{"id":27278,"date":"2024-01-31T16:56:00","date_gmt":"2024-01-31T08:56:00","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27278"},"modified":"2024-01-31T16:56:01","modified_gmt":"2024-01-31T08:56:01","slug":"%e6%95%b2%e9%bb%91%e6%9d%bf%e4%ba%86%ef%bc%81%ef%bc%81%e5%b0%8f%e6%9e%9c%e6%95%99%e4%bd%a0%e9%80%9a%e8%bf%87ssgsea%e7%ae%97%e6%b3%95%e8%af%84%e4%bc%b0%e8%82%bf%e7%98%a4%e5%85%8d%e7%96%ab%e6%b5%b8","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27278","title":{"rendered":"\u6572\u9ed1\u677f\u4e86\uff01\uff01\u5c0f\u679c\u6559\u4f60\u901a\u8fc7ssGSEA\u7b97\u6cd5\u8bc4\u4f30\u80bf\u7624\u514d\u75ab\u6d78\u6da6\u7a0b\u5ea6\uff01\uff01\uff01"},"content":{"rendered":"

\u5c0f\u4f19\u4f34\u8fd8\u5728\u4e3a\u514d\u75ab\u7ec6\u80de\u6d78\u6da6\u5206\u6790\u800c\u56f0\u6270\u5417\uff0c\u662f\u5426\u4e0d\u77e5\u9053\u5982\u4f55\u53bb\u505a\u514d\u75ab\u6d78\u6da6\u5206\u6790\uff0c\u6709\u65f6\u5019\u9762\u5bf9\u5355\u6837\u672c\u7684\u57fa\u56e0\u96c6\u5374\u4e0d\u77e5\u9053\u5982\u4f55\u5206\u6790\uff0c\u4eca\u5929\u5c0f\u679c\u7ed9\u5c0f\u4f19\u4f34\u5e26\u6765\u4e00\u79cd\u5e38\u7528\u4e8e\u514d\u75ab\u7ec6\u80de\u6d78\u6da6\u5206\u6790\u7684\u65b9\u6cd5-ssGSEA\u4e5f\u5c31\u662f\u5355\u6837\u672cGSEA\u3002<\/p>\n

\u8fd9\u79cd\u65b9\u6cd5\u5462\u662f\u901a\u8fc7\u6bcf\u4e2a\u6837\u672c\u7684\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u4e0e\u7279\u5b9a\u7684\u57fa\u56e0\u96c6\u8fdb\u884c\u6bd4\u8f83\uff0c\u4e5f\u5c31\u662f\u514d\u75ab\u7ec6\u80de\u57fa\u56e0\u96c6\u3002\u53bb\u4f30\u8ba1\u8be5\u57fa\u56e0\u96c6\u5728\u8be5\u6837\u672c\u4e2d\u7684\u76f8\u5bf9\u5bcc\u96c6\u7a0b\u5ea6\u3002\u6240\u4ee5\u5462\u6211\u4eec\u5728\u514d\u75ab\u7ec6\u80de\u5206\u6790\u7684\u65f6\u5019\u53bb\u4f7f\u7528ssGSEA\u53bb\u4f30\u8ba1\u6bcf\u4e2a\u6837\u672c\u4e2d\u4e0d\u540c\u514d\u75ab\u7ec6\u80de\u7c7b\u578b\u7684\u76f8\u5bf9\u4e30\u5ea6\u3002\u90a3\u4e48\u63a5\u4e0b\u6765\u5c31\u8ddf\u7740\u5c0f\u679c\u53bb\u5b66\u4e60\u5427\uff1a<\/p>\n

\u6211\u4eec\u8fd9\u91cc\u4f7f\u7528R\u4ee3\u7801\u53bb\u5b9e\u73b0\uff0c\u7b80\u5355\u9ad8\u6548\uff01\uff01<\/p>\n

\u6211\u4eec\u9996\u5148\u8f7d\u5165\u6570\u636e<\/p>\n

\u8fd9\u91cc\u5462\u5c0f\u679c\u4f7f\u7528\u793a\u4f8b\u6570\u636eGSE112996,\u5c0f\u4f19\u4f34\u53ef\u4ee5\u4e0b\u8f7d\u81ea\u5df1\u7684GEO\u6570\u636e\u53bb\u5206\u6790\u3002<\/p>\n

rm(list=ls())<\/p>\n

\u8bfb\u5165\u6570\u636e\uff0c\u8fd9\u91cc\u5c0f\u679c\u51c6\u5907\u4e86\u4e24\u4e2a\u6587\u4ef6GSE112996_merged_fpkm_table.txt.gz\u548cGSE112996_series_matrix.txt’<\/p>\n

a <- read.table(‘GSE112996_merged_fpkm_table.txt.gz’,<\/p>\n

header = T,<\/p>\n

row.names=1)<\/p>\n

raw_data<- a[,-1]<\/p>\n

###\u8868\u578b\u4fe1\u606f\u63d0\u53d6<\/p>\n

pheno <- read.csv(file = ‘GSE112996_series_matrix.txt’)<\/p>\n

pheno <- data.frame(num1 = strsplit(as.character(pheno[42,]),split=’\\t’)[[1]][-1],<\/p>\n

num2 = gsub(‘patient: No.’,’P’,strsplit(as.character(pheno[51,]),split=’\\t’)[[1]][-1]))<\/p>\n

<\/p>\n

\u63a5\u4e0b\u91cc\u6211\u4eec\u5bf9\u6570\u636e\u8fdb\u884c\u5904\u7406\uff0c\u8fc7\u6ee4\u53bb\u9664\u91cd\u590d\u7684\u57fa\u56e0\u540d\u7b49<\/p>\n

{<\/p>\n

####\u6570\u636e\u8fc7\u6ee4<\/p>\n

data<- a[!apply(raw_data,1,sum)==0,]<\/p>\n

####\u53bb\u9664\u91cd\u590d\u57fa\u56e0\u540d\u7684\u884c\uff0c\u5f52\u4e00\u5316<\/p>\n

data$median=apply(data[,-1],1,median)<\/p>\n

data=data[order(data$GeneName,data$median,decreasing = T),]<\/p>\n

data=data[!duplicated(data$GeneName),]<\/p>\n

rownames(data)=data$GeneName<\/p>\n

uni_matrix <- data[,grep(‘\\\\d+’,colnames(data))]<\/p>\n

uni_matrix <- log2(uni_matrix+1)<\/p>\n

colnames(uni_matrix)<- gsub(‘X’,”,gsub(‘\\\\.’,’\\\\-‘,colnames(uni_matrix)))<\/p>\n

uni_matrix<- uni_matrix[,order(colnames(uni_matrix))]<\/p>\n

}<\/p>\n

\u5f97\u5230\u5982\u4e0b\u77e9\u9635<\/p>\n

<\/p>\n

save(uni_matrix,pheno,file = ‘uni_matrix.Rdata’)#\u4fdd\u5b58\u4e0b\u6765\uff0c\u540e\u7eed\u4f7f\u7528<\/p>\n

\u6211\u4eec\u5904\u7406\u597d\u4e4b\u540e\u5c31\u5f00\u59cb\u540e\u7eed\u7684\u5206\u6790\u5427<\/p>\n

\u8fd9\u4e2a\u9700\u8981\u52a0\u8f7d\u51e0\u4e2a\u5305<\/p>\n

rm(list=ls())<\/p>\n

##\u52a0\u8f7d\u5305<\/p>\n

{<\/p>\n

library(genefilter)<\/p>\n

library(GSVA)<\/p>\n

library(Biobase)<\/p>\n

library(stringr)<\/p>\n

}<\/p>\n

\u5c0f\u4f19\u4f34\u6ca1\u6709\u7684\u5305\u4e0d\u8981\u5fd8\u8bb0\u53bb\u4e0b\u8f7d\uff0c\u5982\u4f55\u4e0b\u8f7d\u4e0d\u4e86\u5c31\u7528#BiocManager::install(“genefilter”)<\/p>\n

\u7136\u540e\u6211\u4eec##\u8f7d\u5165\u6d4b\u8bd5\u6570\u636e\uff0c\u91cd\u70b9\u5c31\u662f\u4e0a\u8ff0\u56fe\u7247\u4e2d\u4e24\u7ec4\u6570\u636e<\/p>\n

load(‘uni_matrix.Rdata’)<\/p>\n

\u4e0b\u9762\u6211\u4eec\u8bfb\u53d6\u57fa\u56e0\u5217\u8868\uff0c\u5f97\u5230\u514d\u75ab\u7ec6\u80de\u5bf9\u4e8e\u7684\u7279\u5f02\u7684\u57fa\u56e0<\/p>\n

\u8fd9\u91cc\u5c0f\u679c\u7ed9\u5927\u5bb6\u5728\u5b98\u7f51\u4e0b\u6765\u597d\uff0c\u6709\u60f3\u8981\u7684\u5c0f\u4f19\u4f34\u53ef\u4ee5\u4e0b\u9762\u8fde\u63a5\u83b7\u53d6 mmc3.csv\u6587\u4ef6<\/p>\n

gene_set<- read.csv(‘mmc3.csv’,header = T)##\u8bfb\u53d6\u5df2\u7ecf\u4e0b\u8f7d\u597d\u7684\u514d\u75ab\u7ec6\u80de\u548c\u5bf9\u5e94\u57fa\u56e0\u5217\u8868\uff0c<\/p>\n

<\/p>\n

gene_set<-gene_set[, 1:2]#\u9009\u53d6\u7279\u5f02\u57fa\u56e0\u548c\u5bf9\u5e94\u7684\u514d\u75ab\u7ec6\u80de\u4e24\u884c<\/p>\n

head(gene_set)<\/p>\n

<\/p>\n

list<- split(as.matrix(gene_set)[,1], gene_set[,2])<\/p>\n

\u5f97\u5230\u6bcf\u79cd\u514d\u75ab\u7ec6\u80de\u5bf9\u4e8e\u7684\u57fa\u56e0<\/p>\n

\u63a5\u4e0b\u6765\u6211\u4eec\u53bb\u8bc4\u4f30EstimatesGSVAenrichment scores \u5f97\u5206\uff1a<\/p>\n

gsva_matrix<- gsva(as.matrix(uni_matrix), list,method=’ssgsea’,kcdf=’Gaussian’,abs.ranking=TRUE)<\/p>\n

\u5982\u4e0b\u56fe\uff1a<\/p>\n

<\/p>\n

\u6570\u636e\u5df2\u7ecf\u5206\u6790\u5b8c\u6210\uff0c\u6211\u4eec\u540e\u9762\u5c31\u5c06\u7ed3\u679c\u53ef\u89c6\u5316\uff0c\u901a\u8fc7\u5bcc\u96c6\u5206\u6570\u7684\u753b\u70ed\u56fe\uff01<\/p>\n

library(pheatmap)<\/p>\n

gsva_matrix1<- t(scale(t(gsva_matrix)))#\u5f52\u4e00\u5316<\/p>\n

gsva_matrix1[gsva_matrix1< -2] <- -2<\/p>\n

gsva_matrix1[gsva_matrix1>2] <- 2<\/p>\n

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’, ‘Activated dendritic cell’, ‘CD56bright natural killer cell’, ‘Natural killer cell’, ‘Natural killer T cell’)<\/p>\n

pro_tumor <- c(‘Regulatory T cell’, ‘Type 2 T helper cell’, ‘CD56dim natural killer cell’, ‘Immature dendritic cell’, ‘Macrophage’, ‘MDSC’, ‘Neutrophil’, ‘Plasmacytoid dendritic cell’)<\/p>\n

anti<- gsub(‘^ ‘,”,rownames(gsva_matrix1))%in%anti_tumor<\/p>\n

pro<- gsub(‘^ ‘,”,rownames(gsva_matrix1))%in%pro_tumor<\/p>\n

non <- !(anti|pro)##\u8bbe\u5b9a\u4e09\u79cd\u57fa\u56e0<\/p>\n

gsva_matrix1<- rbind(gsva_matrix1[anti,],gsva_matrix1[pro,],gsva_matrix1[non,])#\u518d\u7ed3\u5408\u8d77\u6765\uff0c\u4f7f\u56fe\u5206\u6210\u4e09\u6bb5<\/p>\n

normalization<-function(x){<\/p>\n

return((x-min(x))\/(max(x)-min(x)))}#\u8bbe\u5b9anormalization\u51fd\u6570<\/p>\n

nor_gsva_matrix1 <- normalization(gsva_matrix1)<\/p>\n

annotation_col = data.frame(patient=pheno$num2)#\u52a0\u4e0a\u75c5\u4eba\u7f16\u53f7<\/p>\n

rownames(annotation_col)<-colnames(uni_matrix)#\u4f7f\u7f16\u53f7\u80fd\u4e92\u76f8\u5bf9\u5e94<\/p>\n

bk = unique(c(seq(0,1, length=100)))#\u8bbe\u5b9a\u70ed\u56fe\u53c2\u6570<\/p>\n

pheatmap(nor_gsva_matrix1,<\/p>\n

show_colnames = F,<\/p>\n

cluster_rows = F,cluster_cols = F,<\/p>\n

annotation_col = annotation_col,<\/p>\n

breaks=bk,cellwidth=5,cellheight=5,<\/p>\n

fontsize=5,gaps_row = c(12,20),<\/p>\n

filename = ‘ssgsea.pdf’,width = 8)#\u753b\u70ed\u56fe<\/p>\n

save(gsva_matrix,gsva_matrix1,pheno,file = ‘score.Rdata’)#\u4e0d\u8981\u5fd8\u8bb0\u5c06\u7ed3\u679c\u4fdd\u5b58<\/p>\n

\u6700\u7ec8\u6211\u4eec\u5f97\u5230\u5982\u4e0a\u7684\u70ed\u56fe\u7ed8\u5236<\/p>\n

\u5f53\u7136\uff0c\u5bf9\u4e8e\u514d\u75ab\u7ec6\u80de\u7684\u7a0b\u5ea6\u5206\u6790\u80af\u5b9a\u5c11\u4e0d\u4e86\u76f8\u5173\u6027\u5206\u6790\u56fe\uff0c\u6211\u4eec\u4e0b\u9762\u901a\u8fc7ggplot2\u53bb\u7ed8\u5236\u4e00\u4e0b\uff1a<\/p>\n

rm(list=ls())<\/p>\n

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’, ‘Activated dendritic cell’, ‘CD56bright natural killer cell’, ‘Natural killer cell’, ‘Natural killer T cell’)<\/p>\n

pro_tumor <- c(‘Regulatory T cell’, ‘Type 2 T helper cell’, ‘CD56dim natural killer cell’, ‘Immature dendritic cell’, ‘Macrophage’, ‘MDSC’, ‘Neutrophil’, ‘Plasmacytoid dendritic cell’)<\/p>\n

load(‘score.Rdata’)<\/p>\n

anti<- as.data.frame(gsva_matrix1[gsub(‘^ ‘,”,rownames(gsva_matrix1))%in%anti_tumor,])<\/p>\n

pro<- as.data.frame(gsva_matrix1[gsub(‘^ ‘,”,rownames(gsva_matrix1))%in%pro_tumor,])<\/p>\n

anti_n<- apply(anti,2,sum)<\/p>\n

pro_n<- apply(pro,2,sum)<\/p>\n

patient <- pheno$num2[match(colnames(gsva_matrix1),pheno$num1)]<\/p>\n

library(ggplot2)<\/p>\n

data <- data.frame(anti=anti_n,pro=pro_n,patient=patient)<\/p>\n

anti_pro<- cor.test(anti_n,pro_n,method=’pearson’)<\/p>\n

gg<- ggplot(data,aes(x = anti, y = pro),color=patient) +<\/p>\n

xlim(-20,15)+ylim(-15,10)+<\/p>\n

labs(x=”Anti-tumor immunity”, y=”Pro-tumor suppression”) +<\/p>\n

geom_point(aes(color=patient),size=3)+geom_smooth(method=’lm’)+<\/p>\n

annotate(“text”, x = -5, y =7.5,label=paste0(‘R=’,round(anti_pro$estimate,4),’\\n’,’p<0.001′))<\/p>\n

ggsave(gg,filename = ‘cor.pdf’, height = 6, width = 8)<\/p>\n

\u7ed8\u5236\u56fe\u7684\u65b9\u5f0f\u539f\u7406\u548c\u4e0a\u9762\u5dee\u4e0d\u591a\uff0c<\/p>\n

\u770b\u4e00\u4e0b\u7ed3\u679c\u628a<\/p>\n

<\/p>\n

\u8fd9\u6837\u4e00\u6765\u6211\u4eec\u5c31\u5927\u529f\u544a\u6210\u4e86\uff0c\u5c0f\u4f19\u4f34\u6709\u6ca1\u6709\u5fc3\u52a8\u5462\uff0c\u5feb\u53bb\u8bd5\u8bd5\u5427<\/p>\n

\u4e0d\u8981\u5fd8\u8bb0\u591a\u591a\u7406\u89e3\u4ee3\u7801\u7684\u610f\u4e49\u8fd9\u6837\u624d\u80fd\u7ed8\u5236\u51fa\u81ea\u5df1\u60f3\u8981\u7684\u56fe\u7247\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"

\u5c0f\u4f19\u4f34\u8fd8\u5728\u4e3a\u514d\u75ab\u7ec6\u80de\u6d78\u6da6\u5206\u6790\u800c\u56f0\u6270\u5417\uff0c\u662f\u5426\u4e0d\u77e5\u9053\u5982\u4f55\u53bb\u505a\u514d\u75ab\u6d78\u6da6\u5206\u6790\uff0c\u6709\u65f6\u5019\u9762\u5bf9\u5355\u6837\u672c\u7684\u57fa\u56e0\u96c6\u5374\u4e0d\u77e5\u9053\u5982\u4f55\u5206\u6790 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/27278"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=27278"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/27278\/revisions"}],"predecessor-version":[{"id":27287,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/27278\/revisions\/27287"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=27278"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=27278"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=27278"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}