TCGA的28篇教程-风险因子关联图-一个价值1000但是迟到的答案
早在 2017-03-13 https://mp.weixin.qq.com/s/J-vaFq1Vv-zR1LC0Wop1zw 我就在生信技能树推出过绘图交易专区： [有偿专区]TCGA 预后作图 ， 那个时候隐隐约约知道这是一个很大的市场，可惜人的精力是有限的， 我需要持续更新 10000+ 生物信息学基础教程，而且那个时候也没有一个愿意负责这块的小伙伴，只好搁置到了现在。
其实那个需求很简单：https://www.ncbi.nlm.nih.gov/pubmed/24893932 文章里面也说的很清楚，如下：
#
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5051943/figure/djw200-F2/
# https://www.ncbi.nlm.nih.gov/pubmed/24893932
# Sixteen of the 111 most significantly altered miRNAs were associated with OS across different clinical subclasses of the TCGA-derived LUAD cohort.
# A linear prognostic model of eight miRNAs (miR-31, miR-196b, miR-766, miR-519a-1, miR-375, miR-187, miR-331 and miR-101-1) was constructed and weighted by the importance scores from the supervised principal component method to divide patients into high- and low-risk groups.
# Patients assigned to the high-risk group exhibited poor OS compared with patients in the low-risk group (hazard ratio [HR]=1.99, P <0.001).
# The eight-miRNA signature is an independent prognostic marker of OS of LUAD patients and demonstrates good performance for predicting 5-year OS (Area Under the respective ROC Curves [AUC] = 0.626, P = 0.003), especially for non-smokers (AUC = 0.686, P = 0.023).
恰好我要去做TCGA商业演讲，这个知识点在授课范围，也顺便解决一下这个一年前的问题。
首先下载好TCGA的LUAD的miRNA表达数据和临床数据
下载方式我就不多说了，大家看我以前的教程：
- 使用R语言的cgdsr包获取TCGA数据 （cBioPortal）
- TCGA的28篇教程- 使用R语言的RTCGA包获取TCGA数据 （离线打包版本, FireBrowse）
- TCGA的28篇教程- 使用R语言的RTCGAToolbox包获取TCGA数据 （Broad Institute FireBrowse portal）
- TCGA的28篇教程- 批量下载TCGA所有数据 （ UCSC的 XENA）
这里选取最方便的来举例说明咯：
rm(list=ls())
library(survival)
library(survminer)
if(F){
library(RTCGA.miRNASeq)
??RTCGA.miRNASeq
s=rownames(LUAD.miRNASeq)[seq(1,nrow(LUAD.miRNASeq),by=3)]
expr <- expressionsTCGA(LUAD.miRNASeq)
dim(expr)
expr[1:40,1:4]
expr=as.data.frame(expr[seq(1,nrow(expr),by=3),3:ncol(expr)])
mi=colnames(expr)
expr=apply(expr,1,as.numeric)
colnames(expr)=s
rownames(expr)=mi
expr[1:4,1:4]
expr=na.omit(expr)
dim(expr)
expr=expr[apply(expr, 1,function(x){sum(x>1)>10}),]
dim(expr)
library(RTCGA.clinical)
??RTCGA.clinical
meta <- LUAD.clinical
tmp=as.data.frame(colnames(meta))
meta[(grepl('patient.bcr_patient_barcode',colnames(meta)))]
meta[(grepl('patient.days_to_last_followup',colnames(meta)))]
meta[(grepl('patient.days_to_death',colnames(meta)))]
meta[(grepl('patient.vital_status',colnames(meta)))]
## patient.race # patient.age_at_initial_pathologic_diagnosis # patient.gender
# patient.stage_event.clinical_stage
meta=as.data.frame(meta[c('patient.bcr_patient_barcode','patient.vital_status',
'patient.days_to_death','patient.days_to_last_followup',
'patient.race',
'patient.age_at_initial_pathologic_diagnosis',
'patient.gender' ,
'patient.stage_event.pathologic_stage')])
#meta[(grepl('patient.stage_event.pathologic_stage',colnames(meta)))]
save(expr,meta,file = 'TCGA-LUAD-miRNA-example.Rdata')
}
load(file = 'TCGA-LUAD-miRNA-example.Rdata')
接着整理好数据格式
还是那句老话，数据分析师其实大部分时间花在数据整理上面：
group_list=ifelse(substr(colnames(expr),14,15)=='01','tumor','normal')
table(group_list)
if(!file.exists('TCGA-LUAD-survival_input.Rdata')){
exprSet=na.omit(expr)
exprSet=exprSet[,group_list=='tumor']
head(meta)
colnames(meta)
meta[,3][is.na(meta[,3])]=0
meta[,4][is.na(meta[,4])]=0
meta$days=as.numeric(meta[,3])+as.numeric(meta[,4])
meta=meta[,c(1:2,5:9)]
colnames(meta)
colnames(meta)=c('ID','event','race','age','gender','stage',"days")
library(survival)
library(survminer)
meta$event=ifelse(meta$event=='alive',0,1)
meta$age=as.numeric(meta$age)
library(stringr)
meta$stage=str_split(meta$stage,' ',simplify = T)[,2]
table( meta$stage)
boxplot(meta$age)
meta$age_group=ifelse(meta$age>median(meta$age,na.rm = T),'older','younger')
table(meta$race)
meta$time=meta$days/30
phe=meta
head(phe)
phe$ID=toupper(phe$ID)
phe=phe[match(substr(colnames(exprSet),1,12),phe$ID),]
head(phe)
exprSet[1:4,1:4]
save(exprSet,phe,file='TCGA-LUAD-survival_input.Rdata')
}
load(file='TCGA-LUAD-survival_input.Rdata')
head(phe)
exprSet[1:4,1:4]
清洗干净的数据如下：
> head(phe)
ID event race age gender stage days age_group time
52.70.0 TCGA-05-4244 0 <NA> 70 male iv 0 older 0.000000
52.70.0.2 TCGA-05-4249 0 <NA> 67 male ib 1158 older 38.600000
52.70.0.3 TCGA-05-4250 1 <NA> 79 female iiia 121 older 4.033333
58.73.0 TCGA-05-4382 0 <NA> 68 male ib 607 older 20.233333
58.73.0.1 TCGA-05-4389 0 <NA> 70 male ia 1369 older 45.633333
58.73.0.2 TCGA-05-4395 1 <NA> 76 male iiib 0 older 0.000000
> exprSet[1:4,1:4]
TCGA-05-4244-01A-01T-1108-13 TCGA-05-4249-01A-01T-1108-13
hsa-let-7a-1 3985 8916
hsa-let-7a-2 7947 17800
hsa-let-7a-3 4128 9079
hsa-let-7b 9756 32960
就很容易做分析了。
挑选感兴趣的基因构建coxph模型
后期我会录制视频讲解其中原理，现在大家就先看看吧，反正我写的代码都是可以运行的，不像其它垃圾教程。
# miR-31, miR-196b, miR-766, miR-519a-1, miR-375, miR-187, miR-331 and miR-101-1
# hsa-mir-31,hsa-mir-196b,hsa-mir-766,hsa-mir-519a-1,hsa-mir-375,hsa-mir-187,hsa-mir-331,hsa-mir-101-1
e=t(exprSet[c('hsa-mir-31','hsa-mir-196b','hsa-mir-766','hsa-mir-519a-1','hsa-mir-375','hsa-mir-187','hsa-mir-331','hsa-mir-101-1'),])
e=log2(e+1)
colnames(e)=c('miR31','miR196b','miR766','miR519a1','miR375','miR187','miR331','miR101')
dat=cbind(phe,e)
dat$gender=factor(dat$gender)
dat$stage=factor(dat$stage)
colnames(dat)
s=Surv(time, event) ~ miR31+miR196b+miR766+miR519a1+miR375+miR187+miR331+miR101
model <- coxph(s, data = dat )
summary(model,data=dat)
options(scipen=1)
ggforest(model, data =dat,
main = "Hazard ratio",
cpositions = c(0.10, 0.22, 0.4),
fontsize = 1.0,
refLabel = "1", noDigits = 4)
# 对于生存数据预后模型评价很多采用C-index ，但c-index展示没有roc曲线图来的直观
new_dat=dat
fp <- predict(model,new_dat,type="risk")
fp <- predict(model,new_dat,type="expected")
plot(fp,phe$days)
fp <- predict(model,new_dat)
basehaz(model)
library(Hmisc)
options(scipen=200)
with(new_dat,rcorr.cens(fp,Surv(time, event) ))
# http://dni-institute.in/blogs/cox-regression-interpret-result-and-predict/
最后进行可视化
出图的代码往往很简单：
library(cowplot)
library(pheatmap)
# https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html
fp_dat=data.frame(s=1:length(fp),v=as.numeric(sort(fp )))
sur_dat=data.frame(s=1:length(fp),
t=phe[names(sort(fp )),'time'] ,
e=phe[names(sort(fp )),'event'] )
sur_dat$e=ifelse(sur_dat$e==0,'alive','death')
exp_dat=new_dat[names(sort(fp )),10:17]
plot.point=ggplot(fp_dat,aes(x=s,y=v))+geom_point()
plot.sur=ggplot(sur_dat,aes(x=s,y=t))+geom_point(aes(col=e))
mycolors <- colorRampPalette(c("black", "green", "red"), bias = 1.2)(100)
tmp=t(scale(exp_dat))
tmp[tmp > 1] = 1
tmp[tmp < -1] = -1
plot.h=pheatmap(tmp,col= mycolors,show_colnames = F,cluster_cols = F)
plot_grid(plot.point, plot.sur, plot.h$gtable,
labels = c("A", "B","C"),
align = 'v',ncol = 1)
但是出图后的美化，就考验人了，这里不赘述啦。
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