{"id":28040,"date":"2024-02-04T17:50:14","date_gmt":"2024-02-04T09:50:14","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28040"},"modified":"2024-02-04T17:50:16","modified_gmt":"2024-02-04T09:50:16","slug":"%e6%80%8e%e6%a0%b7%e8%bf%88%e5%87%ba%e7%b2%be%e5%87%86%e5%8c%bb%e5%ad%a6%e5%88%86%e6%9e%90%e7%ac%ac%e4%b8%80%e6%ad%a5%ef%bc%9fxeva%e5%b8%ae%e4%bd%a0%e6%89%93%e5%a5%bd%e8%8d%af%e7%89%a9%e9%9d%b6","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28040","title":{"rendered":"\u600e\u6837\u8fc8\u51fa\u7cbe\u51c6\u533b\u5b66\u5206\u6790\u7b2c\u4e00\u6b65\uff1fXeva\u5e2e\u4f60\u6253\u597d\u836f\u7269\u9776\u5411\u7684\u57fa\u7840\uff01"},"content":{"rendered":"

\u5728\u7cbe\u51c6\u533b\u5b66\u7684\u9886\u57df\uff0c\u5982\u4f55\u786e\u5b9a\u836f\u7269\u5f3a\u5927\u7684\u751f\u7269\u6807\u5fd7\u7269\u662f\u7814\u7a76\u4eba\u5458\u4e00\u76f4\u9762\u4e34\u7684\u4e00\u4e2a\u5de8\u5927\u6311\u6218\uff0c\u867d\u7136\u60a3\u8005\u6e90\u6027\u80bf\u7624\u5f02\u79cd\u79fb\u690d(PDX)\u5df2\u7ecf\u662f\u975e\u5e38\u53ef\u9760\u7684\u4e34\u5e8a\u524d\u6a21\u578b\u4e86\uff0c\u4f46\u662f\u7f3a\u4e4f\u8ba1\u7b97\u5de5\u5177\u4f7f\u5f97\u5206\u6790PDX\u7684\u9ad8\u901a\u91cf\u5206\u5b50\u548c\u836f\u7406\u5b66\u7279\u5f81\u53d8\u5f97\u7279\u522b\u56f0\u96be\uff0c\u56e0\u6b64\uff0c\u5c0f\u679c\u4eca\u5929\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u7684\u5c31\u662f\u7cbe\u51c6\u533b\u5b66\u5173\u952e\u6311\u6218\u7684\u7834\u5c40\u8005\u2014\u2014Xeva\u3002<\/p>\n

Xeva (XEnograft Visualization & Analysis)\uff0c\u662f\u4e00\u4e2a\u7528\u4e8e\u4f53\u5185\u836f\u7269\u57fa\u56e0\u7ec4\u5b66\u6570\u636e\u96c6\u7684\u5f00\u6e90\u8f6f\u4ef6\u5305, \u53ef\u4ee5\u91cf\u5316\u57fa\u56e0\u8868\u8fbe\u7684\u53ef\u53d8\u6027\u548c\u8de8PDX\u4f20\u4ee3\u7684\u9014\u5f84\u6d3b\u6027\u3002<\/p>\n

Xeva\u901a\u8fc7\u4f7f\u7528\u8fc4\u4eca\u4e3a\u6b62\u6700\u5927\u7684PDX\u836f\u7269\u57fa\u56e0\u7ec4\u6570\u636e\u96c6\uff0c\u786e\u5b9a\u4e8687\u79cd\u4e0e51\u79cd\u836f\u7269\u53cd\u5e94\u663e\u8457\u76f8\u5173\u7684\u9014\u5f84(FDR<0.05)\u3002\u53d1\u73b0\u4e86\u57fa\u4e8e\u57fa\u56e0\u8868\u8fbe\u3001\u62f7\u8d1d\u6570\u7578\u53d8\u548c\u7a81\u53d8\u9884\u6d4b\u836f\u7269\u53cd\u5e94\u7684\u65b0\u7684\u751f\u7269\u6807\u5fd7\u7269(\u4e00\u81f4\u6027\u6307\u6570>0.60;FDR< 0.05)\u3002\u603b\u4e4b\uff0cXeva\u4e3a\u4e34\u5e8a\u524d\u4f53\u5185\u836f\u7269\u57fa\u56e0\u7ec4\u5b66\u6570\u636e\u7684\u7efc\u5408\u5206\u6790\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7075\u6d3b\u7684\u5e73\u53f0\uff0c\u4ee5\u8bc6\u522b\u9884\u6d4b\u836f\u7269\u53cd\u5e94\u7684\u751f\u7269\u6807\u5fd7\u7269\uff0c\u4ee3\u8868\u7740\u7cbe\u51c6\u80bf\u7624\u5b66\u7684\u91cd\u8981\u4e00\u6b65\uff01<\/p>\n

\u4ecb\u7ecd\u4e86\u8fd9\u4e48\u591a\uff0c\u5c0f\u679c\u5df2\u7ecf\u8dc3\u8dc3\u6b32\u8bd5\u5566\uff01\u4eca\u5929\u5c0f\u679c\u5c31\u6765\u624b\u628a\u624b\u6559\u4f1a\u5927\u5bb6\u5982\u4f55\u4f7f\u7528Xeva\u8fd9\u4e2aR\u5305\u3002\u672c\u6b21\u6559\u7a0b\u5bf9\u7535\u8111\u914d\u7f6e\u6709\u4e00\u5b9a\u9700\u6c42\uff0c\u5efa\u8bae\u5927\u5bb6\u4f7f\u7528\u670d\u52a1\u5668\u8fd0\u884c\uff0c\u5982\u679c\u6ca1\u6709\u81ea\u5df1\u7684\u670d\u52a1\u5668\uff0c\u6b22\u8fce\u8054\u7cfb\u6211\u4eec\u6765\u79df\u8d41\u670d\u52a1\u5668\u54e6\uff5e<\/p>\n

Step 1 \u5b89\u88c5Xeva<\/p>\n

\u5206\u6790\u6240\u9700\u7684\u6570\u636e\u5c0f\u679c\u5df2\u7ecf\u5e2e\u5927\u5bb6\u51c6\u5907\u597d\u4e86~<\/p>\n

\u94fe\u63a5\uff1ahttps:\/\/pan.baidu.com\/s\/1E9LxoIqMYYvBJKtkSkGN4A<\/p>\n

\u63d0\u53d6\u7801\uff1ax54w<\/p>\n

<\/p>\n

\u56fe\u4e2d\u7684\u6570\u636e\u662f\u6211\u4eec\u672c\u6b21\u5206\u6790\u6240\u6d89\u53ca\u5230\u7684\u4e00\u4e9b\u836f\u7269\u901a\u8def\uff0c\u4f8b\u5982\u56fe\u4e2d\u7ed9\u51fa\u4e86MAPK\u76f8\u5173\u7684\u4fe1\u53f7\u901a\u8def\u3002<\/p>\n

#\u68c0\u67e5\u548c\u5b89\u88c5Xeva<\/p>\n

library(devtools)<\/p>\n

if(is.element(“Xeva”, installed.packages())==FALSE)<\/p>\n

{<\/p>\n

install_github(“bhklab\/Xeva”)<\/p>\n

}<\/p>\n

library(Xeva)<\/p>\n

Step 2 \u5206\u6790\u836f\u7269\u5bf9\u4e8e\u4e73\u817a\u764c\u7684\u6709\u6548\u6027<\/p>\n

#\u5b89\u88c5\u6240\u9700R\u5305<\/p>\n

library(methods)<\/p>\n

library(BBmisc)<\/p>\n

library(Xeva)<\/p>\n

#\u52a0\u8f7d\u60a3\u8005\u7684\u80bf\u7624\u53cd\u5e94\u6570\u636e<\/p>\n

data(pdxe)<\/p>\n

#\u5c06\u80bf\u7624\u7684\u7f29\u5199\u548c\u5168\u79f0\u5bf9\u5e94\uff0c\u65b9\u4fbf\u7ed8\u56fe<\/p>\n

tumor <- c(“BRCA” = “Breast Cancer”,<\/p>\n

“NSCLC” = “Non-small Cell Lung Carcinoma”,<\/p>\n

“PDAC” = “Pancreatic Ductal Carcinoma”,<\/p>\n

“CM” = “Cutaneous Melanoma”,<\/p>\n

“GC” = “Gastric Cancer”,<\/p>\n

“CRC” = “Colorectal Cancer”)<\/p>\n

#\u8f93\u51faPDF\u6587\u4ef6\uff0c\u6839\u636eXeva\u4e2d\u7684mRECIST \u6807\u51c6\uff0c\u6309\u7167\u60a3\u8005\u7684ID\u548c\u80bf\u7624\u7c7b\u578b\u8fdb\u884c\u4e86\u5206\u7ec4\uff0c\u5f97\u5230\u4e86\u4e00\u4e2a\u65b0\u7684\u6570\u636e\u96c6 pdxe.tt<\/p>\n

pdf(“..\/results\/figure-1.pdf”, width = 14.1, height = 7.6)<\/p>\n

for(tt in names(tumor))<\/p>\n

{<\/p>\n

pdxe.tt <- summarizeResponse(pdxe, response.measure = “mRECIST”,<\/p>\n

group.by=”patient.id”, tissue=tt)<\/p>\n

pdxe.tt <- pdxe.tt[, names(sort(pdxe.tt[“untreated”, ], na.last = TRUE))]<\/p>\n

plotmRECIST(pdxe.tt, control.name = “Untreated”, name = tumor[tt],<\/p>\n

row_fontsize = 12, col_fontsize=12, sort = FALSE)<\/p>\n

}<\/p>\n

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

<\/p>\n

\u901a\u8fc7\u4f7f\u7528Xeva\u7684mRECIST\u51fd\u6570\u8ba1\u7b97\u548c\u53ef\u89c6\u5316PDXE\u4e73\u817a\u764c\u6570\u636e\u4e2d\u57fa\u4e8ePDX\u7684\u836f\u7269\u7b5b\u9009\u7684\u54cd\u5e94\u3002<\/p>\n

\u4ece\u56fe\u7247\u4e2d\u53ef\u4ee5\u770b\u5230\uff0c\u6709\u4e9b\u6cbb\u7597\u7684\u6548\u679c\u6bd4\u8f83\u597d\uff0c\u6bd4\u5982\u00a0BGJ398<\/strong>\u00a0\u548c\u00a0binimetinib<\/strong>\uff0c\u6709\u5f88\u591a\u7ea2\u8272\u7684\u683c\u5b50\uff0c\u8868\u793a\u5b8c\u5168\u53cd\u5e94\uff08CR\uff09\u3002\u6709\u4e9b\u6cbb\u7597\u7684\u6548\u679c\u6bd4\u8f83\u5dee\uff0c\u6bd4\u5982\u00a0paclitaxel<\/strong>\u00a0\u548c\u00a0LLM871<\/strong>\uff0c\u5b83\u4eec\u6709\u5f88\u591a\u84dd\u8272\u7684\u683c\u5b50\uff0c\u8868\u793a\u8fdb\u5c55\u6027\u75be\u75c5\uff08PD\uff09\u3002\u6709\u4e9b\u6cbb\u7597\u7684\u6548\u679c\u6bd4\u8f83\u4e00\u822c\uff0c\u6bd4\u5982LEE011+everolimus<\/strong>\uff0c\u6709\u5f88\u591a\u7eff\u8272\u7684\u683c\u5b50\uff0c\u8868\u793a\u7a33\u5b9a\u75c5\u60c5\uff08SD\uff09\u3002\u53ef\u4ee5\u5e2e\u52a9\u7814\u7a76\u4eba\u5458\u5206\u6790\u54ea\u4e9b\u836f\u7269\u5bf9\u54ea\u4e9b\u60a3\u8005\u6709\u6548\uff0c\u4ee5\u53ca\u54ea\u4e9b\u836f\u7269\u53ef\u80fd\u6709\u526f\u4f5c\u7528\u6216\u65e0\u6548\u3002<\/p>\n

Step 3 \u5bf9\u57fa\u56e0\u6570\u636e\u8fdb\u884cTSNE\u5206\u6790\uff0c\u76f8\u5173\u6027\u5206\u6790<\/p>\n

#\u5bfc\u5165\u529f\u80fd\u51fd\u6570\uff0c\u6240\u7528\u5305<\/p>\n

set.seed(19)<\/p>\n

#\u8c03\u7528\u529f\u80fd\u51fd\u6570\u6587\u4ef6<\/p>\n

source(“imp_functions.R”)<\/p>\n

suppressMessages(library(Biobase))<\/p>\n

library(Rtsne)<\/p>\n

library(ggplot2)<\/p>\n

library(ggpubr)<\/p>\n

#\u51c6\u5907\u8f93\u51fa\u7ed3\u679c<\/p>\n

args <- commandArgs(trailingOnly = TRUE)<\/p>\n

recompute <- FALSE<\/p>\n

if(args[1] %in% c(TRUE, FALSE))<\/p>\n

{ recompute <- args[1] }<\/p>\n

pdf(“..\/results\/figure-2.pdf”, width = 9.27, height = 6.4)<\/p>\n

#\u4ece”..\/data\/passage_data.Rda”\u6587\u4ef6\u4e2d\u8bfb\u53d6\u901a\u9053\u6570\u636e\u3002<\/p>\n

passage <- readRDS(“..\/data\/passage_data.Rda”)<\/p>\n

dp <- pData(passage$data)<\/p>\n

#\u5bf9\u6570\u636e\u8fdb\u884ct-SNE\uff08t\u5206\u5e03\u968f\u673a\u90bb\u5c45\u5d4c\u5165\uff09\u964d\u7ef4\u5904\u7406<\/p>\n

#\u8ba1\u7b97\u80bf\u7624\u5bf9\u5e94\u7684\u60a3\u8005\u6570\u91cf\uff0c\u6392\u5e8f<\/p>\n

ptByTT <- sort(sapply(unique(dp$tumor.type),<\/p>\n

function(tt){length(unique(dp[dp$tumor.type==tt, “patient.id”]))},<\/p>\n

USE.NAMES = TRUE))<\/p>\n

#\u7b5b\u9009\u6570\u91cf\u5927\u4e8e10\u7684\u80bf\u7624\u7c7b\u578b<\/p>\n

tt2take <- names(ptByTT)[ptByTT>10]<\/p>\n

dq <- dp[dp$tumor.type %in% tt2take, ]<\/p>\n

psgFrq<- table(dq$patient.id)<\/p>\n

pid2take <- names(psgFrq)[psgFrq>1]<\/p>\n

dq <- dq[dq$patient.id%in%pid2take, ]<\/p>\n

dq$tumor.type <- gsub(“_”, ” “, dq$tumor.type)<\/p>\n

dq$tumor.type[dq$tumor.type==”large intestine”] <- “LI”<\/p>\n

tissuCol <- c(‘#1b9e77′,’#d95f02′,’#7570b3′,’#e7298a’, ‘#d9f0a3’, ‘#e6ab02′,’#a6761d’,’#666666′)<\/p>\n

names(tissuCol) <- c(“lung”, “ovary”, “LI”, “breast”, “kidney”, “pancreas”,<\/p>\n

“skin”, “soft tissue”)<\/p>\n

#\u7b5b\u9009\u6570\u636e<\/p>\n

dt <- as.matrix(t(Biobase::exprs(passage$data)))<\/p>\n

dt <- dt[dq$biobase.id, ]<\/p>\n

#\u8fd0\u884ct-SNE\u7b97\u6cd5\u5206\u6790<\/p>\n

tsne_out <- Rtsne(dt, perplexity = 13)<\/p>\n

dq$X <- data.frame(tsne_out$Y)[,1]<\/p>\n

dq$Y <- data.frame(tsne_out$Y)[,2]<\/p>\n

#\u8fdb\u884c\u53ef\u89c6\u5316\u5206\u6790<\/p>\n

tsne.plot <- ggplot(data=dq, aes(X, Y, color=tumor.type)) + geom_point(size=0.5)<\/p>\n

tsne.plot <- tsne.plot + theme_classic()<\/p>\n

tsne.plot <- tsne.plot + scale_color_manual(values=tissuCol)<\/p>\n

tsne.plot <- tsne.plot + geom_line(data=dq, aes(X, Y, group=patient.id),<\/p>\n

show.legend=FALSE)<\/p>\n

print(tsne.plot)<\/p>\n

#\u5bf9\u80bf\u7624\u6570\u636e\u7684\u76f8\u5173\u6027\u8fdb\u884c\u5206\u6790<\/p>\n

marExp <- Biobase::exprs(passage$data)<\/p>\n

passMat <- passage$passage<\/p>\n

# \u8fc7\u6ee4\u6709\u591a\u4e2a\u975e NA \u503c\u7684\u884c\u548c\u5217<\/p>\n

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

filter(rowSums(!is.na(.)) > 1) %>%<\/p>\n

filter(colSums(!is.na(.)) > 1)<\/p>\n

if(recompute==TRUE)<\/p>\n

{<\/p>\n

cat(sprintf(“recomputing values…. it may take long time\\n”))<\/p>\n

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

##—– correlation for related pairs ————<\/p>\n

# use map_dfr to loop over the rows and return a data frame<\/p>\n

dfx <- passMat %>%<\/p>\n

map_dfr(~{<\/p>\n

# use combn to get all possible pairs of non-NA values<\/p>\n

prx <- combn(.x[!is.na(.x)], 2, simplify = F)<\/p>\n

# use map_dbl to loop over the pairs and return a numeric vector<\/p>\n

v <- prx %>%<\/p>\n

map_dbl(~cor(marExp[, .x[1]], marExp[, .x[2]], method = “pearson”))<\/p>\n

# return a data frame with correlation and type<\/p>\n

data.frame(Correlation = v, type = “Related pairs”)<\/p>\n

})<\/p>\n

# \u4f7f\u7528 sample_n \u83b7\u53d6\u884c\u7684\u968f\u673a\u6837\u672c<\/p>\n

randPassMat <- passMat %>%<\/p>\n

sample_n(nrow(passMat))<\/p>\n

# \u4f7f\u7528 map_dfr \u904d\u5386\u884c\u5e76\u8fd4\u56de\u6570\u636e\u5e27<\/p>\n

dfx <- randPassMat %>%<\/p>\n

map_dfr(~{<\/p>\n

# use combn to get all possible pairs of non-NA values<\/p>\n

prx <- combn(.x[!is.na(.x)], 2, simplify = F)<\/p>\n

# use keep to filter the pairs that are not in the same row of passMat<\/p>\n

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

keep(~!any(passMat[which(passMat == .x[1]), ] == .x[2]))<\/p>\n

#\u4f7f\u7528 map_dbl \u904d\u5386\u8fd9\u4e9b\u5bf9\u5e76\u8fd4\u56de\u4e00\u4e2a\u6570\u503c\u5411\u91cf<\/p>\n

v <- prx %>%<\/p>\n

map_dbl(~cor(marExp[, .x[1]], marExp[, .x[2]]))<\/p>\n

# return a data frame with correlation and type<\/p>\n

data.frame(Correlation = v, type = “Random pairs”)<\/p>\n

})<\/p>\n

} else<\/p>\n

{<\/p>\n

cat(sprintf(“using pre-computed values\\n”))<\/p>\n

dfx <- readRDS(“..\/data\/PDX_passage_correlation.Rda”)<\/p>\n

}<\/p>\n

pltOrd <- c(“Related pairs”, “Random pairs”)<\/p>\n

dfx$type <- factor(as.character(dfx$type), levels = pltOrd)<\/p>\n

pltCol <- c(‘#e41a1c’,’#377eb8′)<\/p>\n

names(pltCol) <- pltOrd<\/p>\n

#\u521b\u5efa\u5c0f\u63d0\u7434\u56fe\u548c\u6458\u8981\u7edf\u8ba1\u6570\u636e\uff0c\u4ee5\u6bd4\u8f83\u76f8\u5173\u5bf9\u548c\u968f\u673a\u5bf9\u4e4b\u95f4\u7684\u76f8\u5173\u6027<\/p>\n

p <- ggplot(data=dfx, aes(x=type, y=Correlation, fill=type, color=type))<\/p>\n

p <- p + geom_violin(trim=T)<\/p>\n

p <- p + theme_classic()<\/p>\n

p <- p + scale_fill_manual(values=pltCol)<\/p>\n

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

p <- p + stat_summary(fun.data=data_summary, colour = “white”, size=0.4)<\/p>\n

p <- p + theme(legend.position=”none”)<\/p>\n

p <- p + scale_y_continuous(breaks = seq(-1,1,by = 0.1))<\/p>\n

p <- p + theme(axis.text.x = element_text(colour=”grey20″,size=12, angle=0,face=”bold”),<\/p>\n

axis.text.y = element_text(colour=”grey20″,size=11, face=”bold”),<\/p>\n

axis.title.y = element_text(colour=”grey20″,size=11, face=”bold”),<\/p>\n

axis.ticks.length=unit(0.25,”cm”))<\/p>\n

p <- p + theme(axis.line.x = element_line(size = 0, colour = “white”),<\/p>\n

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

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

comparisons <- list(c(“Related pairs”, “Random pairs”))<\/p>\n

p <- p + stat_compare_means(comparisons = comparisons, label = “p.signif”)<\/p>\n

print(p)<\/p>\n

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

<\/p>\n

\u7ed3\u679c\u5982\u56fe\uff0c\u5de6\u56fe\u662f\u5bf9PDXs\u4e0d\u540c\u4f20\u4ee317304\u4e2a\u57fa\u56e0\u7684\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u8fdb\u884cTSNE\u5206\u6790\uff0c\u4ece\u800c\u5c06\u9ad8\u7ef4\u6570\u636e\u6620\u5c04\u5230\u4f4e\u7ef4\u7a7a\u95f4\uff0c\u663e\u793a\u4e86\u4e0d\u540c\u7c7b\u578b\u7684\u80bf\u7624\u5728\u4e8c\u7ef4\u7a7a\u95f4\u4e2d\u7684\u5206\u5e03\u3002\u6bcf\u79cd\u989c\u8272\u4ee3\u8868\u4e00\u79cd\u7279\u5b9a\u7c7b\u578b\u7684\u80bf\u7624\uff0c\u6bd4\u5982\u8bf4\u4e73\u817a\u764c\uff08\u7d2b\u8272\uff09\u7684\u6570\u636e\u70b9\u5206\u5e03\u5f97\u6bd4\u8f83\u5e7f\u6cdb\uff0c\u6709\u4e9b\u70b9\u4e0e\u5176\u4ed6\u7c7b\u578b\u7684\u80bf\u7624\u6df7\u5408\u5728\u4e00\u8d77\uff0c\u6709\u4e9b\u70b9\u5219\u5f62\u6210\u4e86\u4e00\u4e9b\u7c07\u3002\u8fd9\u53ef\u80fd\u8868\u660e\u4e73\u817a\u764c\u7684\u5f02\u8d28\u6027\u6bd4\u8f83\u9ad8\uff0c\u6709\u4e9b\u4e9a\u578b\u4e0e\u5176\u4ed6\u7c7b\u578b\u7684\u80bf\u7624\u66f4\u76f8\u4f3c\uff0c\u6709\u4e9b\u4e9a\u578b\u5219\u66f4\u72ec\u7279\u3002<\/p>\n

\u800c\u53f3\u56fe\u662f\u76f8\u5173\u6837\u672c\u5bf9(\u5c5e\u4e8e\u540c\u4e00\u8c31\u7cfb)\u548c\u968f\u673a\u9009\u62e9\u6837\u672c\u5bf9\u7684Pearson\u76f8\u5173\u6027\u7684\u53ef\u89c6\u5316\uff0c\u4ece\u8fd9\u5e45\u56fe\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff1a\u76f8\u5173\u5bf9\u7684\u76f8\u5173\u6027\u7684\u5e73\u5747\u503c\u662f0.94\uff0c\u968f\u673a\u5bf9\u7684\u76f8\u5173\u6027\u7684\u5e73\u5747\u503c\u662f0.86\uff0c\u8bf4\u660e\u4e24\u8005\u4e4b\u95f4\u6709\u663e\u8457\u7684\u5dee\u5f02\u3002<\/p>\n

Step 4 \u6240\u6709\u57fa\u56e0\u5728PDX\u4f20\u4ee3\u4e2d\u7684\u7c7b\u5185\u76f8\u5173\u6027(ICC)\u53ef\u89c6\u5316<\/p>\n

#\u5bfc\u5165\u5305<\/p>\n

source(“imp_functions.R”)<\/p>\n

library(foreach)<\/p>\n

library(doParallel)<\/p>\n

library(psych)<\/p>\n

##plotViolin\uff1a\u4f7f\u7528ggplot2\u5305\u751f\u6210\u5c0f\u63d0\u7434\u56fe\u3002<\/p>\n

plotViolin <- function(dfx, pltCol, ylim=NULL)<\/p>\n

{<\/p>\n

p <- ggplot(data=dfx, aes(x=type, y=ICC , fill=type,color=type))<\/p>\n

p <- p + geom_boxplot(width = .001, outlier.size = 2.0, outlier.color = “#525252”,<\/p>\n

outlier.shape = 95)<\/p>\n

p <- p + geom_violin(trim=F)<\/p>\n

p <- p + theme_classic()<\/p>\n

p <- p + scale_fill_manual(values=pltCol)<\/p>\n

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

p <- p + stat_summary(fun.data=data_summary, colour = “white”, size=0.4)<\/p>\n

p <- p + theme(legend.position=”none”)<\/p>\n

p <- p + scale_y_continuous(breaks = seq(-1,1,by = 0.25))<\/p>\n

p <- p + theme(axis.text.x = element_text(colour=”grey20″,size=12, angle=0,face=”bold”),<\/p>\n

axis.text.y = element_text(colour=”grey20″,size=11, face=”bold”),<\/p>\n

axis.title.y = element_text(colour=”grey20″,size=11, face=”bold”),<\/p>\n

axis.ticks.length=unit(0.25,”cm”))<\/p>\n

p <- p + theme(axis.line.x = element_line(size = 0, colour = “white”),<\/p>\n

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

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

if(!is.null(ylim))<\/p>\n

{ p <- p + coord_cartesian(ylim = ylim, expand = F) }<\/p>\n

return(p)<\/p>\n

}<\/p>\n

##computeRankMatFor1Gene\u51fd\u6570\uff1a\u8ba1\u7b97\u7ed9\u5b9a\u57fa\u56e0\u5728\u6837\u672c\u4e2d\u7684\u6392\u540d\u77e9\u9635<\/p>\n

computeRankMatFor1Gene <- function(gn, pt, marExp)<\/p>\n

{<\/p>\n

findRankOfGene <- function(gn, sampleIds, marExp)<\/p>\n

{<\/p>\n

rtx <- rep(NA, length(sampleIds)); names(rtx)<- names(sampleIds)<\/p>\n

for(n in names(rtx))<\/p>\n

{<\/p>\n

s <- sampleIds[n]<\/p>\n

if(!is.na(s))<\/p>\n

{<\/p>\n

gval <- sort(marExp[, s])<\/p>\n

rtx[n] <- which(names(gval)==gn)<\/p>\n

}<\/p>\n

}<\/p>\n

return(rtx)<\/p>\n

}<\/p>\n

gnRankMat <- matrix(data = NA, nrow = nrow(pt), ncol = ncol(pt))<\/p>\n

rownames(gnRankMat) <- rownames(pt); colnames(gnRankMat)<- colnames(pt)<\/p>\n

for(patientID in rownames(pt))<\/p>\n

{<\/p>\n

sampleIds <- pt[patientID, ]<\/p>\n

gnRankMat[patientID, ] <- findRankOfGene(gn, sampleIds, marExp)<\/p>\n

}<\/p>\n

gnRankMat <- gnRankMat[!apply(gnRankMat, 1, function(x)all(is.na(x))), ]<\/p>\n

return(gnRankMat)<\/p>\n

}<\/p>\n

##getICCforAllGen\u51fd\u6570\uff1a\u7528\u4e8e\u8ba1\u7b97\u6240\u6709\u57fa\u56e0\u7684ICC\uff08Intraclass Correlation Coefficient\uff09\u3002<\/p>\n

getICCforAllGenes <- function(pt, allGenes, marExp, NCPU=2, verbose=F)<\/p>\n

{<\/p>\n

if(verbose==TRUE) { cl<-makeCluster(NCPU, outfile=””) } else<\/p>\n

{ cl<-makeCluster(NCPU)}<\/p>\n

registerDoParallel(cl)<\/p>\n

allICC <- foreach(i=1:length(allGenes), .export=c(“computeRankMatFor1Gene”),<\/p>\n

.packages = c(“psych”) ) %dopar%<\/p>\n

{<\/p>\n

gn <- allGenes[i]<\/p>\n

gnRankMat <- computeRankMatFor1Gene(gn, pt, marExp)<\/p>\n

v <- NULL<\/p>\n

if(nrow(gnRankMat)>2)<\/p>\n

{<\/p>\n

v <- psych::ICC(gnRankMat, missing = F)<\/p>\n

v$geneName <- gn<\/p>\n

}<\/p>\n

v<\/p>\n

}<\/p>\n

stopCluster(cl)<\/p>\n

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

for(i in 1:length(allICC))<\/p>\n

{<\/p>\n

if(!is.null(allICC[[i]]))<\/p>\n

{<\/p>\n

v <- allICC[[i]]$results[“Single_raters_absolute”,]<\/p>\n

v[“gene”] <- allICC[[i]]$geneName<\/p>\n

rtx <- rbind(rtx, v[c(“ICC”, “p”, “gene”)])<\/p>\n

}<\/p>\n

}<\/p>\n

rownames(rtx) <- NULL<\/p>\n

return(rtx)<\/p>\n

}<\/p>\n

## randomizeMatGetICC\u51fd\u6570\uff1a\u7528\u4e8e\u5bf9\u6570\u636e\u8fdb\u884c\u968f\u673a\u5316\u5904\u7406\uff0c\u5e76\u8ba1\u7b97\u968f\u673a\u5316\u540e\u7684\u57fa\u56e0ICC\u503c\u3002<\/p>\n

randomizeMatGetICC <- function(passMatx, allGenes, marExp, NCPU, verbose=F)<\/p>\n

{<\/p>\n

passMatRand <- passMatx<\/p>\n

for(i in 1:ncol(passMatRand))<\/p>\n

{ passMatRand[,i] <- sample(passMatRand[,i]) }<\/p>\n

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

{ passMatRand[i,] <- sample(passMatRand[i,]) }<\/p>\n

passMatRand <- passMatRand[!(apply(passMatRand, 1, function(x) all(is.na(x)))),]<\/p>\n

rownames(passMatRand) <- paste0(“r”, 1:nrow(passMatRand))<\/p>\n

allICCRand <- getICCforAllGenes(passMatRand, allGenes, marExp, NCPU=NCPU,<\/p>\n

verbose=verbose)<\/p>\n

return(allICCRand)<\/p>\n

}<\/p>\n

## \u6570\u636e\u52a0\u8f7d\u548c\u51c6\u5907<\/p>\n

NCPU = max(1, detectCores()-1 )<\/p>\n

cat(sprintf(“\\n##—- number of CPU %d ——\\n”, NCPU))<\/p>\n

passage <- readRDS(“..\/data\/passage_data.Rda”)<\/p>\n

dp <- pData(passage$data)<\/p>\n

mar <- passage$data<\/p>\n

allGenes <- rownames(mar)<\/p>\n

marExp <- Biobase::exprs(mar)<\/p>\n

pheno <- Biobase::pData(mar)<\/p>\n

pheno$passage <- paste0(“P”, pheno$passage)<\/p>\n

passMat <- passage$passage<\/p>\n

if(recompute==TRUE){<\/p>\n

cat(sprintf(“recomputing values…. it may take long time\\n”))<\/p>\n

ICCv <- list()<\/p>\n

##——————————————————-<\/p>\n

##——————For all tissue type —————–<\/p>\n

##——————————————————-<\/p>\n

allICC <- getICCforAllGenes(passMat, allGenes, marExp, NCPU=NCPU, verbose=F)<\/p>\n

allICC$type <- “All tissue”<\/p>\n

allICCRand <- randomizeMatGetICC(passMat, allGenes, marExp, NCPU=NCPU, verbose=F)<\/p>\n

allICCRand$type <- “Null (All tissue)”<\/p>\n

ICCv[[“All tissue”]] <- rbind(allICC, allICCRand)<\/p>\n

##——————————————————-<\/p>\n

##——————By Tissue Type ———————-<\/p>\n

tissueName <- c(“breast”, “lung”, “pancreas”, “skin”, “large_intestine”)<\/p>\n

for(tt in tissueName)<\/p>\n

{<\/p>\n

ptt <- passMat[pheno[pheno$tumor.type==tt, “patient.id”], , drop=F]<\/p>\n

if(nrow(ptt)>8)<\/p>\n

{<\/p>\n

cat(sprintf(“##— running for %s —–\\n”, tt))<\/p>\n

allICCtt <- getICCforAllGenes(ptt, allGenes, marExp, NCPU=NCPU, verbose=F)<\/p>\n

allICCtt$type <- tt<\/p>\n

allICCttRand <- randomizeMatGetICC(ptt,allGenes,marExp,NCPU=NCPU,verbose=F)<\/p>\n

allICCttRand$type <- sprintf(“Null (%s)”, tt)<\/p>\n

ICCv[[tt]] <- rbind(allICCtt, allICCttRand)<\/p>\n

}<\/p>\n

##\u50a8\u5b58\u7ed3\u679c<\/p>\n

saveRDS(ICCv, “..\/results\/ICC_for_genes.Rda”)<\/p>\n

}<\/p>\n

} else<\/p>\n

{ICCv <- readRDS(“..\/data\/ICC_for_genes.Rda”)}<\/p>\n

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

pltColo <- c(‘#e41a1c’, ‘#8dd3c7′,’#bebada’,’#fb8072′,’#80b1d3′,’#fdb462′)<\/p>\n

names(pltColo)<-c(“All tissue”,”breast”,”lung”,”pancreas”,”skin”,”large_intestine”)<\/p>\n

nullCol <- “#4d4d4d”<\/p>\n

pdf(“..\/results\/figure-3.pdf”, width = 5.15, height = 2.15)<\/p>\n

for(tt in names(pltColo))<\/p>\n

{<\/p>\n

dfx <- ICCv[[tt]]<\/p>\n

pltCol <- c(pltColo[tt], nullCol); names(pltCol) <- c(tt, sprintf(“Null (%s)”, tt))<\/p>\n

dfx$type <- factor(as.character(dfx$type), levels = names(pltCol))<\/p>\n

ylim <- c(-0.25,1)<\/p>\n

p <- plotViolin(dfx, pltCol, ylim = ylim)<\/p>\n

print(p)<\/p>\n

}<\/p>\n

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

<\/p>\n

\u5982\u56fe\uff0c\u5c0f\u63d0\u7434\u56fe\u663e\u793a\u4e86\u6240\u6709\u6837\u672c\u7684\u57fa\u56e0\u5728PDX\u4f20\u4ee3\u4e2d\u7684\u7c7b\u5185\u76f8\u5173\u6027(ICC)\uff0c\u5e76\u6309\u7ec4\u7ec7\u7c7b\u578b\u5206\u5c42\u3002\u9ed1\u8272\u7684\u5c0f\u63d0\u7434\u56fe\u8868\u793a\u4f7f\u7528\u975e\u4f20\u4ee3\u76f8\u5173(\u968f\u673a\u9009\u62e9)\u6837\u672c\u8ba1\u7b97\u7684\u57fa\u56e0\u7684ICC\u503c\u3002<\/p>\n

\u5c0f\u679c\u4ee5\u7b2c\u4e00\u5e45\u56fe\u4e3a\u4f8b\u5b50\u8fdb\u884c\u8bf4\u660e\uff0c\u8fd9\u5e45\u56fe\u663e\u793a\u4e86\u4e0d\u540c\u7c7b\u578b\u7684\u80bf\u7624\u5728\u901a\u9053\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u3002\u6bcf\u79cd\u989c\u8272\u4ee3\u8868\u4e00\u79cd\u7279\u5b9a\u7c7b\u578b\u7684\u80bf\u7624\u3002\u76f8\u5173\u5bf9\u7684\u76f8\u5173\u6027\u7684\u5e73\u5747\u503c\u662f0.94\uff0c\u968f\u673a\u5bf9\u7684\u76f8\u5173\u6027\u7684\u5e73\u5747\u503c\u662f0.86\uff0c\u4e24\u8005\u4e4b\u95f4\u6709\u663e\u8457\u7684\u5dee\u5f02\uff0c\u8fd9\u4e9b\u56fe\u7247\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u80bf\u7624\u7684\u53ef\u5851\u6027\u548c\u9002\u5e94\u6027\u3002<\/p>\n

\u4ee5\u4e0a\u5c31\u662f\u5c0f\u679c\u4eca\u5929\u5206\u4eab\u7684\u5168\u90e8\u5185\u5bb9\u5566\uff0c\u5728\u4eca\u5929\u7684\u4ecb\u7ecd\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7\u4f7f\u7528Xeva\u5305\uff0c\u5bf9PDXE\u4e73\u817a\u764c\u4ee5\u53ca\u4e2d\u57fa\u4e8ePDX\u7684\u836f\u7269\u8fdb\u884c\u4e86\u54cd\u5e94\u9776\u5411\u901a\u8def\u7684\u7b5b\u9009\uff0c\u5e76\u4e14\u4f7f\u7528t-SNE\u5bf9\u4e73\u817a\u764c\u76f8\u5173\u7684\u6570\u636e\u8fdb\u884c\u4e86\u964d\u7ef4\u5904\u7406\uff0c\u6700\u540e\u901a\u8fc7\u5206\u6790\u8bf4\u660ePDX\u6a21\u578b\u80fd\u591f\u4fdd\u6301\u539f\u59cb\u80bf\u7624\u7684\u57fa\u56e0\u8868\u8fbe\u7684\u7a33\u5b9a\u6027\u548c\u4e00\u81f4\u6027\uff0c\u8fdb\u800c\u4e3a\u7cbe\u51c6\u533b\u5b66\u7684\u63a2\u7d22\u63d0\u4f9b\u4e86\u826f\u597d\u7684\u4fdd\u969c\u3002<\/p>\n

\u9664\u4e86\u4eca\u5929\u5c0f\u679c\u4ecb\u7ecd\u7684\u529f\u80fd\u4e4b\u5916\uff0cXeva\u5305\u4e2d\u8fd8\u6709\u5f88\u591a\u7684\u529f\u80fd\u7b49\u7740\u5c0f\u4f19\u4f34\u4eec\u53bb\u63a2\u7d22\uff0c\u5982\u679c\u6709\u5176\u4ed6\u751f\u4fe1\u65b9\u5411\u7684\u4efb\u4f55\u7591\u95ee\uff0c\u6b22\u8fce\u5927\u5bb6\u5173\u6ce8\u5c0f\u679c\uff0c\u8bd5\u4e00\u8bd5\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u5c0f\u5de5\u5177\uff0c\u53ea\u8981\u8f93\u5165\u5408\u9002\u7684\u6570\u636e\uff0c\u5c31\u53ef\u4ee5\u76f4\u63a5\u7ed8\u5236\u60f3\u8981\u7684\u56fe\u5462\uff0c\u94fe\u63a5\uff1ahttp:\/\/www.biocloudservice.com\/home.html\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

\u5728\u7cbe\u51c6\u533b\u5b66\u7684\u9886\u57df\uff0c\u5982\u4f55\u786e\u5b9a\u836f\u7269\u5f3a\u5927\u7684\u751f\u7269\u6807\u5fd7\u7269\u662f\u7814\u7a76\u4eba\u5458\u4e00\u76f4\u9762\u4e34\u7684\u4e00\u4e2a\u5de8\u5927\u6311\u6218\uff0c\u867d\u7136\u60a3\u8005\u6e90\u6027\u80bf\u7624\u5f02\u79cd\u79fb\u690d(PD […]<\/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\/28040"}],"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=28040"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28040\/revisions"}],"predecessor-version":[{"id":28051,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28040\/revisions\/28051"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=28040"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=28040"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=28040"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}