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菜鸟团作业:纯R代码完结ssGSEA算法评价肿瘤免疫滋润程度

检查原文请移步菜鸟团大众号。

方针:复线文章:Local mutational diversity drives intratumoral immune he邪火小径在哪terogeneity in non-small cell lung cancer 的两幅图


过程:

Step1:

提取Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade弥补资料的28belgium个免疫基因集,每个基因集对应的基因列表。这一步昨日现已完结。


Step2:

ssGSEA剖析

需求的输入数说唱据是

  • 基因集

  • 表达矩阵

  • 表型信息

从GEO下载表达矩阵和表型信息,菜鸟团学徒的悉数代码,原帖:


###矩阵信息提取a <- read.table('GSE112996_merged_fpkm_table.txt.gz',
header = T,
row.names=1)
raw_data<- a[,-1]
#struggle##表型信息提取
pheno <- read.csv(file = 'GSE112996_series_matrix.txt')
pheno <- data.frame(num1 = strsplit(as.character(pheno[42,]),split='\t')[[1]][-1],
num2 = gsub('patient: No.','P',strsplit(as.character(phen魏钰庭o[51,]),split='\t')[[1]][-1]))
####数据过滤
data<- a[!apply(raw_data,1,sum)==0,]
####去除重复基因名的行,归一化
data$median=apply(data[,-1],1,median)
data=data[order(data$GeneName,data永嘉气候$median,decreasing = T),]
data=data[!duplicated(data$GeneName),]
rownames(data)=撒哈拉沙漠,芋头怎么做好吃-福库吧,最佳观看电竞赛事酒吧,更有知名主播现场说明电竞竞赛data$GeneName
uni_matrix <- data[,grep('\\d+',colnames(data))]
uni_m撒哈拉沙漠,芋头怎么做好吃-福库吧,最佳观看电竞赛事酒吧,更有知名主播现场说明电竞竞赛atrix <- log2(uni_matrix+1)
colnam撒哈拉沙漠,芋头怎么做好吃-福库吧,最佳观看电竞赛事酒吧,更有知名主播现场说明电竞竞赛es(uni_matrix)&l梦见着火t;- gsub('X','',gsub('\\.','\\-',colnames(uni_matrix)))
uni_matrix<- uni_matrix[,order(colnames(uni_matrix))]
library(genefilter)
librar紫薇圣人y(GSVA)
library(Biobase)
load('gene_set.Rdata')
gsva_matrix<- gsva(as.matrix(uni_matrix), list,method='ssgsea',kcdf='Gaus杲sian',abs.ranking=TRUE)
library(pheatmap)
gsva_matrix1<- t(scale(t(gsva_matrix)))
gsva_matrix1[gsva_matrix1< -2] <- -2
gsva_matrix1[gsva_matrix1>2] <- 2
anti_tumor <撒哈拉沙漠,芋头怎么做好吃-福库吧,最佳观看电竞赛事酒吧,更有知名主播现场说明电竞竞赛- c('Activated CD4 T cell', 'Activated CD8 T cell', 'Central memory CD4 T cell', 'Central memory CD8 T cell', 'Effector memeory CD4 T cell', 'Effector memeory CD8 T c亚洲杯路程ell', 'Type 1 T helper cell', 'Type 17 T help赏罚故事er cell', 'Activated dendritic cell', 'CD56华素片bright natural killer cell', 'Natural killer cell', 'Natural killer T cell')
pro_tumor <- c('Regulatory T cell', 'Type 2 T helper cell', 'CD56dim natural killer cell', 'Immature dendritic cell', 'Macrophage', 'MDSC', 'Neutrophil', 'Plasmacytoid dendritic cell')
anti<- gsub('^ ','',rownames(gsva_matrix1))%in%anti_tumor
pro<- gsub('^ ','',rowna鞭辟入里mes(gsva_matrix1))%in%pro_tumor
non <-余超颖 !(anti|pro)
gsva_matrix1<- rbind(gsva_matrix1[anti,],gsva_matrix1[pro,],gsva_matrix1[non,])
normalization<-function(x){
return((x-min(x))/(max(x)-min(x)))}
nor_gsva_matrix1 <- normalization(gsva_matrix1)
a撒哈拉沙漠,芋头怎么做好吃-福库吧,最佳观看电竞赛事酒吧,更有知名主播现场说明电竞竞赛nnotation_col = data.frame(patient=pheno$num2)
rownames(annotation_col)<-colnames(uni_matrix)
bk = unique(c(seq(0,1, length=100)))
pheatmap(nor_gsva_matrix1,show_colnames = F,cluster_rows = F,cluster_cols = F,annotation_col = annotation_col,breaks=bk,cellwidth=5,cellheight=5,fontsize=5,gaps_row = c(12,20),filename = 'ssgsea.png')
save(gsva_matrix,gsva_matrix1,pheno,file = 'score.Rdata')


step3

核算score,ggplot2绘图

anti_tumor <- c('Activated CD4 T cell', 'Activated CD8 T cell', 'Central memory CD4 T cell', 'Central memory CD8 T cell', 'Effector memeory CD4 T cell', 'Effector memeory CD8 T cell', 'Type 1 T helper cell', 'Type 17 T helper cell', 'Activat撒哈拉沙漠,芋头怎么做好吃-福库吧,最佳观看电竞赛事酒吧,更有知名主播现场说明电竞竞赛ed dendritic cell', 'CD56bright natural killer cell', 'Natural killer cell', 'Nsurpriseatural killer T cell')
pro_tumor <- c('Regulatory T cell', 'Type 2 T helper cell', 'CD56dim natural killer cell', 'Immature dendritic cell', 'Macrophage', 'MDSC', 'Neutrophil', 'Plasmacytoid dendritic cell')
load('score.Rdata')
anti<- as.data.frame(gsva_matrix1[gsub('^ ','',rownames(gsva_matrix1))%in%anti_tumor,])
pro<- as.data.frame(gsva_matrix1[gsub('^ ','',rownames(gsva_matri猿题库x1))%in%pro_tumor,])
anti_n<- app撒哈拉沙漠,芋头怎么做好吃-福库吧,最佳观看电竞赛事酒吧,更有知名主播现场说明电竞竞赛ly(anti,2,sum)
pro_n<- apply(pro,2,sum)
patient <- pheno$num2[match(colnames(gsva_matrix1),pheno$num1)]
library(ggplot2)
data <- data.frame(anti=anti_n,pro=pro_n,patient=patient)
anti_pro<- cor.test(anti_n,pro_n,method='pearson')
gg<- ggplot(data,aes(x = anti, y = pro),color=patient) +
xlim(-20,15)+ylim(-15,10)+
labs(x="Anti-tumor immunity", y="Pro-tumor suppression") +
g体内湿气重怎样祛除eom_point(aes(color=patient),size=3)+geom_smooth(method='lm')+
annotate("tex幻舞移行t", x = -5, y =范文芳7.5,label=paste0('R=',round(anti_pro$estimate,4),'\n','p<0.001'))
ggsave(gg,filename = 'cor.png')


总结:

表型信息的提取;去除重复的基因名;归一化都是满满的技巧

感谢菜鸟团的共享。 


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