{"id":28061,"date":"2024-02-04T18:04:56","date_gmt":"2024-02-04T10:04:56","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28061"},"modified":"2024-02-04T18:04:59","modified_gmt":"2024-02-04T10:04:59","slug":"%e5%8d%95%e4%b8%aa%e6%95%b0%e6%8d%ae%e5%ba%93%e7%94%a8%e8%85%bb%e4%ba%86%ef%bc%9f%e5%a4%9a%e6%95%b0%e6%8d%ae%e5%ba%93%e7%bb%84%e5%90%88%e6%8b%b3%e5%b8%a6%e4%bd%a0%e6%89%93%e5%bc%80","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28061","title":{"rendered":"\u5355\u4e2a\u6570\u636e\u5e93\u7528\u817b\u4e86\uff1f\u591a\u6570\u636e\u5e93\u201c\u7ec4\u5408\u62f3\u201d\u5e26\u4f60\u6253\u5f00\u514d\u75ab\u6d78\u6da6\u65b0\u601d\u8def\uff01"},"content":{"rendered":"

\u6570\u636e\u5e93\u662f\u751f\u4fe1\u5206\u6790\u7684\u5f3a\u5927\u6570\u636e\u57fa\u7840\uff0c\u5f80\u671f\u7684\u6587\u7ae0\u4e2d\u5c0f\u679c\u7ed9\u5c0f\u4f19\u4f34\u4eec\u4ecb\u7ecd\u4e86\u8bb8\u591a\u7684\u6570\u636e\u5e93\u4f7f\u7528\u7684\u65b9\u6cd5\uff0c\u90a3\u4e48\u6709\u6ca1\u6709\u4e00\u79cd\u65b9\u6cd5\u53ef\u4ee5\u540c\u65f6\u5206\u6790\u591a\u4e2a\u6570\u636e\u5e93\u7684\u6570\u636e\u5462\uff1f\u563f\u563f\uff0c\u5c0f\u679c\u8fde\u591c\u5b66\u4e60\u4e86\u53ef\u4ee5\u540c\u65f6\u5bf9\u591a\u4e2a\u6570\u636e\u5e93\u7684\u80bf\u7624\u6570\u636e\u8fdb\u884c\u514d\u75ab\u6d78\u6da6\u7684\u5206\u6790\u65b9\u6cd5\uff0c\u5982\u679c\u5c0f\u4f19\u4f34\u4eec\u611f\u5174\u8da3\uff0c\u5c31\u8ddf\u7740\u5c0f\u679c\u4e00\u8d77\u6765\u5b66\u4e60\u5427\uff01\u8003\u8651\u5230\u672c\u6b21\u590d\u73b0\u7684\u6570\u636e\u91cf\u6bd4\u8f83\u5927\uff0c\u5c0f\u679c\u63a8\u8350\u5927\u5bb6\u79df\u8d41\u6211\u4eec\u7684\u670d\u52a1\u5668\u8fdb\u884c\u672c\u6b21\u7684\u590d\u73b0\u5b66\u4e60\uff5e<\/p>\n

\u672c\u6b21\u6211\u4eec\u4f7f\u7528\u7684\u662f\u4e0b\u9762\u8fd9\u51e0\u4e2a\u6570\u636e\u5e93\uff1a<\/p>\n

TARGET\uff1ahttps:\/\/ocg.cancer.gov\/programs\/target<\/a><\/p>\n

CBTN\uff1ahttps:\/\/cbtn.org\/research\/specimendata\/<\/a><\/p>\n

ICGC \uff1ahttps:\/\/icgc.org\/icgc\/<\/p>\n

TCGA\uff1ahttps:\/\/www.cancer.gov\/about-nci\/organization<\/p>\n

\u5728\u8fd9\u51e0\u4e2a\u6570\u636e\u5e93\u4e2d\uff0c\u6211\u4eec\u4eca\u5929\u4e3b\u8981\u4f7f\u7528\u5176\u4e2d\u7684\u513f\u7ae5\u60a3\u8005\u6570\u636e\u4e3a\u5927\u5bb6\u8fdb\u884c\u6559\u7a0b\uff0c\u5c0f\u4f19\u4f34\u4eec\u5982\u679c\u5bf9\u5176\u4ed6\u7684\u80bf\u7624\u6570\u636e\u611f\u5174\u8da3\uff0c\u4e5f\u53ef\u4ee5\u81ea\u5df1\u8bd5\u4e00\u8bd5\u5176\u4ed6\u7684\u6570\u636e\u5466\uff0c\u6211\u4eec\u5728\u6570\u636e\u5e93\u4e2d\u83b7\u53d6\u513f\u79d1\u795e\u7ecf\u7cfb\u7edf\u80bf\u7624(pedNST)\u7684RNA-seq\u6570\u636e\uff0c\u5c0f\u679c\u5728\u8fd9\u91cc\u5df2\u7ecf\u5e2e\u5927\u5bb6\u6574\u7406\u597d\u4e86\uff0c\u5927\u5bb6\u4ece\u4e0b\u9762\u7684\u94fe\u63a5\u5c31\u53ef\u4ee5\u83b7\u53d6\uff1a<\/p>\n

\u94fe\u63a5\uff1ahttps:\/\/pan.baidu.com\/s\/135jKVqVbFB8CuAvDAGmgFw<\/p>\n

\u63d0\u53d6\u7801\uff1a9if2<\/p>\n

<\/p>\n

\u5982\u56fe\uff0c\u662f\u6211\u4eec\u672c\u6b21\u8981\u7528\u5230\u7684\u4e3b\u8981\u6570\u636e\uff08estimate_ped_pdx.RData\uff09\uff0c\u5176\u4e2d\u5305\u62ec\u4e86\u513f\u7ae5\u7684\u4e00\u4e9b\u80bf\u7624\u6570\u636e\uff0c\u4f8b\u5982StromalScore\uff0c\u662f\u4e00\u79cd\u57fa\u4e8e\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u9884\u6d4b\u80bf\u7624\u7ec4\u7ec7\u4e2d\u57fa\u8d28\u7ec6\u80de\u6d78\u6da6\u7a0b\u5ea6\u7684\u5206\u503c\uff0c\u6570\u503c\u8d8a\u9ad8\uff0c\u9884\u540e\u6d3b\u6027\u8d8a\u5dee\uff0c\u5e76\u4e14\u514d\u75ab\u6d3b\u6027\u8d8a\u4f4e\uff0c\u8fd8\u6709\u514d\u75ab\u5f97\u5206\uff0cESTIMATE\u514d\u75ab\u8bc4\u5206\u7b49\u3002<\/p>\n

\u5728\u5206\u6790\u4e4b\u524d\uff0c\u5c0f\u679c\u5148\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e0b\u4eca\u5929\u7684\u5206\u6790\u601d\u8def\uff0c\u6211\u4eec\u5148\u5bfc\u5165\u4e0b\u8f7d\u7684\u6570\u636e\uff0c\u7136\u540e\u4f9d\u6b21\u5bf9\u6570\u636e\u8fdb\u884c\u603b\u4f53\u7684\u6982\u89c8\uff0c\u5229\u7528\u4e00\u4e9b\u8bc4\u5206\u6807\u51c6\u8fdb\u884c\u8bc4\u5206\uff0c\u5212\u5206\u514d\u75ab\u96c6\u7fa4\uff0c\u6700\u540e\u8fdb\u884c\u4e00\u4e9b\u76f8\u5173\u6027\u5206\u6790\uff0c\u8fd9\u4e2a\u601d\u8def\u540c\u6837\u53ef\u4ee5\u5e94\u7528\u5230\u5176\u4ed6\u7684\u514d\u75ab\u5206\u6790\u4e2d~<\/p>\n

Step 1 \u5bf9\u5bfc\u5165\u7684\u56db\u4e2a\u6570\u636e\u5e93\u7684\u6837\u672c\u91cf\u548c\u7814\u7a76\u7684\u80bf\u7624\u7c7b\u578b\u8fdb\u884c\u6982\u89c8<\/p>\n

#\u5bfc\u5165\u6574\u5408\u597d\u7684\u6570\u636e\u5e93\u6570\u636e<\/p>\n

load(paste0(datapath,”estimate_ped_pdx.RData”))<\/p>\n

#\u53ea\u4fdd\u7559ped\uff08\u513f\u79d1\uff09\u6570\u636e<\/p>\n

ped <- estimate_ped_pdx[ estimate_ped_pdx$group != “TCGA”,]<\/p>\n

tab <- as.data.frame(table(ped$cohort), stringsAsFactors = F)<\/p>\n

tab <- tab[order(tab$Freq, decreasing = T),]<\/p>\n

#\u6392\u5e8f<\/p>\n

ped$cohort <- factor(ped$cohort, levels = tab$Var1)<\/p>\n

ped$group <- factor(ped$group, levels = c(“CBTN”, “ICGC”, “TARGET”, “PDX (ITCC)”))<\/p>\n

fig1a <- ggplot(data = ped) + geom_bar(aes(y = cohort, fill = group)) + myaxis + myplot +<\/p>\n

theme(axis.title = element_blank(), axis.text.x = element_text(size = 25, angle = 0, hjust = 0.5),<\/p>\n

legend.position = c(0.8,0.9), legend.title = element_blank(), plot.margin = margin(1,1,1,1, “cm”)) +<\/p>\n

scale_fill_manual(values = group_col)<\/p>\n

pdf(file = paste0(plotpath,”Fig1_A.pdf”),<\/p>\n

width = 10, height = 10, useDingbats = FALSE)<\/p>\n

fig1a<\/p>\n

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

<\/p>\n

\u5982\u56fe\u662f\u6982\u89c8\u7684\u7ed3\u679c\uff0c\u56fe\u4e2d\u663e\u793a\u4e86\u4e0d\u540c\u80bf\u7624\u7c7b\u578b\uff08\u5982ETMR\u3001NFB\u7b49\uff09\u5728CBTN\u3001ICGC\u548cTARGET\u4e09\u4e2a\u6570\u636e\u5e93\u4e2d\u7684\u6570\u91cf\u3002\u5176\u4e2d\uff0cPDX:\u60a3\u8005\u6765\u6e90\u7684\u5f02\u79cd\u79fb\u690d\u6a21\u578b\uff0cETMR:\u5e26\u6709\u591a\u5c42\u82b1\u7c07\u7684\u80da\u80ce\u6027\u80bf\u7624\uff0cNFB:\u795e\u7ecf\u7ea4\u7ef4\u7624\uff0cMNG:\u8111\u819c\u7624\uff0cSCHW:\u795e\u7ecf\u9798\u7624\uff0cCP:\u8109\u7edc\u4e1b\u80bf\u7624\uff0cCPH:\u9885\u54bd\u7ba1\u7624\uff0cATRT:\u975e\u5178\u578b\u7578\u80ce\u7624\/\u6a2a\u7eb9\u808c\u6837\u80bf\u7624\uff0cEPN:\u5ba4\u7ba1\u819c\u7624\uff0cpedHGG:\u513f\u79d1\u9ad8\u7ea7\u522b\u80f6\u8d28\u7624\uff0cNBL:\u795e\u7ecf\u6bcd\u7ec6\u80de\u7624\uff0cMB:\u9ad3\u6bcd\u7ec6\u80de\u7624\uff0cpedLGG:\u513f\u79d1\u4f4e\u7ea7\u522b\u80f6\u8d28\u7624\u3002\u8bf4\u660e\u5176\u4e2d\u7684\u80bf\u7624\u7c7b\u578b\u8fd8\u662f\u975e\u5e38\u4e30\u5bcc\u7684\u3002<\/p>\n

Step 2 \u5bf9\u6240\u6709\u7684\u80bf\u7624\u6570\u636e\u8fdb\u884cESTIMATE\u514d\u75ab\u8bc4\u5206\u5206\u6790<\/p>\n

#\u8bbe\u7f6e\u6570\u7ec4<\/p>\n

emptyvar <- as.data.frame(matrix(ncol = 7, nrow = 2))<\/p>\n

colnames(emptyvar) <- colnames(estimate_ped_pdx)<\/p>\n

emptyvar$group <- as.character(emptyvar$group)<\/p>\n

emptyvar$cohort <- as.character(emptyvar$cohort)<\/p>\n

emptyvar[1,\u201daliquot_id\u201d] <- \u201cempty1\u201d<\/p>\n

emptyvar[2,\u201daliquot_id\u201d] <- \u201cempty2\u201d<\/p>\n

emptyvar[1,\u201dsample_id\u201d] <- \u201cempty1\u201d<\/p>\n

emptyvar[2,\u201dsample_id\u201d] <- \u201cempty2\u201d<\/p>\n

emptyvar[1,\u201dImmuneScore\u201d] <- 3500<\/p>\n

emptyvar[2,\u201dImmuneScore\u201d] <- 3500<\/p>\n

emptyvar[1,\u201dcohort\u201d] <- \u201cEMPTY1\u201d<\/p>\n

emptyvar[2,\u201dcohort\u201d] <- \u201cEMPTY2\u201d<\/p>\n

estimate_ped_pdx <- rbind(estimate_ped_pdx,emptyvar)<\/p>\n

#\u8ba1\u7b97ESTIMATE\u514d\u75ab\u8bc4\u5206<\/p>\n

estimate_ped_pdx$percread <- 8.0947988*exp(estimate_ped_pdx$ImmuneScore*0.0006267)<\/p>\n

immune.cohorts <- cbind(NA, unique(estimate_ped_pdx$cohort))<\/p>\n

colnames(immune.cohorts) <- c(“group”,”cohort”)<\/p>\n

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

immune.cohorts$group <- as.character(immune.cohorts$group)<\/p>\n

adults <- c(“PRAD”, “LGG”, “OV”, “SKCM”, “COAD”, “GBM”, “LUAD”)<\/p>\n

peds <- c(“PDX”,”ETMR”, “MB”, “ATRT”, “EPN”, “pedHGG”, “CP”, “NBL”, “pedLGG”, “CPH”, “MNG”, “SCHW”, “NFB”)<\/p>\n

immune.cohorts[immune.cohorts$cohort %in% adults, 1] <- “Adult”<\/p>\n

immune.cohorts[immune.cohorts$cohort %in% peds, 1] <- “Pediatric”<\/p>\n

immune.cohorts[immune.cohorts$cohort == “EMPTY1”,1] <- “Pediatric”<\/p>\n

immune.cohorts[immune.cohorts$cohort == “EMPTY2”,1] <- “Pediatric”<\/p>\n

#\u83b7\u53d6\u6bcf\u4e00\u7ec4\u7684\u836f\u7269\u6570\u636e<\/p>\n

for(i in 1:nrow(immune.cohorts)){<\/p>\n

immune.cohorts$median_immunereads[i]<-median(estimate_ped_pdx$percread[estimate_ped_pdx$cohort == immune.cohorts$cohort[i]])<\/p>\n

}<\/p>\n

#\u5bf9\u80bf\u7624\u7684\u7c7b\u578b\u8fdb\u884c\u6392\u5e8f<\/p>\n

tmp <- immune.cohorts[which(immune.cohorts$group == “Pediatric”),]<\/p>\n

tmp1 <- immune.cohorts[which(immune.cohorts$group == “Adult”),]<\/p>\n

immune.cohorts <- rbind(tmp,tmp1)<\/p>\n

immune.cohorts$cohort <- factor(immune.cohorts$cohort, levels = c(“PDX”,”ETMR”, “MB”, “ATRT”, “EPN”, “pedHGG”, “CP”,<\/p>\n

“NBL”, “pedLGG”, “CPH”, “MNG”, “SCHW”, “NFB”,<\/p>\n

“EMPTY1″,”EMPTY2”, “PRAD”, “LGG”, “OV”, “SKCM”,<\/p>\n

“COAD”, “GBM”, “LUAD”))<\/p>\n

immune.cohorts <- immune.cohorts[order(immune.cohorts$cohort),]<\/p>\n

# \u8c03\u7528Splot \u5bf9\u6570\u636e\u8fdb\u884c\u5904\u7406\uff0c\u51c6\u5907\u7ed8\u56fe<\/p>\n

disease.width <- (nrow(estimate_ped_pdx)\/nrow(immune.cohorts))<\/p>\n

sorted.estimate_ped_pdx <- estimate_ped_pdx[0,]<\/p>\n

start = 0<\/p>\n

for(i in 1:(nrow(immune.cohorts))){<\/p>\n

tmp <- estimate_ped_pdx[estimate_ped_pdx$cohort == immune.cohorts$cohort[i],]<\/p>\n

tmp <- tmp[order(tmp$percread),]<\/p>\n

#create range of x values to squeeze dots into equal widths of the plot for each Disease regardless of the number of samples<\/p>\n

div <- disease.width\/nrow(tmp)<\/p>\n

#If there is only one sample, put the dot in the middle of the alloted space<\/p>\n

if(dim(tmp)[1]==1)<\/p>\n

{<\/p>\n

tmp$Xpos<-start+(disease.width\/2)<\/p>\n

} else tmp$Xpos<-seq(from = start, to = start+disease.width, by = div)[-1]<\/p>\n

sorted.estimate_ped_pdx<-rbind(sorted.estimate_ped_pdx,tmp)<\/p>\n

immune.cohorts$Median.start[i] <- tmp$Xpos[1]<\/p>\n

immune.cohorts$Median.stop[i] <- tmp$Xpos[nrow(tmp)]<\/p>\n

immune.cohorts$N[i]<-nrow(tmp)<\/p>\n

start <- start+disease.width+30<\/p>\n

}<\/p>\n

immune.cohorts$medianloc <- immune.cohorts$Median.start+((immune.cohorts$Median.stop-immune.cohorts$Median.start)\/2)<\/p>\n

sorted.estimate_ped_pdx$cohort <- factor(sorted.estimate_ped_pdx$cohort, levels = levels(immune.cohorts$cohort))<\/p>\n

#\u989c\u8272\u8bbe\u7f6e<\/p>\n

rmEMPTY <- rep(“black”,22)<\/p>\n

rmEMPTY[14:15] <- “white”<\/p>\n

immune.cohorts$color_crossbar <- NA<\/p>\n

immune.cohorts$color_crossbar[immune.cohorts$cohort == “EMPTY1”] <- “white”<\/p>\n

immune.cohorts$color_crossbar[immune.cohorts$cohort == “EMPTY2”] <- “white”<\/p>\n

immune.cohorts$color_crossbar[is.na(immune.cohorts$color_crossbar)] <- “black”<\/p>\n

immune.cohorts$cohort_n <- paste0(immune.cohorts$cohort, ” (n=”, immune.cohorts$N, “)”)<\/p>\n

#\u7ed8\u5236\u56fe\u7247<\/p>\n

fig1b1.fx <- function(x){<\/p>\n

ggplot() +<\/p>\n

geom_point(data = sorted.estimate_ped_pdx, aes(x = Xpos ,y = percread, color = cohort),<\/p>\n

size = 7, shape = 20) +<\/p>\n

geom_crossbar(data = immune.cohorts,<\/p>\n

aes(x = medianloc,y = median_immunereads, color = color_crossbar,<\/p>\n

ymin = median_immunereads,ymax = median_immunereads), width = disease.width) +<\/p>\n

myaxis + myplot +<\/p>\n

theme(axis.title.x = element_blank(), axis.title.y = element_text(size = 25),<\/p>\n

axis.text.x = element_text(size = 25, angle = 45, hjust = 1, color = rmEMPTY),<\/p>\n

axis.text.y = element_text(size = 25)) +<\/p>\n

scale_color_manual(values = c(cohort_col, “white” = “white”, “black” = “black”), guide = “none”) +<\/p>\n

scale_x_continuous(breaks = seq((disease.width)\/2,max(sorted.estimate_ped_pdx$Xpos),<\/p>\n

disease.width+30), labels = immune.cohorts$cohort_n, expand = c(0,20)) +<\/p>\n

scale_y_continuous(breaks = seq(0, 70, by = 10)) +<\/p>\n

labs(y = “% Immune Reads”)<\/p>\n

}<\/p>\n

fig1b <- fig1b1.fx(sorted.estimate_ped_pdx)<\/p>\n

pdf(file = paste0(plotpath,”Fig1_B.pdf”),<\/p>\n

width = 20, height = 8, useDingbats = FALSE)<\/p>\n

fig1b<\/p>\n

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

<\/p>\n

\u5982\u56fe\uff0c\u662f\u57fa\u4e8eESTIMATE\u514d\u75ab\u8bc4\u5206\u7684\u513f\u7ae5\u548c\u6210\u4eba\u764c\u75c7\u7684\u767e\u5206\u6bd4\u514d\u75ab\u8bfb\u6570\u5206\u5e03\u3002ESTIMATE\uff0c\u4e5f\u5c31\u662fEstimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data\uff0c\u662f\u4e00\u79cd\u4f7f\u7528\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u9884\u6d4b\u80bf\u7624\u7eaf\u5ea6\u548c\u80bf\u7624\u7ec4\u7ec7\u4e2d\u6d78\u6da6\u6027\u57fa\u8d28\/\u514d\u75ab\u7ec6\u80de\u5b58\u5728\u7684\u5206\u6570\uff0c\u5305\u542b\u57fa\u8d28\u8bc4\u5206(\u7528\u4e8e\u6355\u6349\u80bf\u7624\u7ec4\u7ec7\u4e2d\u57fa\u8d28\u7684\u5b58\u5728) ,\u514d\u75ab\u8bc4\u5206(\u7528\u4e8e\u8868\u793a\u80bf\u7624\u7ec4\u7ec7\u4e2d\u514d\u75ab\u7ec6\u80de\u7684\u6d78\u6da6) \uff0cestimate\u8bc4\u5206(\u7528\u4e8e\u63a8\u65ad\u80bf\u7624\u7eaf\u5ea6)\u3002<\/p>\n

\u56fe\u4e2d\u7684PDX\u662f\u4f5c\u4e3a\u514d\u75ab\u6d78\u6da6\u7684\u9634\u6027\u5bf9\u7167\u800c\u8bbe\u7f6e\u7684\uff0c\u6bcf\u79cd\u989c\u8272\u4ee3\u8868\u4e00\u79cd\u7279\u5b9a\u7c7b\u578b\u7684\u764c\u75c7\uff0cy\u8f74\u8868\u793a%\u514d\u75ab\u8bfb\u6570\uff0cx\u8f74\u4e0a\u5217\u51fa\u4e86\u5404\u79cd\u764c\u75c7\u7c7b\u578b\u53ca\u5176\u6837\u672c\u6570\u91cf\u3002<\/p>\n

\u901a\u8fc7ESTIMATE\u514d\u75ab\u8bc4\u5206\u53ef\u4ee5\u6620\u4e86\u514d\u75ab\u7cfb\u7edf\u5bf9\u80bf\u7624\u7684\u53cd\u5e94\u5f3a\u5ea6\u3002\u4ece\u56fe\u4e2d\u53ef\u4ee5\u770b\u51fa\uff0c\u4e0d\u540c\u7c7b\u578b\u7684\u764c\u75c7\u5728\u514d\u75ab\u8bfb\u6570\u4e0a\u6709\u5f88\u5927\u7684\u5dee\u5f02\uff0c\u80be\u7ec6\u80de\u764c\u3001\u9ed1\u8272\u7d20\u7624\u548c\u5375\u5de2\u764c\u7b49\u764c\u75c7\u7684\u514d\u75ab\u8bfb\u6570\u4e5f\u8f83\u9ad8\u3002\u8fd9\u5bf9\u4e8e\u7814\u7a76\u764c\u75c7\u7684\u514d\u75ab\u6cbb\u7597\u548c\u514d\u75ab\u8010\u53d7\u662f\u6709\u91cd\u8981\u610f\u4e49\u7684\u3002<\/p>\n

Step 3 \u5bf9\u4e0d\u540c\u7684\u514d\u75ab\u96c6\u7fa4\u8fdb\u884c\u5212\u5206<\/p>\n

#\u52a0\u8f7d\u6570\u636e<\/p>\n

load(file = paste0(datapath,”metadata_IC.RData”))<\/p>\n

load(file = paste0(datapath, “geneset_cc_normalized.RData”))<\/p>\n

#\u57fa\u4e8e\u514d\u75ab\u7c07\u8fdb\u884c\u6392\u5e8f<\/p>\n

cluster_cohort <- metadata_IC[order(metadata_IC$immune_cluster, metadata_IC$cohort),]<\/p>\n

#\u63d0\u53d6\u514d\u75ab\u7c07\u4fe1\u606f<\/p>\n

mycluster <- as.character(cluster_cohort$immune_cluster)<\/p>\n

names(mycluster) <- rownames(cluster_cohort)<\/p>\n

cluster_hm <- class_hm.fx(mycluster)<\/p>\n

#\u63d0\u53d6\u80bf\u7624\u7c7b\u578b\u7684\u4fe1\u606f<\/p>\n

mycohort <- cluster_cohort$cohort<\/p>\n

names(mycohort) <- rownames(cluster_cohort)<\/p>\n

mycohorts <- t(as.matrix(mycohort))<\/p>\n

rownames(mycohorts) <- “Cohort”<\/p>\n

cohorts_hm <- cohorts_hm.fx(mycohorts)<\/p>\n

cells_mat <- geneset_cc_norm[,rownames(cluster_cohort)]<\/p>\n

# \u7ed8\u5236\u70ed\u56fe<\/p>\n

cells_hm <- cells_hm.fx(cells_mat)<\/p>\n

#\u8fdb\u884c\u6ce8\u91ca<\/p>\n

annotation_order <- c(“Pediatric Inflammed”, “Myeloid Predominant”, “Immune Neutral”, “Immune Desert”)<\/p>\n

cluster_ha = HeatmapAnnotation(clusters = anno_mark(at = c(50, 235, 566, 844), labels_rot = 0,<\/p>\n

labels = annotation_order, side = “top”,<\/p>\n

labels_gp = gpar(fontsize = 20),<\/p>\n

link_height = unit(0.5, “cm”)))<\/p>\n

fig1c <- cluster_ha %v% cluster_hm %v% cells_hm %v% cohorts_hm<\/p>\n

#\u8bbe\u7f6e\u53c2\u6570<\/p>\n

lgd_cohort = Legend(labels = names(cohort_col)[2:13], title = “”, nrow = 1, legend_gp = gpar(fill = cohort_col[2:13]))<\/p>\n

pdf(paste0(plotpath,”Fig1_C.pdf”),<\/p>\n

width = 18, height = 10)<\/p>\n

draw(fig1c, annotation_legend_side = “bottom”, legend_grouping = “original”,<\/p>\n

annotation_legend_list = list(lgd_cohort))<\/p>\n

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

<\/p>\n

\u5206\u6790\u7684\u7ed3\u679c\u5982\u56fe\u6240\u793a\uff0c\u56fe\u7247\u7684\u9876\u90e8\u6709\u56db\u4e2a\u7c7b\u522b\uff0c\u5206\u522b\u662fPediatric Inflamed\uff08\u708e\u75c7\u578b\uff09\uff0cMyeloid Predominant\uff08\u9ad3\u7cfb\u4e3b\u5bfc\u578b\uff09\uff0cImmune Neutral\uff08\u514d\u75ab\u4e2d\u6027\u578b\uff09\uff0c\u548cImmune Desert\uff08\u514d\u75ab\u6c99\u6f20\u578b\uff09\uff0c\u662f\u6839\u636e\u514d\u75ab\u7ec6\u80de\u7684\u603b\u4f53\u6c34\u5e73\u6216\u6bd4\u4f8b\u8fdb\u884c\u5212\u5206\u7684\u3002<\/p>\n

\u800c\u56fe\u7247\u7684\u53f3\u4fa7\u6709\u4e00\u4e2a\u56fe\u4f8b\uff0c\u663e\u793a\u4e86\u4e0d\u540c\u989c\u8272\u4ee3\u8868\u7684\u514d\u75ab\u7ec6\u80de\u7c7b\u578b\uff0c\u4f8b\u5982\u5355\u6838\u7ec6\u80de\uff0c\u7c92\u7ec6\u80de\uff0c\u6811\u7a81\u72b6\u7ec6\u80de\uff0cT\u7ec6\u80de\uff0cNK\u7ec6\u80de\uff0c\u548cB\u7ec6\u80de\u3002\u8fd9\u4e9b\u514d\u75ab\u7ec6\u80de\u90fd\u662f\u4eba\u4f53\u514d\u75ab\u7cfb\u7edf\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff0c\u53c2\u4e0e\u62b5\u6297\u611f\u67d3\u548c\u80bf\u7624\u7b49\u75c5\u7406\u8fc7\u7a0b\u3002<\/p>\n

\u5728\u56fe\u7247\u7684\u5e95\u90e8\u6709\u4e00\u4e9b\u5f69\u8272\u7684\u6761\u5f62\uff0c\u7528\u6765\u8868\u793a\u4e0d\u540c\u7684\u80bf\u7624\u7c7b\u578b\uff0c\u4e2d\u95f4\u90e8\u5206\u662f\u4e00\u4e2a\u77e9\u9635\uff0c\u6bcf\u4e2a\u5c0f\u65b9\u683c\u4e2d\u7684\u989c\u8272\u7528\u6765\u8868\u793a\u67d0\u4e00\u79cd\u514d\u75ab\u7ec6\u80de\u5728\u67d0\u4e00\u7c7b\u522b\u6216\u961f\u5217\u4e2d\u7684\u4e30\u5ea6\uff0c\u989c\u8272\u8d8a\u6df1\u8868\u793a\u4e30\u5ea6\u8d8a\u9ad8\uff0c\u989c\u8272\u8d8a\u6d45\u8868\u793a\u4e30\u5ea6\u8d8a\u4f4e\u3002\u4ece\u77e9\u9635\u4e2d\u53ef\u4ee5\u770b\u51fa\uff0c\u4e0d\u540c\u7c7b\u522b\u6216\u961f\u5217\u4e4b\u95f4\u7684\u514d\u75ab\u7ec6\u80de\u5206\u5e03\u548c\u4e30\u5ea6\u6709\u5f88\u5927\u7684\u5dee\u5f02\uff0c\u4f8b\u5982\uff0cPediatric Inflamed\u7c7b\u522b\u4e2d\u7684NK\u7ec6\u80de\u548c\u6811\u7a81\u72b6\u7ec6\u80de\u4e30\u5ea6\u8f83\u9ad8\uff0c\u800cImmune Desert\u7c7b\u522b\u4e2d\u7684\u514d\u75ab\u7ec6\u80de\u4e30\u5ea6\u666e\u904d\u8f83\u4f4e\uff0c\u8fd9\u4e9b\u5dee\u5f02\u53ef\u80fd\u53cd\u6620\u4e86\u4e0d\u540c\u6761\u4ef6\u4e0b\u7684\u514d\u75ab\u72b6\u6001\u548c\u529f\u80fd\u3002<\/p>\n

Step 4 \u764c\u75c7\u4e0e\u7279\u5b9a\u514d\u75ab\u96c6\u7fa4\u7684\u76f8\u5173\u6027\u5206\u6790<\/p>\n

#\u5bfc\u5165\u6570\u636e<\/p>\n

load(file = paste0(datapath,”metadata_IC.RData”))<\/p>\n

tab <- as.data.frame(table(metadata_IC$cohort), stringsAsFactors = F)<\/p>\n

tab <- tab[order(tab$Freq, decreasing = F),]<\/p>\n

# \u5c06\u80bf\u7624\u7c07\u548c\u80bf\u7624\u7c7b\u578b\u6c47\u805a\u4e3a\u77e9\u9635<\/p>\n

cancer_IC_mat <- matrix(nrow = 12, ncol = 4,<\/p>\n

dimnames = list(tab$Var1,<\/p>\n

c(“Pediatric Inflamed”, “Myeloid Predominant”, “Immune Neutral”, “Immune Desert”)))<\/p>\n

for(i in 1:nrow(cancer_IC_mat)){<\/p>\n

mycancer <- metadata_IC[ metadata_IC$cohort == rownames(cancer_IC_mat)[i],]<\/p>\n

freq_tab <- as.data.frame(table(mycancer$immune_cluster), stringsAsFactors = F)<\/p>\n

freq_tab$perc <- freq_tab$Freq\/sum(freq_tab$Freq)<\/p>\n

cancer_IC_mat[i, freq_tab$Var1] <- freq_tab$perc *100<\/p>\n

}<\/p>\n

#\u5904\u7406NA\u6570\u636e<\/p>\n

cancer_IC_mat[is.na(cancer_IC_mat)] <- 0<\/p>\n

# \u5b9a\u4e49\u7c07<\/p>\n

row_dend <- as.dendrogram(hclust(dist(cancer_IC_mat), “complete”))<\/p>\n

row_dend <- dendextend::rotate(row_dend,<\/p>\n

c(“NFB”, “SCHW”, “MNG”, “CPH”, “ATRT”, “NBL”, “pedLGG”, “ETMR”, “MB”, “CP”, “pedHGG”, “EPN”))<\/p>\n

# \u6ce8\u91ca<\/p>\n

ha = rowAnnotation(`cohort size` = anno_barplot(tab$Freq, bar_width = 1,<\/p>\n

gp = gpar(col = “white”, fill = “#4d4d4d”),<\/p>\n

border = FALSE,<\/p>\n

axis_param = list(at = c(0, 100, 200, 300), labels_rot = 45, gp = gpar(fontsize = 20)),<\/p>\n

width = unit(3, “cm”)),<\/p>\n

show_annotation_name = FALSE)<\/p>\n

ha_1 = HeatmapAnnotation(`immune size` = anno_barplot( as.matrix(table(metadata_IC$immune_cluster)), bar_width = 1,<\/p>\n

gp = gpar(col = “white”, fill = “#4d4d4d”),<\/p>\n

border = FALSE,<\/p>\n

axis_param = list(at = c(0, 100,200,300), labels_rot = 45, gp = gpar(fontsize = 20)),<\/p>\n

height = unit(3, “cm”)),<\/p>\n

show_annotation_name = FALSE)<\/p>\n

# \u7ed8\u56fe<\/p>\n

col_fun= colorRamp2(c(0, 100), c(“white”, “red”))<\/p>\n

cancer_hm = Heatmap(cancer_IC_mat,<\/p>\n

#titles and names<\/p>\n

name = “% cancer”,<\/p>\n

show_row_names = TRUE,<\/p>\n

show_column_names = TRUE,<\/p>\n

#clusters and orders<\/p>\n

cluster_columns = FALSE,<\/p>\n

cluster_rows = row_dend,<\/p>\n

show_row_dend = TRUE,<\/p>\n

#aesthestics<\/p>\n

row_names_side = “left”,<\/p>\n

col = col_fun,<\/p>\n

column_names_rot = 45,<\/p>\n

column_names_gp = gpar(fontsize = 10),<\/p>\n

row_names_gp = gpar(fontsize = 10),<\/p>\n

height = unit(nrow(cancer_IC_mat), “cm”),<\/p>\n

width = unit(ncol(cancer_IC_mat), “cm”),<\/p>\n

column_title_gp = gpar(fontsize = 10),<\/p>\n

column_title = NULL,<\/p>\n

row_title = NULL,<\/p>\n

right_annotation = ha,<\/p>\n

top_annotation = ha_1,<\/p>\n

show_heatmap_legend = TRUE)<\/p>\n

pdf(paste0(plotpath,”Fig1_D.pdf”),<\/p>\n

width = 10, height = 10)<\/p>\n

draw(cancer_hm)<\/p>\n

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

<\/p>\n

\u7ed3\u679c\u5982\u56fe\uff0c\u663e\u793a\u4e86\u4e0d\u540c\u7c7b\u578b\u7684\u764c\u75c7\u4e0e\u7279\u5b9a\u514d\u75ab\u96c6\u7fa4\u7684\u8868\u8fbe\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u56fe\u4e2d\u7eb5\u8f74\u5217\u51fa\u4e86\u4e0d\u540c\u7c7b\u578b\u7684\u80bf\u7624\uff08NFB, SCHW, JMNG \u7b49\uff09\uff0c\u6a2a\u8f74\u5217\u51fa\u4e86\u514d\u75ab\u96c6\u7fa4\uff0c\u540c\u65f6\uff0c\u5728\u7eb5\u8f74\u4e0a\u8fd8\u6709\u4e00\u4e2a\u6e10\u53d8\u8272\u6761\uff0c\u8868\u793a0\u5230100%\u7684\u764c\u75c7\u53d1\u751f\u7387\u3002\uff08\u764c\u75c7\u53d1\u751f\u7387\u662f\u6307\u67d0\u4e00\u57fa\u56e0\u5728\u67d0\u4e00\u7c7b\u522b\u4e2d\u7684\u6837\u672c\u4e2d\u8868\u8fbe\u5f02\u5e38\u7684\u6bd4\u4f8b\uff0c\u53cd\u6620\u4e86\u8be5\u80bf\u7624\u4e0e\u514d\u75ab\u96c6\u7fa4\u7684\u76f8\u5173\u6027\uff09\u3002<\/p>\n

\u6bcf\u4e2a\u5355\u5143\u683c\u7684\u989c\u8272\u4ee3\u8868\u8be5\u80bf\u7624\u5728\u7279\u5b9a\u96c6\u7fa4\u4e2d\u7684\u764c\u75c7\u53d1\u751f\u7387\uff1b\u989c\u8272\u8d8a\u6df1\uff0c\u767e\u5206\u6bd4\u8d8a\u9ad8\uff0c\u8868\u793a\u8be5\u57fa\u56e0\u4e0e\u8be5\u7c7b\u522b\u66f4\u52a0\u76f8\u5173\uff1b\u989c\u8272\u8d8a\u6d45\uff0c\u767e\u5206\u6bd4\u8d8a\u4f4e\uff0c\u8868\u793a\u8be5\u57fa\u56e0\u4e0e\u8be5\u7c7b\u522b\u4e0d\u592a\u76f8\u5173\u3002\u4f8b\u5982\uff0cNFB\u57fa\u56e0\u5728ImmuDesert\u4e2d\u7684\u764c\u75c7\u53d1\u751f\u7387\u4e3a0%\uff0c\u800cEPN\u5728Immune Neutral\u7c7b\u522b\u4e2d\u7684\u764c\u75c7\u53d1\u751f\u7387\u4e3a100%\uff0c\u8bf4\u660eEPN\u4e0eImmune Neutral\u7c7b\u522b\u6709\u5f88\u5f3a\u7684\u76f8\u5173\u6027\uff0c\u53ef\u80fd\u662fEPN\u7684\u81f4\u75c5\u56e0\u7d20\u6216\u8005\u8bca\u65ad\u6307\u6807\u3002<\/p>\n

Step 5 \u5206\u6790\u514d\u75ab\u7c7b\u578b\u4e0eCRI-iAtlas\u7c07\uff08\u514d\u75ab\u96c6\u7fa4\uff09\u7684\u5173\u7cfb<\/p>\n

#\u5bfc\u5165\u6570\u636e<\/p>\n

load(file = paste0(datapath,”metadata_IC.RData”))<\/p>\n

tab <- as.data.frame(table(metadata_IC$CRI_cluster), stringsAsFactors = F)<\/p>\n

tab <- tab[order(tab$Freq, decreasing = F),]<\/p>\n

#\u5b58\u50a8\u6bcf\u4e2a\u514d\u75ab\u96c6\u7fa4\u548c\u514d\u75ab\u7c07\u7684\u767e\u5206\u6bd4<\/p>\n

cri_IC_mat <- matrix(nrow = 6, ncol = 4,<\/p>\n

dimnames = list(tab$Var1,c(“Pediatric Inflamed”, “Myeloid Predominant”,<\/p>\n

“Immune Neutral”, “Immune Desert”)))<\/p>\n

for(i in 1:nrow(cri_IC_mat)){<\/p>\n

mycancer <- metadata_IC[ metadata_IC$CRI_cluster == rownames(cri_IC_mat)[i],]<\/p>\n

freq_tab <- as.data.frame(table(mycancer$immune_cluster), stringsAsFactors = F)<\/p>\n

freq_tab$perc <- freq_tab$Freq\/sum(freq_tab$Freq)<\/p>\n

cri_IC_mat[i, freq_tab$Var1] <- freq_tab$perc *100<\/p>\n

}<\/p>\n

cri_IC_mat[is.na(cri_IC_mat)] <- 0<\/p>\n

col_fun= colorRamp2(c(0, 100), c(“white”, “red”))<\/p>\n

#\u8bbe\u7f6e\u70ed\u56fe\u53c2\u6570<\/p>\n

cri_hm = Heatmap(cri_IC_mat,<\/p>\n

#titles and names<\/p>\n

name = “% CRI-iAtlas cluster”,<\/p>\n

show_row_names = TRUE,<\/p>\n

show_column_names = TRUE,<\/p>\n

#clusters and orders<\/p>\n

cluster_columns = FALSE,<\/p>\n

cluster_rows = FALSE,<\/p>\n

show_column_dend = TRUE,<\/p>\n

#aesthestics<\/p>\n

row_names_side = “left”,<\/p>\n

col = col_fun,<\/p>\n

column_names_rot = 45,<\/p>\n

column_names_gp = gpar(fontsize = 15),<\/p>\n

row_names_gp = gpar(fontsize = 15),<\/p>\n

height = unit(nrow(cri_IC_mat), “cm”),<\/p>\n

width = unit(ncol(cri_IC_mat), “cm”),<\/p>\n

column_title_gp = gpar(fontsize = 15),<\/p>\n

column_title = NULL,<\/p>\n

row_title = NULL,<\/p>\n

show_heatmap_legend = TRUE)<\/p>\n

ha = rowAnnotation(<\/p>\n

`cohort size` = anno_barplot(tab$Freq, bar_width = 1,<\/p>\n

gp = gpar(col = “white”, fill = “#4d4d4d”),<\/p>\n

border = FALSE,<\/p>\n

axis_param = list(gp = gpar(fontsize=15), at = c(0, 100,250,500), labels_rot = 45),<\/p>\n

width = unit(4, “cm”)),<\/p>\n

show_annotation_name = FALSE)<\/p>\n

pdf(paste0(plotpath, “Fig1_E.pdf”), width = 10, height = 10)<\/p>\n

cri_hm + ha<\/p>\n

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

<\/p>\n

\u5206\u6790\u7684\u7ed3\u679c\u5982\u56fe\uff0c\u5de6\u8fb9\u7684\u70ed\u56fe\u663e\u793a\u4e86pedNST\u4e2d\u6bcf\u4e2aCRI-iAtlas\u7c07\uff08\u514d\u75ab\u96c6\u7fa4\uff09\u4e2d\u8de8\u8d8a\u514d\u75ab\u7c07\u7684\u6837\u672c\u6bd4\u4f8b\uff0c\u800c\u67f1\u72b6\u56fe\u663e\u793a\u4e86\u6bcf\u4e2aCRI-iAtlas\u7c07\u805a\u7c7b\u7684\u6837\u672c\u603b\u6570\uff0c\u4ece\u800c\u5c55\u793a\u4e86\u4e0d\u540c\u514d\u75ab\u53cd\u5e94\u7c7b\u578b\u4e0eCRI-iAtlas\u7c07\u7684\u6570\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n

\u4f8b\u5982\uff1aY\u8f74\u5217\u51fa\u4e86\u4e03\u79cd\u4e0d\u540c\u7684\u514d\u75ab\u53cd\u5e94\u7c7b\u578b\uff1aTGFbeta dominant\uff08TGF-\u03b2\u5360\u4f18\u52bf\uff09, IFN-gamma dominant\uff08IFN-\u03b3\u5360\u4f18\u52bf\uff09, Wound Healing\uff08\u521b\u4f24\u6108\u5408\uff09, Immunologically quiet\uff08\u514d\u75ab\u9759\u9ed8\uff09, Inflammatory\uff08\u708e\u75c7\uff09, Lymphocyte depleted\uff08\u6dcb\u5df4\u7ec6\u80de\u8017\u7aed\uff09\u548cMixed\uff08\u6df7\u5408\u578b\uff09\uff0c\u8868\u4e2d\u7528\u7ea2\u8272\u3001\u767d\u8272\u6765\u8868\u793a\u6bcf\u79cd\u514d\u75ab\u53cd\u5e94\u7c7b\u578b\u5bf9\u5e94\u7684CRI-iAtlas\u7c07\u7684\u6570\u91cf\u3002\u7ea2\u8272\u4ee3\u8868\u8f83\u9ad8\u6570\u91cf\uff0c\u767d\u8272\u4ee3\u8868\u8f83\u4f4e\u6570\u91cf\u3002<\/p>\n

Step 6 \u7528H&E TIL\u5206\u6570\u5206\u6790\u514d\u75ab\u7cfb\u7edf\u5bf9\u80bf\u7624\u7684\u53cd\u5e94\u5f3a\u5ea6<\/p>\n

#\u5bfc\u5165\u6570\u636e\uff08HE\u8bc4\u5206\uff09<\/p>\n

load(file = file.path(datapath,”HE_manifest.RData”))<\/p>\n

#\u7ed8\u56fe<\/p>\n

heplot <- ggplot(data = HE_manifest,<\/p>\n

aes(x = immune_cluster, y = agg_tilScore)) +<\/p>\n

geom_beeswarm(cex = 1.5, aes(color = cohort), size = 5) +<\/p>\n

geom_boxplot(width = 0.5, outlier.colour = NA, fill = NA) +<\/p>\n

myaxis + myplot +<\/p>\n

scale_color_manual(values = cohort_col) +<\/p>\n

theme(legend.position = “none”,<\/p>\n

plot.margin = unit(c(0.2,0.2,0.2,2), “cm”),<\/p>\n

axis.title.x = element_blank(),<\/p>\n

axis.title.y = element_text(size = 30),<\/p>\n

axis.text.x = element_text(size = 30),<\/p>\n

axis.text.y = element_text(size = 30),<\/p>\n

plot.title = element_text(size = 30, hjust = 0.5)) +<\/p>\n

geom_signif(comparisons = list(c(“Pediatric Inflamed”, “Myeloid Predominant”)), y_position = 0.4,<\/p>\n

map_signif_level=TRUE, textsize = 10, test = “wilcox.test”, vjust = 0.5) +<\/p>\n

geom_signif(comparisons = list(c(“Pediatric Inflamed”, “Immune Desert”)), y_position = 0.45,<\/p>\n

map_signif_level=TRUE, textsize = 10, test = “wilcox.test”, vjust = 0.5) +<\/p>\n

labs(y = “Average TIL score”) + ggtitle(~underline(“H&E TIL score (n = 355)”))<\/p>\n

pdf(paste0(plotpath,”Fig1_F.pdf”),<\/p>\n

width = 10, height = 12)<\/p>\n

print(heplot)<\/p>\n

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

<\/p>\n

\u7ed3\u679c\u5982\u56fe\uff0c\u6211\u4eec\u5728\u8fd9\u4e00\u6b65\u4e2d\u7ed8\u5236\u7684\u70ed\u56fe\u662f\u4e00\u4e2a\u663e\u793aH&E TIL\u5206\u6570\u548c\u514d\u75ab\u96c6\u7fa4\u5173\u7cfb\u7684\u6563\u70b9\u56fe\u3002<\/p>\n

\u5bf9\u4e8eH&E TIL\u5206\u6570\uff0c\u8fd9\u662f\u4e00\u79cd\u7528\u6765\u8bc4\u4f30\u80bf\u7624\u6d78\u6da6\u6027\u6dcb\u5df4\u7ec6\u80de\uff08TIL\uff09\u7684\u65b9\u6cd5\uff0c\u57fa\u4e8eH&E\u67d3\u8272\u7684\u80bf\u7624\u5207\u7247\uff0c\u7528\u4e8e\u53cd\u6620\u4e86\u514d\u75ab\u7cfb\u7edf\u5bf9\u80bf\u7624\u7684\u53cd\u5e94\u5f3a\u5ea6\u3002<\/p>\n

\u56fe\u4e2d\u4e0d\u540c\u989c\u8272\u548c\u5f62\u72b6\u7684\u70b9\u8868\u793a\u4e0d\u540c\u75be\u75c5\u6216\u6837\u672c\u6765\u6e90\uff0c\u4f8b\u5982CBTN\uff0cICGC\uff0cTCGA\u3002\u6bcf\u4e2a\u96c6\u7fa4\u4e2d\u70b9\u7684\u4f4d\u7f6e\u8868\u793a\u6bcf\u4e2a\u6837\u672c\u7684H&E TIL\u5206\u6570\uff0c\u8d8a\u9760\u8fd1y\u8f74\u8868\u793a\u5206\u6570\u8d8a\u9ad8\uff0c\u8d8a\u9760\u8fd1x\u8f74\u8868\u793a\u5206\u6570\u8d8a\u4f4e\u3002\u8fd9\u91cc\u7528\u7bb1\u5f62\u56fe\u6765\u663e\u793aTIL\u8bc4\u5206\uff0c\u901a\u8fc7\u5bf9\u6574\u4e2a\u513f\u79d1\u4e2d\u67a2\u795e\u7ecf\u7cfb\u7edf\u80bf\u7624\u6837\u672c(CBTN)\u7684\u75c5\u7406\u56fe\u50cf\u8fdb\u884c\u5206\u5272\u5206\u6790\u800c\u786e\u5b9a\u3002\u6240\u793a\u7684\u6837\u672c\u56fe\u50cf\u5206\u522b\u4ee3\u88681%(\u4e0b)\u300110%(\u4e2d)\u548c15%(\u4e0a)\u7684TIL\u5206\u6570\uff0c\u5206\u522b\u5bf9\u5e94\u4e8e\u4f4e\u56db\u5206\u4f4d\u6570\u3001\u5e73\u5747\u503c\u548c\u9ad8\u56db\u5206\u4f4d\u6570\u3002<\/p>\n

\u9664\u6b64\u4e4b\u5916\u56fe\u4e2d\u8fd8\u6709\u4e00\u4e9b\u663e\u8457\u6027\u6807\u8bb0\uff0c\u7528\u6765\u663e\u793a\u4e0d\u540c\u7c7b\u522b\u4e4b\u95f4\u7684H&E TIL\u5206\u6570\u662f\u5426\u6709\u663e\u8457\u5dee\u5f02\uff0c\u4f8b\u5982\uff0cPediatric Inflamed\u548cMyeloid Predominant\u4e4b\u95f4\u6709\u4e00\u4e2a\u661f\u53f7\uff0c\u8868\u793a\u5b83\u4eec\u4e4b\u95f4\u7684\u5dee\u5f02\u662f\u663e\u8457\u7684\uff0c\u800cPediatric Inflamed\u548cImmune Desert\u4e4b\u95f4\u6709\u4e00\u4e2a\u661f\u53f7\uff0c\u540c\u6837\u8868\u793a\u5b83\u4eec\u4e4b\u95f4\u7684\u5dee\u5f02\u662f\u975e\u5e38\u663e\u8457\u7684\u3002<\/p>\n

Step 7 \u80bf\u7624\u4e0e\u4fe1\u53f7\u901a\u8def\u9891\u7387\u5206\u6790<\/p>\n

#\u5bfc\u5165\u6570\u636e<\/p>\n

load(file = paste0(datapath,”metadata_IC.RData”))<\/p>\n

n_x <- 4<\/p>\n

#<\/p>\n

lggp_c <- subgroupcount_IC.fx(metadata_IC, “pedLGG”)<\/p>\n

lggp_f <- subgroupfreq_IC.fx(metadata_IC, “pedLGG”)<\/p>\n

hggp_c <- subgroupcount_IC.fx(metadata_IC, “pedHGG”)<\/p>\n

hggp_f <- subgroupfreq_IC.fx(metadata_IC, “pedHGG”)<\/p>\n

nblp_c <- subgroupcount_IC.fx(metadata_IC, “NBL”)<\/p>\n

nblp_f <- subgroupfreq_IC.fx(metadata_IC, “NBL”)<\/p>\n

atrtp_c <- subgroupcount_IC.fx(metadata_IC, “ATRT”)<\/p>\n

atrtp_f <- subgroupfreq_IC.fx(metadata_IC, “ATRT”)<\/p>\n

mbp_c <- subgroupcount_IC.fx(metadata_IC, “MB”)<\/p>\n

mbp_f <- subgroupfreq_IC.fx(metadata_IC, “MB”)<\/p>\n

epnp_c <- subgroupcount_IC.fx(metadata_IC, “EPN”)<\/p>\n

epnp_f <- subgroupfreq_IC.fx(metadata_IC, “EPN”)<\/p>\n

#\u7ec4\u5408\u6761\u5f62\u56fe<\/p>\n

c_ls = list(atrtp_c + ggtitle(expression(~underline(“ATRT”))),<\/p>\n

nblp_c+ ggtitle(expression(~underline(“NBL”))),<\/p>\n

mbp_c+ ggtitle(expression(~underline(“MB”))),<\/p>\n

hggp_c + ggtitle(expression(~underline(“pedHGG”))),<\/p>\n

lggp_c + ggtitle(expression(~underline(“pedLGG”))),<\/p>\n

epnp_c + ggtitle(expression(~underline(“EPN”))))<\/p>\n

f_ls = list(atrtp_f, nblp_f, mbp_f, hggp_f, lggp_f, epnp_f )<\/p>\n

#\u5408\u5e76<\/p>\n

pdf(paste0(plotpath, “Fig1_G.pdf”),<\/p>\n

width = 20, height = 10, useDingbats = FALSE)<\/p>\n

wrap_plots(c(c_ls, f_ls), nrow = 2)<\/p>\n

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

<\/p>\n

\u8fd9\u662f\u4eca\u5929\u7684\u6700\u540e\u4e00\u5e45\u56fe\u5566\uff0c\u8fd9\u5e45\u56fe\u662f\u4e00\u4e2a\u663e\u793a\u4e0d\u540c\u7c7b\u578b\u764c\u75c7\u9891\u7387\u548c\u5206\u6570\u7684\u6761\u5f62\u56fe\u3002\u56fe\u4e2d\u6709\u516d\u4e2a\u7c7b\u522b\u7684\u80bf\u7624\uff0c\u6bcf\u4e2a\u7c7b\u522b\u90fd\u6709\u4e00\u4e2a\u9891\u7387\u6761\u5f62\u56fe\u548c\u4e00\u4e2a\u5206\u6570\u5806\u53e0\u6761\u5f62\u56fe\uff0c\u5728\u6761\u5f62\u56fe\u4e2d\uff0c\u7528\u7070\u8272\u9634\u5f71\u8868\u793a\u6bcf\u4e2a\u7c7b\u522b\u7684\u6837\u672c\u6570\uff0c\u5206\u6570\u5806\u53e0\u6761\u5f62\u56fe\u5219\u7528\u591a\u79cd\u989c\u8272\u8868\u793a\u6bcf\u4e2a\u7c7b\u522b\u4e2d\u4e0d\u540c\u4e9a\u578b\u7684\u767e\u5206\u6bd4\uff0c\u6bcf\u4e2a\u4e9a\u578b\u7684\u7f29\u5199\u548c\u4ee3\u7801\u5728\u56fe\u7684\u5e95\u90e8\u6807\u6ce8\uff0c\u4f8b\u5982\uff0cATRT-TYR\u8868\u793aATRT\u4e2d\u7684\u916a\u6c28\u9178\u4e9a\u578b\uff0cMB-WNT\u8868\u793aMB\u4e2d\u7684WNT\u4fe1\u53f7\u901a\u8def\u4e9a\u578b\uff0c\u8fd9\u4e9b\u4e9a\u578b\u662f\u6839\u636e\u57fa\u56e0\u7ec4\u6216\u8f6c\u5f55\u7ec4\u6570\u636e\u8fdb\u884c\u5206\u6790\u548c\u5206\u7c7b\u7684\uff0c\u53cd\u6620\u4e86\u4e0d\u540c\u764c\u75c7\u7c7b\u578b\u7684\u751f\u7269\u5b66\u7279\u5f81\u548c\u9884\u540e\u7279\u5f81\u3002<\/p>\n

\u901a\u8fc7\u4ee5\u4e0a\u7684\u5206\u6790\uff0c\u6211\u4eec\u5728pedNST\u4e2d\u627e\u5230\u4e86\u56db\u4e2a\u5e7f\u6cdb\u7684\u514d\u75ab\u96c6\u7fa4\uff0c\u5e76\u4e14\u8fdb\u884c\u4e86\u548c\u514d\u75ab\u96c6\u7fa4\u6709\u5173\u7684\u76f8\u5173\u6027\u5206\u6790\u3002<\/p>\n

\u4ee5\u4e0a\u5c31\u662f\u5c0f\u679c\u4eca\u5929\u7684\u5206\u4eab\u5566\uff0c\u4eca\u5929\u6211\u4eec\u4e3b\u8981\u5bf9\u513f\u7ae5\u7684\u4e00\u4e9b\u80bf\u7624\u5e93\u6570\u636e\u8fdb\u884c\u4e86\u5206\u6790\uff0c\u76f8\u4fe1\u5927\u5bb6\u4e00\u5b9a\u5bf9\u591a\u6570\u636e\u5e93\u7684\u5206\u6790\u6709\u4e86\u65b0\u7684\u601d\u8003\uff01\u5982\u679c\u5c0f\u4f19\u4f34\u4eec\u6709\u4efb\u4f55\u7591\u95ee\uff0c\u6b22\u8fce\u5173\u6ce8\u5c0f\u679c\uff0c\u5f53\u7136\uff0c\u4e5f\u8bd5\u4e00\u8bd5\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u5c0f\u5de5\u5177\uff0c\u53ea\u8981\u8f93\u5165\u5408\u9002\u7684\u6307\u4ee4\u5c31\u53ef\u4ee5\u76f4\u63a5\u7ed8\u5236\u60f3\u8981\u7684\u56fe\u5462\uff0c\u94fe\u63a5\uff1ahttp:\/\/www.biocloudservice.com\/home.html<\/a>\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

\u6570\u636e\u5e93\u662f\u751f\u4fe1\u5206\u6790\u7684\u5f3a\u5927\u6570\u636e\u57fa\u7840\uff0c\u5f80\u671f\u7684\u6587\u7ae0\u4e2d\u5c0f\u679c\u7ed9\u5c0f\u4f19\u4f34\u4eec\u4ecb\u7ecd\u4e86\u8bb8\u591a\u7684\u6570\u636e\u5e93\u4f7f\u7528\u7684\u65b9\u6cd5\uff0c\u90a3\u4e48\u6709\u6ca1\u6709\u4e00\u79cd\u65b9\u6cd5\u53ef\u4ee5 […]<\/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\/28061"}],"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=28061"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28061\/revisions"}],"predecessor-version":[{"id":28070,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28061\/revisions\/28070"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=28061"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=28061"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=28061"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}