{"id":15919,"date":"2023-10-19T22:07:43","date_gmt":"2023-10-19T14:07:43","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=15919"},"modified":"2023-10-19T22:07:44","modified_gmt":"2023-10-19T14:07:44","slug":"sci%e9%83%bd%e5%9c%a8%e4%bd%bf%e7%94%a8%e7%9a%84%e5%8d%95%e5%9f%ba%e5%9b%a0%e5%85%8d%e7%96%ab%e6%b5%b8%e6%b6%a6%e8%bf%98%e4%b8%8d%e4%bc%9a%e5%90%97%ef%bc%9f%e5%b0%8f%e8%8a%b1%e6%89%8b%e6%8a%8a","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=15919","title":{"rendered":"SCI\u90fd\u5728\u4f7f\u7528\u7684\u5355\u57fa\u56e0\u514d\u75ab\u6d78\u6da6\u8fd8\u4e0d\u4f1a\u5417\uff1f\u5c0f\u82b1\u624b\u628a\u624b\u6559\u4f60\u5b66\u4f1a\uff01\uff08\u4e8c\uff09"},"content":{"rendered":"

\u5c0f\u82b1\u5728\u4e0a\u7bc7\u6587\u7ae0\u8bf4\u660e\u4e86\u5355\u57fa\u56e0\u7684\u514d\u75ab\u6d78\u6da6\uff0c\u5c31\u662f\u5bf9\u5206\u6790\u67d0\u4e2a\u57fa\u56e0\u4e0e\u514d\u75ab\u7ec6\u80de\u7684\u76f8\u5173\u6027\uff0c\u8fd9\u79cd\u5206\u6790\u65b9\u6cd5\u867d\u7136\u4e0d\u5e38\u89c1\uff0c\u4f46\u662f\u4e5f\u662f\u5728\u9ad8\u5206\u6587\u7ae0\u4e2d\u7ecf\u5e38\u89c1\u5230\u4e00\u5c0f\u90e8\u5206\uff0c\u5176\u5b9e\u8fd8\u6709\u4e00\u79cd\u65b9\u5f0f\uff0c\u5c0f\u4f19\u4f34\u7684\u57fa\u56e0\u592a\u591a\u4e0d\u77e5\u9053\u5982\u4f55\u653e\uff0c\u6216\u8005\u60f3\u628a\u57fa\u56e0\u5f53\u4f5c\u4e00\u4e2a\u6574\u4f53\u8be5\u5982\u4f55\u5206\u6790\u5462\uff1f\u5c0f\u82b1\u8fd9\u91cc\u6709\u4e00\u79cd\u65b9\u6cd5\uff0c\u5c31\u662f\u628a\u6211\u4eec\u57fa\u56e0\u805a\u7c7b\u3002\u4e0d\u540c\u7684\u7ec4\u8fdb\u884c\u5206\u6790\uff0c\u6bcf\u4e2a\u7ec4\u5305\u62ec\u76f8\u5e94\u7684\u57fa\u56e0\u3002\u5c0f\u4f19\u4f34\u662f\u4e0d\u662f\u5f88\u65b0\u5947\uff0c\u6765\u770b\u770b\u5982\u4f55\u53bb\u7ed8\u5236\u5427\uff01<\/p>\n

\u539f\u7406\u5176\u5b9e\u5f88\u7b80\u5355\uff0c\u5c0f\u82b1\u4e4b\u95f4\u6559\u8fc7\u5927\u5bb6\u4e00\u81f4\u6027\u805a\u7c7b\uff0c\u90a3\u4e48\u6211\u4eec\u5c31\u7528\u4e00\u81f4\u6027\u805a\u7c7b\u7ed3\u679c\u53bb\u5206\u6790\uff0c<\/p>\n

\u6211\u4eec\u5148\u628a\u57fa\u56e0\u805a\u7c7b\uff0c\u8fd9\u91cc\u6211\u4eec\u9700\u8981\u7528\u5230\u4e00\u4e9b\u5305\uff0c\u6ca1\u6709\u7684\u5c0f\u4f19\u4f34\u53bb\u4e0b\u8f7d\u4e00\u4e0b<\/p>\n

#install.packages(“BiocManager”)<\/p>\n

#BiocManager::install(‘ConsensusClusterPlus’)<\/p>\n

library(tidyverse)<\/p>\n

library(ConsensusClusterPlus)<\/p>\n

\u8fd9\u91cc\u793a\u4f8b\u6570\u636e\u8fd8\u662f\u548c\u4e0a\u4e00\u7bc7\u4e00\u6837\u6765\u770b\u770b\uff1a<\/p>\n

exp <- read.table(“CESC_fpkm_mRNA_01A.txt”,sep = “\\t”,row.names = 1,check.names = F,stringsAsFactors = F,header = T)<\/p>\n

<\/p>\n

\u4e0b\u9762\u6211\u4eec\u53bb\u9009\u62e9\u8981\u805a\u7c7b\u7684\u57fa\u56e0\uff0c\u5e76\u4e14\u505a\u4e00\u4e9b\u5904\u7406<\/p>\n

d=as.matrix(exp)<\/p>\n

gene <- c(“MMP9″,”CCND1″,”STAT1″,”AR”,”AKR1C3″,”GSTP1″,”PGR”) #\u8fd9\u91cc\u5c0f\u82b1\u968f\u673a\u51e0\u4e2a\u57fa\u56e0\u53bb\u5c55\u793a<\/p>\n

d <- d[gene,]<\/p>\n

mads=apply(d,1,mad)<\/p>\n

d=d[rev(order(mads))[1:7],] #\u6ce8\u610f\u4e0b\u8fd9\u91cc\u76847\u662f\u57fa\u56e0\u4e2a\u6570<\/p>\n

d = sweep(d,1, apply(d,1,median,na.rm=T))<\/p>\n

title=(“JULEI”) ##\u6587\u4ef6\u5939\u8f93\u51fa\u56fe\u7247\u7684\u4f4d\u7f6e\uff0c\u540d\u5b57\u5c0f\u4f19\u4f34\u53ef\u4ee5\u6309\u7167\u81ea\u5df1\u60f3\u6cd5\u53bb\u5199<\/p>\n

set.seed(1) #\u5c0f\u82b1\u53d1\u73b0\u8bbe\u4e0d\u8bbe\u7f6e\u79cd\u5b50\u90fd\u4e00\u6837<\/p>\n

results = ConsensusClusterPlus(d,maxK=9,reps=50,pItem=0.8,pFeature=1,<\/p>\n

title=title,clusterAlg=”hc”,distance=”pearson”,seed=1,plot=”pdf”)<\/p>\n

results[[2]][[“consensusMatrix”]][1:5,1:5]<\/p>\n

results[[2]][[“consensusTree”]]<\/p>\n

results[[2]][[“consensusClass”]][1:5]<\/p>\n

icl = calcICL(results,title=title,plot=”pdf”) ##\u753b\u53e6\u4e00\u7ec4\u56fe\u7247<\/p>\n

group<-results[[2]][[“consensusClass”]]<\/p>\n

group<-as.data.frame(group)<\/p>\n

group$group <- factor(group$group,levels=c(1,2))<\/p>\n

save(group,file = “group_AY.Rda”)#\u6211\u4eec\u4fdd\u5b58\u4e00\u4e0b\u805a\u7c7b\u7684\u5206\u7ec4\u4fe1\u606f\uff0c\u4e0b\u6b21\u5c31\u662f\u76f4\u63a5\u6253\u5f00\u4f7f\u7528\u4e86<\/p>\n

\u5148\u770b\u770b\u7ed3\u679c<\/p>\n

<\/p>\n

\u518d\u770b\u4e00\u4e0b\u53ef\u89c6\u5316\u7684\u7ed3\u679c\u56fe<\/p>\n

<\/p>\n

\u5c0f\u82b1\u8fd9\u91cc\u9009\u62e9K=2\u7684\u60c5\u51b5\u53bb\u5c55\u793a\uff0c<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

\u805a\u7c7b\u7ed3\u679c\u7684\u89e3\u8bfb\uff0c\u5c0f\u82b1\u8fd9\u91cc\u4e0d\u591a\u8d58\u8ff0\u4e86\uff0c\u5c0f\u4f19\u4f34\u53ef\u4ee5\u770b\u770b\u5f80\u671f\u5c0f\u82b1\u4ecb\u7ecd\u7684\u3002<\/p>\n

\u8fd9\u91cc\u5c0f\u82b1\u8bbe\u7f6e\u4e00\u4e0b\u914d\u8272<\/p>\n

\u540e\u7eed\u4e3a\u4e86\u597d\u770b\uff0c\u54c8\u54c8<\/p>\n

#\u914d\u8272<\/p>\n

# \u5b89\u88c5\u5e76\u52a0\u8f7dcolorspace\u5305<\/p>\n

#install.packages(“colorspace”)<\/p>\n

library(colorspace)<\/p>\n

# choosing HCL-based color palettes<\/p>\n

# \u67e5\u770b\u6240\u6709\u7684\u989c\u8272\u753b\u677f<\/p>\n

hcl_palettes(plot = TRUE)<\/p>\n

\u7136\u540e\u6211\u4eec\u5c31\u5bf9\u805a\u7c7b\u5206\u7ec4\u8fdb\u884c\u70ed\u56fe\u7684\u53ef\u89c6\u5316\uff0c\u6bd5\u7adf\u6211\u4eec\u8981\u5c55\u793a\u5206\u7ec4\u7684\u4fe1\u606f\u7684\uff1a<\/p>\n

# \u7ed8\u5236ConsensusClusterPlus\u540e\u7684\u70ed\u56fe<\/p>\n

library(pheatmap)<\/p>\n

group <- group %>%<\/p>\n

rownames_to_column(“sample”)<\/p>\n

annotation <- group %>% arrange(group) %>% column_to_rownames(“sample”)<\/p>\n

a <- group %>% arrange(group) %>% mutate(sample=substring(.$sample,1,12))<\/p>\n

b <- t(exp_gene) %>%<\/p>\n

as.data.frame() %>%<\/p>\n

rownames_to_column(“sample”) %>%<\/p>\n

mutate(sample=substring(.$sample,1,12))<\/p>\n

c <- inner_join(a,b,”sample”) %>% .[,-2] %>% column_to_rownames(“sample”) %>% t(.)<\/p>\n

pheatmap(c,annotation = annotation,<\/p>\n

cluster_cols = F,fontsize=5,fontsize_row=5,<\/p>\n

scale=”row”,show_colnames=F,<\/p>\n

fontsize_col=3)<\/p>\n

bk <- c(seq(-5,-0.1,by=0.01),seq(0,5,by=0.01))<\/p>\n

pheatmap(c,annotation = annotation,<\/p>\n

annotation_colors = list(group = c(“1″ =”#8B1C62”,”2″= “#EE9572″)),<\/p>\n

cluster_cols = F,fontsize=10,fontsize_row=10,<\/p>\n

scale=”row”,show_colnames=F,cluster_row = F,<\/p>\n

color = c(colorRampPalette(colors = c(“#551A8B”,”white”))(length(bk)\/2),colorRampPalette(colors = c(“white”,”#CD3700″))(length(bk)\/2)),<\/p>\n

#legend_breaks=seq(-3,3,1),<\/p>\n

fontsize_col=3)<\/p>\n

dev.off()<\/p>\n

<\/p>\n

\u8fd9\u6837\u770b\u6765\uff0c\u805a\u7c7b\u4e00\u7ec4\u7684\u9ad8\u8868\u8fbe\u57fa\u56e0\u5728\u805a\u7c7b\u4e8c\u7ec4\u662f\u4f4e\u8868\u8fbe\u3002\u90a3\u4e48\u6211\u4eec\u5c31\u8be5\u5bf9\u7740\u4e24\u7ec4\u8fdb\u884c\u514d\u75ab\u5206\u6790\u4e86\uff0c\u63a5\u4e0b\u6765\u6211\u4eec\u8fdb\u5165\u6b63\u9898\uff0c<\/p>\n

#cluster\u5206\u7ec4CIBERSORT<\/p>\n

setwd(“”)#\u53ef\u4ee5\u8bbe\u7f6e\u4e00\u4e0b\u5206\u7ec4<\/p>\n

library(tidyverse)<\/p>\n

\u6211\u4eec\u8fd9\u91cc\u7528\u7684\u514d\u75ab\u6d78\u6da6\u7684\u7ed3\u679c\u662f\u4e0a\u671f\u5c0f\u82b1\u4ecb\u7ecd\u8fc7\u7684\uff0c\u5c0f\u4f19\u4f34\u6ca1\u6709\u7684\u53ef\u4ee5\u53bb\u770b\u4e0a\u671f\u6587\u7ae0\uff0c\u300aSCI\u90fd\u5728\u4f7f\u7528\u7684\u5355\u57fa\u56e0\u514d\u75ab\u6d78\u6da6\u8fd8\u4e0d\u4f1a\u5417\uff1f\u5c0f\u82b1\u624b\u628a\u624b\u6559\u4f60\u5b66\u4f1a\uff01\uff08\u4e00\uff09\u300b<\/p>\n

a <- read.table(“CIBERSORT-Results.txt”, sep = “\\t”,row.names = 1,check.names = F,header = T)<\/p>\n

<\/p>\n

a <- a[,1:22]<\/p>\n

identical(rownames(a),rownames(group))<\/p>\n

b <- group<\/p>\n

class(b$group)<\/p>\n

a$group <- b$group<\/p>\n

a <- a %>% rownames_to_column(“sample”)<\/p>\n

library(ggsci)<\/p>\n

library(tidyr)<\/p>\n

library(ggpubr)<\/p>\n

#install.packages(“ggsci”)<\/p>\n

#install.packages(“tidyr”)<\/p>\n

#install.packages(“ggpubr”)<\/p>\n

b <- gather(a,key=CIBERSORT,value = Proportion,-c(group,sample))<\/p>\n

ggboxplot(b, x = “CIBERSORT”, y = “Proportion”,<\/p>\n

fill = “group”, palette = “lancet”)+<\/p>\n

stat_compare_means(aes(group = group),<\/p>\n

method = “wilcox.test”,<\/p>\n

label = “p.signif”,<\/p>\n

symnum.args=list(cutpoints = c(0, 0.001, 0.01, 0.05, 1),<\/p>\n

symbols = c(“***”, “**”, “*”, “ns”)))+<\/p>\n

theme(text = element_text(size=10),<\/p>\n

axis.text.x = element_text(angle=45, hjust=1))<\/p>\n

dev.off()<\/p>\n

<\/p>\n

\u6211\u4eec\u7528\u5c0f\u63d0\u7434\u56fe\u5c55\u793a\u4e00\u4e0b\u4fe1\u606f\uff0c\u8fd9\u4e2a\u56fe\u5c31\u662f\u5c55\u793a\u4e86\u4e0d\u540c\u7ec4\u5728\u4e0d\u540c\u514d\u75ab\u7ec6\u80de\u4e2d\u7684\u5360\u6bd4\uff0c\u4e5f\u5c31\u662f\u6b63\u5e38\u7684\u514d\u75ab\u6d78\u6da6\u5206\u6790\uff0c<\/p>\n

\u4e0b\u9762\u6211\u4eec\u4ecb\u7ecd\u6700\u540e\u4e00\u79cd\u514d\u75ab\u6d78\u6da6\u7684\u65b9\u5f0f\uff0c\u805a\u7c7b\u7684ssgsea\uff0c\u4e4b\u524d\u5c0f\u82b1\u4e13\u95e8\u4ecb\u7ecd\u8fc7\u4e00\u671fssGSEA\uff0c\u5355\u6837\u672c\u7684\u514d\u75ab\u6d78\u6da6\uff0c\u8fd9\u6b21\u6211\u4eec\u4f7f\u7528\u805a\u7c7b\u540e\u7684\u7ed3\u679c\u53bb\u7ed8\u5236\uff0c\u6765\u770b\u770b\u5982\u4f55\u7ed8\u5236\u7684<\/p>\n

ssGSEA<\/p>\n

setwd(“ssGSEA_AY”)<\/p>\n

#BiocManager::install(‘GSVA’)<\/p>\n

library(tidyverse)<\/p>\n

library(data.table)<\/p>\n

#library(GSVA)<\/p>\n

#BiocManager::install(“GSVA”)<\/p>\n

#BiocManager::install(“DelayedArray”)<\/p>\n

#library(DelayedArray)<\/p>\n

library(GSVA)<\/p>\n

#\u51c6\u5907\u7ec6\u80demarker<\/p>\n

cellMarker <- data.table::fread(“cellMarker.csv”,data.table = F)<\/p>\n

colnames(cellMarker)[2] <- “celltype”<\/p>\n

#\u5c06cellMarker\u6587\u4ef6\u5217\u540d\u7684\u7b2c2\u4e2a\u4fee\u6539\u4e3acelltype<\/p>\n

type <- split(cellMarker,cellMarker$celltype)<\/p>\n

#\u5c06cellMarker\u6587\u4ef6\u4ee5celltype\u4e3a\u5206\u7ec4\u62c6\u5206\u6210list\u6570\u636e\u683c\u5f0f<\/p>\n

#\u5904\u7406data.tables\u5217\u8868\u901a\u5e38\u6bd4\u4f7f\u7528group by\u53c2\u6570\u6309\u7ec4\u5bf9\u5355\u4e2adata.table\u8fdb\u884c\u64cd\u4f5c\u8981\u6162\u5f97\u591a<\/p>\n

cellMarker <- lapply(type, function(x){<\/p>\n

dd = x$Metagene<\/p>\n

unique(dd)<\/p>\n

})<\/p>\n

#\u5c06list\u4e2d\u6bcf\u4e2acelltype\u4e2d\u7684\u57fa\u56e0\u8fdb\u884c\u5408\u5e76<\/p>\n

save(cellMarker,file = “cellMarker_ssGSEA.Rdata”)#\u4fdd\u5b58\u4e2d\u95f4\u6587\u4ef6<\/p>\n

#load(“immune_infiltration\/\/cellMarker_ssGSEA.Rdata”)<\/p>\n

##\u8868\u8fbe\u91cf\u77e9\u9635\u7684\u51c6\u5907<\/p>\n

###\u884c\u662f\u57fa\u56e0\uff0c\u5217\u662f\u6837\u672c<\/p>\n

expr <- data.table::fread(“CESC_fpkm_mRNA_01A.txt”,data.table = F) #\u8bfb\u53d6\u8868\u8fbe\u6587\u4ef6<\/p>\n

rownames(expr) <- expr[,1] #\u5c06\u7b2c\u4e00\u5217\u4f5c\u4e3a\u884c\u540d<\/p>\n

expr <- expr[,-1] #\u53bb\u9664\u7b2c\u4e00\u5217<\/p>\n

expr <- as.matrix(expr) #\u5c06expr\u8f6c\u6362\u4e3a\u77e9\u9635\u683c\u5f0f<\/p>\n

# \u4e0b\u9762\u5c31\u662f\u4f7f\u7528ssGSEA\u91cf\u5316\u514d\u75ab\u6d78\u6da6<\/p>\n

gsva_data <- gsva(expr,cellMarker, method = “ssgsea”)<\/p>\n

a <- gsva_data %>% t() %>% as.data.frame()<\/p>\n

identical(rownames(a),rownames(group))<\/p>\n

a$group <- group$group<\/p>\n

a <- a %>% rownames_to_column(“sample”)<\/p>\n

write.table(a,”ssGSEA.txt”,sep = “\\t”,row.names = T,col.names = NA,quote = F)<\/p>\n

library(ggsci)<\/p>\n

library(tidyr)<\/p>\n

library(ggpubr)<\/p>\n

b <- gather(a,key=ssGSEA,value = Expression,-c(group,sample))<\/p>\n

ggboxplot(b, x = “ssGSEA”, y = “Expression”,<\/p>\n

fill = “group”, palette = “lancet”)+<\/p>\n

stat_compare_means(aes(group = group),<\/p>\n

method = “wilcox.test”,<\/p>\n

label = “p.signif”,<\/p>\n

symnum.args=list(cutpoints = c(0, 0.001, 0.01, 0.05, 1),<\/p>\n

symbols = c(“***”, “**”, “*”, “ns”)))+<\/p>\n

theme(text = element_text(size=10),<\/p>\n

axis.text.x = element_text(angle=45, hjust=1))<\/p>\n

dev.off() <\/p>\n

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