\u63a5\u4e0b\u6765\uff0c\u5c0f\u5e08\u59b9\u5c06\u4ee5\u5375\u5de2\u764c\u6570\u636e\u4f5c\u4e3a\u6848\u4f8b\uff0c\u4e00\u6b65\u6b65\u6307\u5bfc\u60a8\u5b8c\u6210\u4ece\u6570\u636e\u9884\u5904\u7406\u5230\u6267\u884c\u9014\u5f84\u5206\u6790\u7684\u6574\u4e2a\u6d41\u7a0b\uff0c\u786e\u5b9a\u4e0e\u751f\u5b58\u7ed3\u679c\u76f8\u5173\u7684\u86cb\u767d\u8868\u8fbe\u7684\u91cd\u8981\u9014\u5f84\u3002\u6211\u4eec\u5c06\u4e00\u8d77\u63ed\u5f00\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u795e\u79d8\u9762\u7eb1\uff0c\u63a2\u7d22\u5176\u80cc\u540e\u7684\u751f\u7269\u5b66\u610f\u4e49\u3002\u5982\u679c\u5728\u5206\u6790\u8fc7\u7a0b\u4e2d\u9047\u5230\u4efb\u4f55\u95ee\u9898\uff0c<\/strong>\u5c0f\u5e08\u59b9<\/strong>\u968f\u65f6\u51c6\u5907\u4e3a\u60a8\u63d0\u4f9b\u5e2e\u52a9\u3002<\/strong><\/p>\n\n\n\n \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5pathwayPCA\u5305\u3002\u5982\u679c\u60a8\u5c1a\u672a\u5b89\u88c5\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n\n\n\n if (!require(“BiocManager”, quietly = TRUE))<\/p>\n\n\n\n install.packages(“BiocManager”)<\/p>\n\n\n\n BiocManager::install(version=’devel’)<\/p>\n\n\n\n BiocManager::install(“pathwayPCA”)<\/p>\n\n\n\n \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u52a0\u8f7dpathwayPCA\u5305\uff1a<\/p>\n\n\n\n library(tidyverse)<\/p>\n\n\n\n library(pathwayPCA)<\/p>\n\n\n\n \u5728\u672c\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u4e34\u5e8a\u86cb\u767d\u8d28\u7ec4\u5b66\u80bf\u7624\u5206\u6790\u8054\u76df(CPTAC)\u6700\u8fd1\u751f\u6210\u7684\u57fa\u4e8e\u8d28\u8c31\u7684\u5375\u5de2\u764c\u7684\u86cb\u767d\u8d28\u7ec4\u5b66\u6570\u636e\u3002\u5f52\u4e00\u5316\u86cb\u767d\u4e30\u5ea6\u8868\u8fbe\u6570\u636e\u96c6\u53ef\u4eceLinkedOmics\u6570\u636e\u5e93http:\/\/linkedomics.org\/data_download\/TCGA-OV\/\u83b7\u5f97\u3002\u6211\u4eec\u9009\u7528\u4e86\u7531\u592a\u5e73\u6d0b\u897f\u5317\u56fd\u5bb6\u5b9e\u9a8c\u5ba4\uff08PNNL\uff09\u751f\u6210\u7684\u201cProteome (PNNL, Gene level)\u201d\u6570\u636e\u96c6\u3002<\/p>\n\n\n\n \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u5b58\u50a81\u7684omics\u7c7b\u6570\u636e\u5bf9\u8c61\uff08\u5305\u542b\u8868\u8fbe\u5f0f\u6570\u636e\u96c6\u3001\u6837\u672c\u7684\u8868\u578b\u4fe1\u606f\u3001\u9014\u5f84\u96c6\u5408\uff09\u3002<\/p>\n\n\n\n \u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u52a0\u8f7dovarian_PNNL_survival\u83b7\u5f97\u5375\u5de2\u764c\u6570\u636e\u96c6\u3002<\/p>\n\n\n\n # \u4e0b\u8f7d\u6570\u636e ovSurv_df <- readRDS(<\/p>\n\n\n\n url(paste0(gitHubPath_char, “ovarian_PNNL_survival.RDS”))<\/p>\n\n\n\n )<\/p>\n\n\n\n # \u6216\u8005\u76f4\u63a5\u52a0\u8f7d\u63d0\u4f9b\u7684\u6570\u636e\u96c6<\/p>\n\n\n\n ovSurv_df <- read.csv(“ovSurv_df.csv”)<\/p>\n\n\n\n ovSurv_df [1:5, 1:5]<\/p>\n\n\n\n ovSurv_df\u6570\u636e\u96c6\u662f\u4e00\u4e2a\u5305\u542b\u86cb\u767d\u8d28\u8868\u8fbe\u6c34\u5e73\u548c\u4e0e\u6837\u672cid\u5339\u914d\u7684\u5b58\u6d3b\u7ed3\u679c\u7684\u6570\u636e\u6846\u67b6\u3002\u53d8\u91cf(\u5217)\u5305\u62ec\u603b\u4f53\u5b58\u6d3b\u65f6\u95f4\u548c\u5ba1\u67e5\u72b6\u6001\uff0c\u4ee5\u53ca83\u4e2a\u6837\u54c1\u4e2d\u6bcf\u4e2a5162\u4e2a\u86cb\u767d\u7684\u8868\u8fbe\u6570\u636e\u3002<\/p>\n\n\n\n \u5728\u8fdb\u884cpathway\u5206\u6790\u65f6\uff0c\u6211\u4eec\u9700\u8981\u6536\u96c6\u4e00\u7cfb\u5217\u7684\u751f\u7269\u5b66\u9014\u5f84\u3002\u5bf9\u4e8e\u8fd9\u4e9b\u9014\u5f84\u7684\u6536\u96c6\uff0c\u6211\u4eec\u9700\u8981\u6307\u5b9a\u4e00\u4e2a.gmt\u6587\u4ef6\uff0c\u8fd9\u662f\u4e00\u4e2a\u6587\u672c\u6587\u4ef6\uff0c\u5176\u4e2d\u6bcf\u4e00\u884c\u5bf9\u5e94\u4e00\u4e2a\u9014\u5f84\u3002\u6bcf\u4e00\u884c\u5305\u542b\u4e00\u4e2aID\uff08TERMS\u5217\uff09\u3001\u4e00\u4e2a\u53ef\u9009\u7684\u63cf\u8ff0\uff08description\u5217\uff09\uff0c\u4ee5\u53ca\u9014\u5f84\u4e2d\u7684\u57fa\u56e0\uff08\u6240\u6709\u540e\u7eed\u5217\uff09\u3002pathwayPCA\u5305\u542b\u4e862018\u5e746\u6708\u7684Wikipathways\u96c6\u5408\uff0c\u9002\u7528\u4e8e\u667a\u4eba\uff08Homo sapiens\uff09\uff0c\u53ef\u4ee5\u4f7f\u7528read_gmt\u51fd\u6570\u52a0\u8f7d\uff1a<\/p>\n\n\n\n print(“wikipathways-20190110-gmt-Homo_sapiens.gmt”)<\/p>\n\n\n\n dataDir_path <- system.file(“extdata”, package = “pathwayPCA”, mustWork = TRUE)<\/p>\n\n\n\n wikipathways_PC <- read_gmt(paste0(dataDir_path, “\/wikipathways_human_symbol.gmt”),description = TRUE)<\/p>\n\n\n\n \u73b0\u5728\u6211\u4eec\u5df2\u7ecf\u51c6\u5907\u597d\u4e86\u4e09\u79cd\u6570\u636e\uff08\u8868\u8fbe\u5f0f\u6570\u636e\u96c6\u3001\u6837\u672c\u7684\u8868\u578b\u4fe1\u606f\u3001\u9014\u5f84\u96c6\u5408\uff09\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2aOmicsSurv\u6570\u636e\u5bb9\u5668\u3002<\/p>\n\n\n\n \u8981\u67e5\u770b\u6211\u4eec\u521a\u521a\u521b\u5efa\u7684Omics\u6570\u636e\u5bf9\u8c61\u7684\u6458\u8981\uff0c\u53ea\u9700\u8f93\u5165\u5bf9\u8c61\u7684\u540d\u79f0\u5373\u53ef\u3002<\/p>\n\n\n\n ov_OmicsSurv <- CreateOmics(<\/p>\n\n\n\n # protein expression data<\/p>\n\n\n\n assayData_df = ovSurv_df[, -(2:3)],<\/p>\n\n\n\n # pathway collection<\/p>\n\n\n\n pathwayCollection_ls = wikipathways_PC,<\/p>\n\n\n\n # survival phenotypes<\/p>\n\n\n\n response = ovSurv_df[, 1:3],<\/p>\n\n\n\n # phenotype is survival data<\/p>\n\n\n\n respType = “survival”,<\/p>\n\n\n\n # retain pathways with > 5 proteins<\/p>\n\n\n\n minPathSize = 5 <\/p>\n\n\n\n )<\/p>\n\n\n\n \u6211\u4eec\u53ef\u4ee5\u67e5\u770b\u521a\u521a\u8fd9\u4e2aOmics\u6570\u636e\u5bf9\u8c61\u7684\u6458\u8981\uff1a<\/p>\n\n\n\n \u521b\u5efaOmics\u5bf9\u8c61\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u91c7\u7528\u4e24\u79cd\u65b9\u6cd5\u5206\u6790\u6570\u636e\uff1aAES-PCA\u548cSuperPCA\u3002AES-PCA\u662f\u4e00\u79cd\u65e0\u76d1\u7763\u65b9\u6cd5\uff0c\u901a\u8fc7\u964d\u7ef4\u6280\u672f\u63d0\u53d6\u6bcf\u4e2a\u9014\u5f84\u7684\u4e3b\u6210\u5206\uff0c\u5e76\u7528\u6837\u672c\u6807\u7b7e\u7684\u968f\u673a\u7f6e\u6362\u6765\u6d4b\u8bd5\u8fd9\u4e9b\u6210\u5206\u4e0e\u8868\u578b\u7684\u5173\u7cfb\u3002\u800cSuperPCA\u5219\u662f\u4e00\u79cd\u76d1\u7763\u65b9\u6cd5\uff0c\u5b83\u9009\u62e9\u4e0e\u75be\u75c5\u7ed3\u679c\u5bc6\u5207\u76f8\u5173\u7684\u57fa\u56e0\u5b50\u96c6\u6765\u4f30\u8ba1\u9014\u5f84\u7684\u6f5c\u5728\u53d8\u91cf\u3002\u7531\u4e8eSuperPCA\u6d89\u53ca\u57fa\u56e0\u9009\u62e9\uff0c\u5176\u7edf\u8ba1\u91cf\u7684\u5206\u5e03\u4e0d\u518d\u662f\u6807\u51c6\u7684t\u5206\u5e03\uff0c\u56e0\u6b64\u6211\u4eec\u4f7f\u7528Gumbel\u6781\u503c\u5206\u5e03\u7684\u6df7\u5408\u6a21\u578b\u6765\u4f30\u8ba1p\u503c\u3002<\/p>\n\n\n\n \u9274\u4e8eSuperPCA\u4e0eAES-PCA\u5728\u8bed\u6cd5\u4e0a\u7684\u76f8\u4f3c\u6027\uff0c\u6211\u4eec\u5c06\u91cd\u70b9\u4ecb\u7ecdAES-PCA\u7684\u5b9e\u65bd\u6b65\u9aa4\u3002\u9700\u6ce8\u610f\uff0c\u5f53numReps\u53c2\u6570\u8bbe\u7f6e\u4e3a\u5927\u4e8e0\u7684\u503c\u65f6\uff0cAESPCA_pvals()\u51fd\u6570\u4f1a\u91c7\u7528\u53c2\u6570\u5316\u65b9\u6cd5\u6765\u8bc4\u4f30\u9014\u5f84\u7684\u7edf\u8ba1\u663e\u8457\u6027\uff0c\u5177\u4f53\u6a21\u578b\u4e3a\uff1a\u201c\u8868\u578b ~ \u622a\u8ddd + \u7b2c\u4e00\u4e3b\u6210\u5206(PC1)\u201d\u3002\u9014\u5f84\u7684p\u503c\u901a\u8fc7\u4f3c\u7136\u6bd4\u68c0\u9a8c\u5f97\u51fa\uff0c\u8be5\u68c0\u9a8c\u5c06\u6240\u63d0\u6a21\u578b\u4e0e\u4ec5\u5305\u542b\u622a\u8ddd\u7684\u96f6\u5047\u8bbe\u6a21\u578b\u8fdb\u884c\u6bd4\u8f83\u3002<\/p>\n\n\n\n ovarian_aespcOut <- AESPCA_pVals(<\/p>\n\n\n\n # The Omics data container<\/p>\n\n\n\n object = ov_OmicsSurv,<\/p>\n\n\n\n # One principal component per pathway<\/p>\n\n\n\n numPCs = 1,<\/p>\n\n\n\n # Use parallel computing with 2 cores<\/p>\n\n\n\n parallel = TRUE,<\/p>\n\n\n\n numCores = 2,<\/p>\n\n\n\n # # Use serial computing<\/p>\n\n\n\n # parallel = FALSE,<\/p>\n\n\n\n # Estimate the p-values parametrically<\/p>\n\n\n\n numReps = 0,<\/p>\n\n\n\n # Control FDR via Benjamini-Hochberg<\/p>\n\n\n\n adjustment = “BH”<\/p>\n\n\n\n )<\/p>\n\n\n\n \u6700\u7ec8\u7ed3\u679c\u5c06\u5305\u62ec\u6bcf\u4e2a\u9014\u5f84\u7684p\u503c\u3001\u57fa\u4e8eAESPCA\u65b9\u6cd5\u4e3a\u6bcf\u4e2a\u6837\u672c\u4f30\u8ba1\u7684\u4e3b\u6210\u5206\u5f97\u5206\uff0c\u4ee5\u53ca\u5404\u86cb\u767d\u8d28\u5bf9\u8fd9\u4e9b\u4e3b\u6210\u5206\u7684\u8d21\u732e\u5ea6\u3002<\/p>\n\n\n\n \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528getPathpVals\u51fd\u6570\u6765\u83b7\u53d6AES-PCA\u5728\u5375\u5de2\u764c\u6570\u636e\u4e0a\u8bc6\u522b\u51fa\u7684\u524d10\u6761\u6700\u91cd\u8981\u7684\u901a\u8def\u3002<\/p>\n\n\n\n getPathpVals(ovarian_aespcOut, numPaths = 10)<\/p>\n\n\n\n \u5728\u6211\u4eec\u7ed8\u5236p\u503c\u56fe\u8868\u4e4b\u524d\uff0c\u4f1a\u5148\u7b5b\u9009\u51fa20\u4e2a\u6700\u91cd\u8981\u7684\u9014\u5f84\uff08\u9ed8\u8ba4\u8bbe\u7f6e\u4e2dnumPaths\u53c2\u6570\u503c\u4e3a20\uff09\u3002<\/p>\n\n\n\n ovOutGather_df <- getPathpVals(ovarian_aespcOut, score = TRUE)<\/p>\n\n\n\n \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4e3a\u8fd9\u4e9b\u9876\u5c16\u9014\u5f84\u7ed8\u5236\u5b83\u4eec\u7684\u91cd\u8981\u6027\u8bc4\u5206\u56fe\u3002\u56fe\u4e2d\u7684\u5206\u6570\u4ee3\u8868\u6bcf\u4e2a\u9014\u5f84\u539f\u59cbp\u503c\u7684\u8d1f\u81ea\u7136\u5bf9\u6570\uff0c\u4ee5\u6b64\u6765\u5c55\u793a\u5b83\u4eec\u5728\u7edf\u8ba1\u5b66\u4e0a\u7684\u610f\u4e49\u3002<\/p>\n\n\n\n ggplot(ovOutGather_df) +<\/p>\n\n\n\n # set overall appearance of the plot<\/p>\n\n\n\n theme_bw() +<\/p>\n\n\n\n # Define the dependent and independent variables<\/p>\n\n\n\n aes(x = reorder(terms, score), y = score) +<\/p>\n\n\n\n # From the defined variables, create a vertical bar chart<\/p>\n\n\n\n geom_col(position = “dodge”, fill = “#66FFFF”, width = 0.7) +<\/p>\n\n\n\n # Add pathway labels<\/p>\n\n\n\n geom_text(<\/p>\n\n\n\n aes(<\/p>\n\n\n\n x = reorder(terms, score),<\/p>\n\n\n\n label = reorder(description, score),<\/p>\n\n\n\n y = 0.1<\/p>\n\n\n\n ),<\/p>\n\n\n\n color = “black”,<\/p>\n\n\n\n size = 2,<\/p>\n\n\n\n hjust = 0<\/p>\n\n\n\n ) +<\/p>\n\n\n\n # Set main and axis titles<\/p>\n\n\n\n ggtitle(“AES-PCA Significant Pathways: Ovarian Cancer”) +<\/p>\n\n\n\n xlab(“Pathways”) +<\/p>\n\n\n\n ylab(“Negative LN (p-Value)”) +<\/p>\n\n\n\n # Add a line showing the alpha = 0.01 level<\/p>\n\n\n\n geom_hline(yintercept = -log(0.01), size = 1, color = “blue”) +<\/p>\n\n\n\n # Flip the x and y axes<\/p>\n\n\n\n coord_flip()<\/p>\n\n\n\n \u7531\u4e8e\u9014\u5f84\u662f\u9884\u5148\u8bbe\u5b9a\u7684\uff0c\u6bcf\u4e2a\u9014\u5f84\u4e2d\u901a\u5e38\u53ea\u6709\u90e8\u5206\u57fa\u56e0\u4e0e\u7279\u5b9a\u8868\u578b\u76f8\u5173\uff0c\u5e76\u5f71\u54cd\u9014\u5f84\u7684\u7edf\u8ba1\u663e\u8457\u6027\u3002\u5728AESPCA\u5206\u6790\u4e2d\uff0c\u6211\u4eec\u5173\u6ce8\u7684\u662f\u90a3\u4e9b\u5728\u7b2c\u4e00\u4e3b\u6210\u5206\u5206\u6790\uff08AESPCs\uff09\u4e2d\u5177\u6709\u975e\u96f6\u8f7d\u8377\u7684\u57fa\u56e0\uff0c\u56e0\u4e3a\u5b83\u4eec\u4e0e\u8868\u578b\u53d8\u5316\u6709\u76f4\u63a5\u8054\u7cfb\u3002<\/p>\n\n\n\n \u4ee5\u201cIL-1\u4fe1\u53f7\u9014\u5f84\u201d\uff08Wikipathways WP195\uff09\u4e3a\u4f8b\uff0c\u6211\u4eec\u80fd\u591f\u5229\u7528getPathPCLs()\u51fd\u6570\u6765\u63d0\u53d6\u76f8\u5173\u7684\u4e3b\u6210\u5206\u53ca\u5176\u5bf9\u5e94\u7684\u86cb\u767d\u8d28\u8f7d\u8377\u503c\u3002<\/p>\n\n\n\n wp195PCLs_ls <- getPathPCLs(ovarian_aespcOut, “WP195”)<\/p>\n\n\n\n \u975e\u96f6\u8f7d\u8377\u7684\u86cb\u767d\u8d28\u53ef\u4ee5\u7528\u4ee5\u4e0b\u65b9\u6cd5\u63d0\u53d6:<\/p>\n\n\n\n wp195Loadings_df <- wp195PCLs_ls$Loadings %>% filter(PC1 != 0)<\/p>\n\n\n\n \u540c\u65f6\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u8fd9\u4e9b\u8f7d\u8377\u6570\u636e\u6574\u7406\uff0c\u4ee5\u4fbf\u7528\u4e8e\u56fe\u5f62\u5316\u5c55\u793a\u3002<\/p>\n\n\n\n wp195Loadings_df <-<\/p>\n\n\n\n wp195Loadings_df %>%<\/p>\n\n\n\n # Sort Loading from Strongest to Weakest<\/p>\n\n\n\n arrange(desc(abs(PC1))) %>%<\/p>\n\n\n\n # Order the Genes by Loading Strength<\/p>\n\n\n\n mutate(featureID = factor(featureID, levels = featureID)) %>%<\/p>\n\n\n\n # Add Directional Indicator (for Colour)<\/p>\n\n\n\n mutate(Direction = factor(ifelse(PC1 > 0, “Up”, “Down”)))<\/p>\n\n\n\n \u73b0\u5728\u6211\u4eec\u5c06\u4f7f\u7528ggplot2\u7684geom_col()\u51fd\u6570\u6784\u9020\u4e00\u4e2a\u67f1\u72b6\u56fe\u3002<\/p>\n\n\n\n ggplot(data = wp195Loadings_df) +<\/p>\n\n\n\n # Set overall appearance<\/p>\n\n\n\n theme_bw() +<\/p>\n\n\n\n # Define the dependent and independent variables<\/p>\n\n\n\n aes(x = featureID, y = PC1, fill = Direction) +<\/p>\n\n\n\n # From the defined variables, create a vertical bar chart<\/p>\n\n\n\n geom_col(width = 0.5, fill = “#005030”, color = “#f47321”) +<\/p>\n\n\n\n # Set main and axis titles<\/p>\n\n\n\n labs(<\/p>\n\n\n\n title = “Gene Loadings on IL-1 Signaling Pathway”,<\/p>\n\n\n\n x = “Protein”,<\/p>\n\n\n\n y = “Loadings of PC1”<\/p>\n\n\n\n ) +<\/p>\n\n\n\n # Remove the legend<\/p>\n\n\n\n guides(fill = FALSE)<\/p>\n\n\n\n \u5728\u63a2\u7a76\u590d\u6742\u75be\u75c5\u65f6\uff0c\u7531\u4e8e\u75be\u75c5\u7684\u6210\u56e0\u53ca\u5bf9\u6cbb\u7597\u7684\u53cd\u5e94\u5728\u4e0d\u540c\u4e2a\u4f53\u95f4\u5b58\u5728\u663e\u8457\u5dee\u5f02\uff0c\u56e0\u6b64\u9664\u4e86\u4e3a\u6240\u6709\u60a3\u8005\u786e\u5b9a\u4e0e\u75be\u75c5\u76f8\u5173\u7684\u9014\u5f84\u5916\uff0c\u5236\u5b9a\u6709\u6548\u7684\uff08\u4e2a\u6027\u5316\u7684\uff09\u6cbb\u7597\u65b9\u6848\u4e5f\u9700\u8981\u4e86\u89e3\u7279\u5b9a\u9014\u5f84\u5728\u6bcf\u4e2a\u60a3\u8005\u8eab\u4e0a\u7684\u5f02\u5e38\u60c5\u51b5\u3002<\/p>\n\n\n\n \u4e3a\u4e86\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\uff0c\u6211\u4eec\u53ef\u4ee5\u8bc4\u4f30\u6bcf\u4e2a\u4e2a\u4f53\u7684\u9014\u5f84\u6d3b\u6027\u3002\u5982\u540c\u4e4b\u524d\u6240\u8ff0\uff0cgetPathPCLs()\u51fd\u6570\u4e0d\u4ec5\u80fd\u591f\u8fd4\u56de\u4e2a\u4f53\u9014\u5f84\u7684\u4e3b\u6210\u5206\u5206\u6790\u7ed3\u679c\uff0c\u8fd8\u80fd\u63d0\u4f9b\u4e2a\u4f53\u7279\u5b9a\u7684\u4f30\u8ba1\u503c\u3002<\/p>\n\n\n\n ggplot(data = wp195PCLs_ls$PCs) +<\/p>\n\n\n\n # Set overall appearance<\/p>\n\n\n\n theme_classic() +<\/p>\n\n\n\n # Define the independent variable<\/p>\n\n\n\n aes(x = V1) +<\/p>\n\n\n\n # Add the histogram layer<\/p>\n\n\n\n geom_histogram(bins = 10, color = “#005030”, fill = “#f47321”) +<\/p>\n\n\n\n # Set main and axis titles<\/p>\n\n\n\n labs(<\/p>\n\n\n\n title = “Distribution of Sample-specific Estimate of Pathway Activities”,<\/p>\n\n\n\n subtitle = paste0(wp195PCLs_ls$pathway, “: “, wp195PCLs_ls$description),<\/p>\n\n\n\n x = “PC1 Value for Each Sample”,<\/p>\n\n\n\n y = “Count”)<\/p>\n\n\n\n \u8fd9\u5f20\u56fe\u663e\u793a\u4e86\u4e0d\u540c\u60a3\u8005\u4e4b\u95f4\u5728\u9014\u5f84\u6d3b\u6027\u4e0a\u53ef\u80fd\u5b58\u5728\u76f8\u5f53\u5927\u7684\u5dee\u5f02\u3002<\/p>\n\n\n\n \u7814\u7a76\u8005\u4eec\u7ecf\u5e38\u5e0c\u671b\u6df1\u5165\u4e86\u89e3\u9876\u7ea7\u9014\u5f84\u5206\u6790\u80cc\u540e\u7684\u5177\u4f53\u6570\u636e\uff0c\u5c24\u5176\u662f\u90a3\u4e9b\u4e0e\u9014\u5f84\u76f4\u63a5\u76f8\u5173\u7684\u7279\u5b9a\u57fa\u56e0\u3002\u901a\u8fc7SubsetPathwayData()\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u63d0\u53d6\u8fd9\u4e9b\u6570\u636e\u3002\u4ee5\u4e0b\u6b65\u9aa4\u5c55\u793a\u4e86\u5982\u4f55\u63d0\u53d6\u6700\u5177\u7edf\u8ba1\u610f\u4e49\u7684\u9014\u5f84\uff08\u4f8b\u5982IL-1\u4fe1\u53f7\u9014\u5f84\uff09\u7684\u6570\u636e\uff1a<\/p>\n\n\n\n wp195Data_df <- SubsetPathwayData(ov_OmicsSurv, “WP195”)<\/p>\n\n\n\n \u4e3a\u4e86\u5236\u4f5c\u8be5\u9014\u5f84\u5185\u86cb\u767d\u8d28\u7684\u70ed\u56fe\uff0c\u6211\u4eec\u9996\u5148\u9700\u8981\u5c06\u6570\u636e\u6574\u7406\u6210\u4e09\u4e2a\u4e3b\u8981\u5217\u3002<\/p>\n\n\n\n wp195gather_df <- wp195Data_df %>% arrange(EventTime) %>% select(-EventTime, -EventObs) %>% gather(protein, value, -sampleID)<\/p>\n\n\n\n \u73b0\u5728\uff0c\u8ba9\u6211\u4eec\u6765\u7ed8\u5236\u70ed\u56fe\uff1a<\/p>\n\n\n\n ggplot(wp195gather_df, aes(x = protein, y = sampleID)) + geom_tile(aes(fill = value)) + scale_fill_gradient2(low = “red”, mid = “black”, high = “green”) + labs(x = “Proteins”, y = “Subjects”, fill = “Protein level”) +theme(axis.text.x = element_text(angle = 90)) + coord_flip()<\/p>\n\n\n\n \u6211\u4eec\u8fd8\u53ef\u4ee5\u5bf9\u9014\u5f84\u5185\u7684\u5355\u4e2a\u57fa\u56e0\u8fdb\u884c\u6df1\u5165\u5206\u6790\uff1a<\/p>\n\n\n\n library(survival)<\/p>\n\n\n\n NFKB1_df <- wp195Data_df %>% select(EventTime, EventObs, NFKB1)<\/p>\n\n\n\n wp195_mod <- coxph(Surv(EventTime, EventObs) ~ NFKB1,data = NFKB1_df)<\/p>\n\n\n\n summary(wp195_mod)<\/p>\n\n\n\n \u6b64\u5916\uff0c\u6211\u4eec\u80fd\u591f\u4e3a\u5355\u4e2a\u57fa\u56e0\u7684\u9ad8\u8868\u8fbe\u6216\u4f4e\u8868\u8fbe\u60a3\u8005\u7684\u7fa4\u4f53\u4f30\u8ba1Kaplan-Meier\u751f\u5b58\u66f2\u7ebf\uff1a<\/p>\n\n\n\n # Add the direction<\/p>\n\n\n\n NFKB1_df <-NFKB1_df %>%<\/p>\n\n\n\n # Group subjects by gene expression<\/p>\n\n\n\n mutate(NFKB1_Expr = ifelse(NFKB1 > median(NFKB1), “High”, “Low”)) %>%<\/p>\n\n\n\n # Re-code time to years<\/p>\n\n\n\n mutate(EventTime = EventTime \/ 365.25) %>%<\/p>\n\n\n\n # Ignore any events past 10 years<\/p>\n\n\n\n filter(EventTime <= 10)<\/p>\n\n\n\n # Fit the survival model<\/p>\n\n\n\n NFKB1_fit <- survfit(Surv(EventTime, EventObs) ~ NFKB1_Expr, data = NFKB1_df)<\/p>\n\n\n\n \u6700\u7ec8\uff0c\u6211\u4eec\u53ef\u4ee5\u5728NFKB1\u86cb\u767d\u7684\u8868\u8fbe\u6c34\u5e73\u4e0a\u7ed8\u5236\u8fd9\u4e9b\u751f\u5b58\u66f2\u7ebf\u3002<\/p>\n\n\n\n library(survminer)<\/p>\n\n\n\n ggsurvplot(NFKB1_fit,conf.int = FALSE, pval = TRUE,xlab = “Time in Years”,palette = c(“#f47321”, “#005030”),xlim = c(0, 10))<\/p>\n\n\n\n \u8fd9\u5f20\u56fe\u5c55\u793a\u4e86\u6839\u636eNFKB1\u86cb\u767d\u8868\u8fbe\u6c34\u5e73\uff08\u9ad8\u6216\u4f4e\uff09\u5206\u7ec4\u7684\u60a3\u8005\u7684Kaplan-Meier\u751f\u5b58\u66f2\u7ebf\u3002\u4ece\u56fe\u4e2d\u53ef\u4ee5\u770b\u51fa\uff0c\u9ad8\u8868\u8fbe\u7ec4\uff08NFKB1_Expr=High\uff09\u548c\u4f4e\u8868\u8fbe\u7ec4\uff08NFKB1_Expr=Low\uff09\u7684\u751f\u5b58\u7387\u6709\u663e\u8457\u5dee\u5f02\uff0c\u8fd9\u8868\u660eNFKB1\u53ef\u80fd\u662f\u5f71\u54cd\u75be\u75c5\u9884\u540e\u7684\u5173\u952e\u751f\u7269\u6807\u5fd7\u7269\u3002<\/p>\n\n\n\n \u4f19\u4f34\u4eec\uff0c\u5c0f\u5e08\u59b9\u5df2\u7ecf\u5e26\u9886\u5927\u5bb6\u4e00\u8d77\u5b8c\u6210\u4e86pathwayPCA\u7684\u521d\u6b65\u63a2\u7d22\u3002\u73b0\u5728\uff0c\u4f60\u53ef\u4ee5\u5f00\u59cb\u81ea\u5df1\u52a8\u624b\uff0c\u5229\u7528pathwayPCA\u5305\u6765\u5206\u6790\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u5566\uff0c\u5305\u62ec\u6d4b\u8bd5\u9014\u5f84\u4e0e\u8868\u578b\u4e4b\u95f4\u7684\u5173\u8054\u3001\u63d0\u53d6\u76f8\u5173\u57fa\u56e0\u3001\u8ba1\u7b97\u4ee3\u8868\u4e2a\u4f53\u6837\u672c\u9014\u5f84\u7684\u4e3b\u6210\u5206\u7b49\u3002\u5728\u4f7f\u7528\u8fc7\u7a0b\u4e2d\uff0c\u8bb0\u5f97\u5145\u5206\u5229\u7528pathwayPCA\u63d0\u4f9b\u7684\u56fe\u5f62\u51fd\u6570\uff0c\u5b83\u53ef\u4ee5\u8ba9\u4f60\u7684\u5206\u6790\u8fc7\u7a0b\u66f4\u52a0\u76f4\u89c2\u548c\u9ad8\u6548\uff01<\/p>\n\n\n\n \u65e0\u8bba\u4f60\u662f\u5728\u4f18\u5316\u4ee3\u7801\uff0c\u8fd8\u662f\u5728\u4e91\u7aef\u8fdb\u884c\u4fbf\u6377\u7684\u5206\u6790\uff0c\u4e91\u751f\u4fe1\u795e\u5668\u90fd\u80fd\u4e3a\u4f60\u63d0\u4f9b\u5f3a\u5927\u7684\u652f\u6301\u3002\u6b22\u8fce\u8bd5\u8bd5\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u795e\u5668\uff0c\u53ea\u9700\u4e00\u952e\u4e0a\u4f20\u6570\u636e\uff0c\u60f3\u8981\u7684\u56fe\u5c31\u80fd\u8f7b\u677eget~<\/strong><\/strong><\/p>\n\n\n\n\n
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gitHubPath_char <- “https:\/\/raw.githubusercontent.com\/lizhongliu1996\/pathwayPCAdata\/master\/”<\/p>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/10\/1728614782222_602CB872-C17D-4791-BE58-3DE5832961E5.png?resize=428%2C102\")
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