 1729_at TRADD
15 177_at PLD1
作业3
找到R包CLL内置的数据集的表达矩阵里面的TP53基因的表达量，并且绘制在 progres.-stable分组的boxplot图
suppressPackageStartupMessages(library(CLL))
data(sCLLex)
sCLLex
exprSet=exprs(sCLLex)
pd=pData(sCLLex)
library(hgu95av2.db)
ids=toTable(hgu95av2SYMBOL)
boxplot(exprSet['1939_at',]~pd$Disease)
boxplot(exprSet['1974_s_at',]~pd$Disease)
boxplot(exprSet['31618_at',]~pd$Disease)
作业4
找到BRCA1基因在TCGA数据库的乳腺癌数据集(Breast Invasive Carcinoma (TCGA, PanCancer Atlas))的表达情况
a<-read.table('plot.txt',sep = '\t',fill = T,header = T)
colnames(a)=c('id','subtype','expression','mut')
dat=a
library(ggplot2)
ggsave('plot-again-BRCA1-TCGA-BRCA-cbioportal.png')
作业5
找到TP53基因在TCGA数据库的乳腺癌数据集的表达量分组看其是否影响生存
a=read.table('BRCA_7157_50_50.csv',sep = ',',fill = T,header = T)
tmp=a
library(ggplot2)
library(survival)
library(survminer)
tmp$Status=ifelse(tmp$Status=='Dead',1,0)
sfit <- surv_fit(Surv(Days,Status)~Group,data=tmp)
summary(sfit)
ggsurvplot(sfit,conf.int = F,pval = TRUE)
ggsave('survival_TP53_in_BRCA_TCGA.png')
作业6
下载数据集GSE17215的表达矩阵并且提取下面的基因画热图
ACTR3B ANLN BAG1 BCL2 BIRC5 BLVRA CCNB1 CCNE1 CDC20 CDC6 CDCA1 CDH3 CENPF CEP55 CXXC5 EGFR ERBB2 ESR1 EXO1 FGFR4 FOXA1 FOXC1 GPR160 GRB7 KIF2C KNTC2 KRT14 KRT17 KRT5 MAPT MDM2 MELK MIA MKI67 MLPH MMP11 MYBL2 MYC NAT1 ORC6L PGR PHGDH PTTG1 RRM2 SFRP1 SLC39A6 TMEM45B TYMS UBE2C UBE2T
if (!file.exists(f)) {
gset <- getGEO('GSE17215',destdir=".",AnnotGPL = F,
getGPL =F)
save(gset,file = f)
}
a=gset[[1]]
load('GSE17215_eSet.Rdata')
dat=exprs(a)
library(hgu133a.db)
ids=toTable(hgu133aSYMBOL)
dat=dat[ids$probe_id,]
ids$median=apply(dat,1,median)
ids=ids[order(ids$symbol,ids$median,decreasing = T),]
ids=ids[!duplicated(ids$symbol),]
dat=dat[ids$probe_id,]
rownames(dat)=ids$symbol
ng='ACTR3B ANLN BAG1 BCL2 BIRC5 BLVRA CCNB1 CCNE1 CDC20 CDC6 CDCA1 CDH3 CENPF CEP55 CXXC5 EGFR ERBB2 ESR1 EXO1 FGFR4 FOXA1 FOXC1 GPR160 GRB7 KIF2C KNTC2 KRT14 KRT17 KRT5 MAPT MDM2 MELK MIA MKI67 MLPH MMP11 MYBL2 MYC NAT1 ORC6L PGR PHGDH PTTG1 RRM2 SFRP1 SLC39A6 TMEM45B TYMS UBE2C UBE2T'
ng=strsplit(ng,' ')[[1]]
ng=ng[ng%in% rownames(dat)]
dat[ng,]
dat=log2(dat)
pheatmap::pheatmap(dat)
作业7
下载数据集GSE24673的表达矩阵计算样本的相关性并且绘制热图，需要标记上样本分组信息
if (!file.exists(f)) {
gset <- getGEO('GSE17215',destdir=".",AnnotGPL = F,
getGPL =F)
save(gset,file = f)
}
a=gset[[1]]
load('GSE17215_eSet.Rdata')
dat=exprs(a)
library(hgu133a.db)
ids=toTable(hgu133aSYMBOL)
dat=dat[ids$probe_id,]
ids$median=apply(dat,1,median)
ids=ids[order(ids$symbol,ids$median,decreasing = T),]
ids=ids[!duplicated(ids$symbol),]
dat=dat[ids$probe_id,]
rownames(dat)=ids$symbol
ng='ACTR3B ANLN BAG1 BCL2 BIRC5 BLVRA CCNB1 CCNE1 CDC20 CDC6 CDCA1 CDH3 CENPF CEP55 CXXC5 EGFR ERBB2 ESR1 EXO1 FGFR4 FOXA1 FOXC1 GPR160 GRB7 KIF2C KNTC2 KRT14 KRT17 KRT5 MAPT MDM2 MELK MIA MKI67 MLPH MMP11 MYBL2 MYC NAT1 ORC6L PGR PHGDH PTTG1 RRM2 SFRP1 SLC39A6 TMEM45B TYMS UBE2C UBE2T'
ng=strsplit(ng,' ')[[1]]
ng=ng[ng%in% rownames(dat)]
dat[ng,]
dat=log2(dat)
pheatmap::pheatmap(dat)
作业8
找到 GPL6244 platform of Affymetrix Human Gene 1.0 ST Array 对应的R的bioconductor注释包，并且安装它！
BiocManager::install("hugene10sttranscriptcluster.db",ask = F,update = F)
作业9
下载数据集GSE42872的表达矩阵，并且分别挑选出 所有样本的(平均表达量/sd/mad/)最大的探针，并且找到它们对应的基因
options(stringsAsFactors = F)
f='GSE42872_eSet.Rdata'
library(GEOquery)
if (!file.exists(f)) {
gset <- getGEO('GSE42872',destdir=".",AnnotGPL = F,
getGPL =F)
save(gset,file = f)
}
a=gset[[1]]
load('GSE42872_eSet.Rdata')
dat=exprs(a)
sort(apply(dat,1,mean),decreasing = T)[1]
运行结果
7917645
14.185
作业10
下载数据集GSE42872的表达矩阵，并且根据分组使用limma做差异分析，得到差异结果矩阵
options(stringsAsFactors = F)
f='GSE42872_eSet.Rdata'
library(GEOquery)
if (!file.exists(f)) {
gset <- getGEO('GSE42872',destdir=".",AnnotGPL = F,
getGPL =F)
save(gset,file = f)
}
a=gset[[1]]
load('GSE42872_eSet.Rdata')
dat=exprs(a)
pd=pData(a)
group_list=unlist(lapply(pd$title, function(x){
strsplit(x,' ')[[1]][4]
}))
experSet=dat
#差异分析
suppressMessages(library(limma))
design<-model.matrix(~0+factor(group_list))
colnames(design)=levels(factor(group_list))
row.names(design)=colnames(experSet)
design
contrast.matrix<-makeContrasts(paste0(unique(group_list),collapse = "-"),levels = design)
fit<-lmFit(experSet,design)
fit2<-contrasts.fit(fit,contrast.matrix)
fit2<-eBayes(fit2)
tempOutput=topTable(fit2,coef = 1,n=Inf)
nrDEG=na.omit(tempOutput)
