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歡迎來到醫(yī)科研,，這里是白介素2的讀書筆記,，跟我一起聊臨床與科研的故事, 生物醫(yī)學(xué)數(shù)據(jù)挖掘,，R語言,，TCGA,、GEO數(shù)據(jù)挖掘,。 
 R語言生存分析 
生存分析是醫(yī)學(xué)數(shù)據(jù)挖掘中的重要內(nèi)容 R語言中用于生存分析的包主要有survival與survminer
library(survival)
library(survminer)
library(RTCGA.clinical)
提取生存信息 應(yīng)用RTCGA.clinical包 Sys.setlocale('LC_ALL','C')
## [1] "C"
survivalTCGA(BRCA.clinical, OV.clinical,
extract.cols = "admin.disease_code") -> BRCAOV.survInfo
head(BRCAOV.survInfo)
## times bcr_patient_barcode patient.vital_status admin.disease_code
## 1 3767 TCGA-3C-AAAU 0 brca
## 2 3801 TCGA-3C-AALI 0 brca
## 3 1228 TCGA-3C-AALJ 0 brca
## 4 1217 TCGA-3C-AALK 0 brca
## 5 158 TCGA-4H-AAAK 0 brca
## 6 1477 TCGA-5L-AAT0 0 brca
Surv(OS_time,status)~factor(分組因素) 構(gòu)建生存對象需要生存時間,，生存狀態(tài),，datafit <- survfit(Surv(times, patient.vital_status) ~ admin.disease_code,
data = BRCAOV.survInfo)
fit
## Call: survfit(formula = Surv(times, patient.vital_status) ~ admin.disease_code,
## data = BRCAOV.survInfo)
##
## n events median 0.95LCL 0.95UCL
## admin.disease_code=brca 1098 104 3472 3126 4456
## admin.disease_code=ov 576 297 1354 1229 1470
survminer包可視化:生存對象,，data,risk.table是風(fēng)險表 survminer::ggsurvplot(fit, data = BRCAOV.survInfo, risk.table = TRUE)
 Fig1對生存曲線進(jìn)一步細(xì)節(jié)控制美化 調(diào)整參數(shù)ggsurvplot(
fit, # 生存對象
data = BRCAOV.survInfo, # data.
risk.table = TRUE, # 風(fēng)險表.
pval = TRUE, # p-value of log-rank test.
conf.int = TRUE, # 95%CI
# 生存曲線的點(diǎn)估計.
xlim = c(0,2000), # present narrower X axis, but not affect
# survival estimates.
break.time.by = 500, # break X axis in time intervals by 500.
ggtheme = theme_minimal(), # 主題定制.
risk.table.y.text.col = T, # colour risk table text annotations.
risk.table.y.text = FALSE # show bars instead of names in text annotations
# in legend of risk table
)
 image.png基礎(chǔ)版本的生存曲線 library("survival")
head(lung)
## inst time status age sex ph.ecog ph.karno pat.karno meal.cal wt.loss
## 1 3 306 2 74 1 1 90 100 1175 NA
## 2 3 455 2 68 1 0 90 90 1225 15
## 3 3 1010 1 56 1 0 90 90 NA 15
## 4 5 210 2 57 1 1 90 60 1150 11
## 5 1 883 2 60 1 0 100 90 NA 0
## 6 12 1022 1 74 1 1 50 80 513 0
fit<- survfit(Surv(time, status) ~ sex, data = lung)
繪圖ggsurvplot(fit, data = lung)
 image.png定制版的生存曲線 ggsurvplot(fit, data = lung,
title = "Survival curves", subtitle = "Based on Kaplan-Meier estimates",#標(biāo)題
caption = "created with survminer",#說明-右下角
font.title = c(16, "bold", "darkblue"),#標(biāo)題字體
font.subtitle = c(15, "bold.italic", "purple"),#副標(biāo)題字體
font.caption = c(14, "plain", "orange"),#說明字體
font.x = c(14, "bold.italic", "red"),#x軸字體
font.y = c(14, "bold.italic", "darkred"),#y軸字體
font.tickslab = c(12, "plain", "darkgreen"))#
 image.png風(fēng)險表risk.table ggsurvplot(fit, data = lung, risk.table = TRUE)
 image.pngncens plot-展示刪失數(shù)據(jù)的情況 這個功能在文章里面見得比較少，但是也能畫 ggsurvplot(fit, data = lung, risk.table = TRUE, ncensor.plot = TRUE)
 image.png
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