![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28200-1.png?resize=640%2C162\")
<\/p>\n\u6211\u4eec\u751f\u4fe1\u4eba\u5728\u505a\u5206\u6790\u7684\u65f6\u5019\uff0c\u5982\u679c\u9047\u5230\u7684\u662f\u5355\u4e00\u7c7b\u578b\u7684\u6570\u636e\uff0c\u5982\u7532\u57fa\u5316\u6570\u636e\uff0c\u8f6c\u5f55\u7ec4\u5b66\u6570\u636e\u3001\u4ee3\u8c22\u7ec4\u5b66\u6570\u636e\u7b49\uff0c\u6211\u4eec\u53ef\u80fd\u662f\u624b\u62ff\u628a\u6390\uff0c\u4fe1\u624b\u634f\u6765\u3002\u5982\u679c\u6211\u4eec\u9047\u5230\u7684\u662f\u591a\u79cd\u7ec4\u5b66\u6570\u636e\u5462\uff0c\u6211\u4eec\u53ef\u80fd\u8fd8\u662f\u6bcf\u4e00\u7c7b\u6570\u636e\u90fd\u7ed9\u4ed6\u5355\u4e00\u5206\u6790\u4e00\u904d\uff0c\u54fc\u54e7\u54fc\u54e7\u5206\u6790\u4e00\u987f\uff0c\u6bcf\u4e2a\u7c7b\u578b\u90fd\u6309\u7167\u5e38\u89c4\u6d41\u7a0b\u5206\u6790\u51fa\u6765\uff0c\u5f53\u7136\u53ef\u4ee5\uff0c\u628a\u7ed3\u679c\u4e00\u7f57\u5217\uff0c\u6211\u4eec\u7684\u5206\u6790\u5185\u5bb9\u4e5f\u4f1a\u663e\u5f97\u5f88\u4e30\u5bcc\u3002\u4f46\u662f\uff0c\u5927\u5bb6\u6709\u6ca1\u6709\u60f3\u8fc7\uff0c\u8fd9\u6837\u5355\u4e00\u7684\u5206\u6790\u4f1a\u5ffd\u7565\u4e0d\u540c\u7279\u5f81\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u5f88\u6709\u53ef\u80fd\u56e0\u6b64\u5ffd\u7565\u5173\u952e\u7684\u4fe1\u606f\uff01\u96be\u9053\u6211\u4eec\u5c31\u4e0d\u80fd\u591a\u7ec4\u5b66\u6570\u636e\u5171\u540c\u5206\u6790\u5417\uff1fmixOmics\u5e2e\u6211\u4eec\u89e3\u51b3\u4e86\u8fd9\u4e48\u4e00\u5927\u95ee\u9898\uff01\u5b83\u53ef\u4ee5\u8ba9\u6211\u4eec\u5c06\u4e0d\u540c\u7684\u7ec4\u5b66\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u540e\u5171\u540c\u5206\u6790\uff0c\u5e76\u5c06\u95ee\u9898\u4e3b\u8981\u653e\u5728\u7279\u5f81\u9009\u62e9\u4e0a\uff0c\u5176\u5728\u591a\u7ec4\u5b66\u9886\u57df\u77e5\u540d\u5ea6\u4e5f\u662f\u5f88\u9ad8\uff0c\u6587\u7ae0\u5f15\u7528\u91cf\u4e5f\u5f88\u9ad8\uff0c\u4e0b\u56fe\u662f\u8be5R\u5305\u7684\u6587\u7ae0\uff1a<\/p>\n
<\/p>\n
\u6587\u4e2d\u6307\u51fa\uff0c\u7814\u7a76\u56e2\u961f\u63a8\u51fa\u7684mixOmics\uff0c\u662f\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u751f\u7269\u6570\u636e\u96c6\u591a\u53d8\u91cf\u5206\u6790\u7684R\u5305\uff0c\u7279\u522b\u5173\u6ce8\u6570\u636e\u63a2\u7d22\u3001\u964d\u7ef4\u548c\u53ef\u89c6\u5316\u3002\u901a\u8fc7\u91c7\u7528\u7cfb\u7edf\u751f\u7269\u5b66\u65b9\u6cd5\uff0c\u8be5\u5de5\u5177\u5305\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u540c\u65f6\u7edf\u8ba1\u6574\u5408\u591a\u4e2a\u6570\u636e\u96c6\uff0c\u4ee5\u63a2\u7d22\u5f02\u6784\u201c\u7ec4\u5b66\u201d\u6570\u636e\u96c6\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u7b80\u800c\u8a00\u4e4b\uff0cmixOmics\u4e3b\u8981\u6709\u4e09\u79cd\u5206\u6790\u6a21\u5f0f\uff1a<\/p>\n
\uff081\uff09\u5728\u9762\u5bf9\u5355\u7c7b\u7ec4\u5b66\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528PCA\uff0csPCA\u8fdb\u884c\u964d\u7ef4\u4ee5\u53ca\u7279\u5f81\u7b5b\u9009\uff0c\u800c\u9762\u5bf9\u6709\u76d1\u7763\u7684\u6570\u636e\u7c7b\u578b\u65f6\uff0c\u53ef\u4ee5\u8f6c\u800c\u4f7f\u7528PLS-DA\uff0csPLS-DA\u5728\u8003\u8651\u6837\u672c\u5206\u7c7b\u4fe1\u606f\u7684\u60c5\u51b5\u4e0b\u5c06\u6837\u672c\u6295\u5c04\u5230\u4f4e\u7ef4\u7a7a\u95f4\u4e2d\u5e76\u8fdb\u884c\u5206\u6790\u3002<\/p>\n
\uff082\uff09\u800c\u9762\u5bf9\u591a\u79cd\u4e0d\u540c\u7c7b\u578b\u7ec4\u5b66\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528N-\u96c6\u6210\uff08DIABLO\uff09\u5c06\u4e0d\u540c\u6570\u636e\u8054\u5408\u8d77\u6765\uff0c\u4f8b\u5982\u57fa\u56e0\u7ec4\uff0cmRNA\u6570\u636e\uff0c\u86cb\u767d\u7ec4\u5b66\u4ee5\u53ca\u4ee3\u8c22\u7ec4\u5b66\u7684\u8054\u5408\u5206\u6790\u3002<\/p>\n
\uff083\uff09\u9762\u5bf9\u591a\u7ec4\u76f8\u540c\u7c7b\u578b\u7ec4\u5b66\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528P-\u96c6\u6210\uff08MINT\uff09\u65b9\u6cd5\uff0c\u5982\u5bf9\u4e0d\u540c\u7684\u86cb\u767d\u7ec4\u5b66\u6570\u636e\u8fdb\u884c\u8054\u5408\u5206\u6790\u3002<\/p>\n
\u4eca\u5929\u5c0f\u679c\u4f1a\u7ed9\u5927\u5bb6\u5c55\u793a\u8be5\u9879\u76ee\u7b2c\u4e00\u79cd\u5206\u6790\u6a21\u5f0f\u7684\u793a\u4f8b\u6570\u636e\u53ca\u4ee3\u7801\uff0c\u4e5f\u662f\u6211\u4eec\u6700\u5e38\u7528\u7684\u4e00\u79cd\u60c5\u51b5\u3002\u7279\u522b\u63d0\u793a\uff0c\u672c\u6b21\u64cd\u4f5c\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\u6b22\u8fce\u8054\u7cfb\u6211\u4eec\u6765\u79df\u8d41\u670d\u52a1\u5668\u54e6\uff5e<\/p>\n
\u6b63\u5f0f\u8bb2\u89e3\u6d41\u7a0b\u4e4b\u524d\uff0c\u5927\u5bb6\u5148\u770b\u4e00\u4e0b\u4e09\u79cd\u5206\u6790\u7684\u793a\u610f\u56fe\uff0c\u8be5\u793a\u610f\u56fe\u5f88\u76f4\u89c2\u5730\u5c55\u793a\u4e86\u4e09\u79cd\u6570\u636e\u7c7b\u578b\uff0c\u65b9\u4fbf\u5927\u5bb6\u8fdb\u884c\u7406\u89e3\uff0c\u5982\u4e0b\u56fe\uff1a<\/p>\n
![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28200-2.png?resize=640%2C405\")
<\/p>\n
\u53ef\u4ee5\u770b\u5230\uff0c\u5de6\u8fb9\u662f\u5355\u4e00\u7ec4\u5b66\u6570\u636e\uff0c\u6839\u636e\u662f\u5426\u6709\u5206\u7c7b\u6807\u7b7e\u5bf9\u5e94\u7740\u4e0d\u540c\u7684\u5206\u6790\u7b56\u7565\uff0c\u4e2d\u95f4\u662f\u4e0d\u540c\u7ec4\u5b66\u6570\u636e\u8054\u5408\uff0c\u8fdb\u884c\u9884\u5904\u7406\u540e\u540c\u6837\u6839\u636e\u6807\u7b7e\u6709\u65e0\u6765\u8fdb\u884c\u5bf9\u5e94\u7684\u5206\u6790\uff0c\u53f3\u8fb9\u662f\u76f8\u540c\u7ec4\u5b66\u6570\u636e\u7684\u8054\u5408\u5206\u6790\u3002\u6211\u4eec\u53ef\u4ee5\u53d1\u73b0\uff0c\u8be5\u5de5\u5177\u5305\u57fa\u672c\u5305\u542b\u4e86\u6211\u4eec\u5904\u7406\u7ec4\u5b66\u6570\u636e\u7684\u6240\u6709\u60c5\u51b5\uff0c\u90a3\u4e48\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u7ed9\u5927\u5bb6\u5c55\u793a\u4e00\u4e0b\u8be5\u5de5\u5177\u5305\u7684\u7b80\u5355\u64cd\u4f5c\uff0c\u4ee5\u53ca\u5355\u4e00\u7ec4\u5b66\u6570\u636e\u7684\u793a\u4f8b\u5206\u6790\u3002\u5176\u4ed6\u4e24\u79cd\u6a21\u5f0f\u5927\u5bb6\u53ef\u4ee5\u767b\u9646\u5b98\u65b9\u6587\u6863\u81ea\u884c\u67e5\u770b\u54e6\uff5ehttp:\/\/mixomics.org\/<\/p>\n
\u5728\u4f7f\u7528\u8be5R\u5305\u5206\u6790\u6570\u636e\u4e4b\u524d\uff0c\u8bf7\u6ce8\u610f\u6570\u636e\u9884\u5904\u7406\u8981\u6c42\uff1a<\/p>\n
\u7b80\u800c\u8a00\u4e4b\uff0c\u5c31\u662f\u9700\u8981\u6211\u4eec\u63d0\u524d\u5c06\u6211\u4eec\u7684\u6570\u636e\u8fdb\u884c\u5904\u7406\uff0c\u8fd9\u548c\u6211\u4eec\u7684\u5e38\u89c4\u6d41\u7a0b\u5904\u7406\u6570\u636e\u5176\u5b9e\u662f\u4e00\u6837\u7684\u3002<\/p>\n
\u9996\u5148\uff0c\u7ed9\u5927\u5bb6\u5c55\u793a\u4e00\u4e0b\u5982\u4f55\u5feb\u901f\u5b89\u88c5\u3001\u4f7f\u7528\u8fd9\u4e2aR\u5305\u3002<\/p>\n
2.1\u5b89\u88c5<\/p>\n
\u9996\u5148\uff0c\u4eceBioconductor\u4e0b\u8f7d\u6700\u65b0\u7248\u672c\uff1a<\/p>\n
if (!requireNamespace(“BiocManager”, quietly = TRUE))<\/p>\n
install.packages(“BiocManager”)<\/p>\n
BiocManager::install(“mixOmics”)<\/p>\n
\u6216\u8005\u4eceGitHub\u5b89\u88c5\uff1a<\/p>\n
BiocManager::install(“mixOmicsTeam\/mixOmics”)<\/p>\n
# \u8be5mixOmics\u5305\u5e94\u4f1a\u76f4\u63a5\u5bfc\u5165\u4ee5\u4e0b\u5305\uff1aigraph, rgl, ellipse, corpcor, RColorBrewer, plyr, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra\u3002\u5bf9\u4e8eApple mac\u7528\u6237\uff1a\u5982\u679c\u65e0\u6cd5\u5b89\u88c5\u5bfc\u5165\u7684\u5305rgl\uff0c\u5219\u9700\u8981\u5148\u5b89\u88c5XQuartz\u8f6f\u4ef6<\/a>\u3002<\/p>\n 2.2\u52a0\u8f7d\u5305<\/p>\n library(mixOmics)<\/p>\n # \u68c0\u67e5\u52a0\u8f7d\u5305\u65f6\u662f\u5426\u6709\u9519\u8bef\uff0c\u5c24\u5176\u662frgl\u5e93\uff08\u89c1\u4e0a\u6587\uff09<\/p>\n 2.3\u52a0\u8f7d\u6570\u636e<\/p>\n \u4e0b\u9762\u6b65\u9aa4\u7ed9\u51fa\u7684\u793a\u4f8b\u4f7f\u7528\u7684\u6570\u636e\u5df2\u7ecf\u662f\u5305\u7684\u4e00\u90e8\u5206\u3002\u8981\u4e0a\u4f20\u60a8\u81ea\u5df1\u7684\u6570\u636e\uff0c\u8bf7\u9996\u5148\u68c0\u67e5\u60a8\u7684\u5de5\u4f5c\u76ee\u5f55\u662f\u5426\u5df2\u8bbe\u7f6e\uff0c\u7136\u540e\u901a\u8fc7\u4f7f\u7528RStudio \u4e2d\u7684\u201c\u6587\u4ef6\u201d>\u201c\u5bfc\u5165\u6570\u636e\u96c6\u201d\u6216\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u884c\u4e4b\u4e00\u4ee5.txt\u6216.csv\u8bfb\u53d6\u6570\u636e\u3002<\/p>\n # from csv file<\/p>\n data <- read.csv(“your_data.csv”, row.names = 1, header = TRUE)<\/p>\n # from txt file<\/p>\n data <- read.table(“your_data.txt”, header = TRUE)<\/p>\n 2.4\u5feb\u901f\u542f\u52a8mixOmics<\/p>\n \u6bcf\u4e2a\u5206\u6790\u90fd\u5e94\u9075\u5faa\u4ee5\u4e0b\u5de5\u4f5c\u6d41\u7a0b\uff1a<\/p>\n \uff081\uff09\u8fd0\u884c\u8be5\u65b9\u6cd5<\/p>\n \uff082\uff09\u6837\u54c1\u7684\u56fe\u5f62\u8868\u793a<\/p>\n \uff083\uff09\u53d8\u91cf\u7684\u56fe\u5f62\u8868\u793a<\/p>\n \u4f8b\u5982\uff0c\u5bf9\u4e8e\u4e3b\u6210\u5206\u5206\u6790\uff0c\u6211\u4eec\u9996\u5148\u52a0\u8f7d\u6570\u636e\uff1a<\/p>\n data(nutrimouse) #R\u5305\u81ea\u5e26\u793a\u4f8b\u6570\u636e<\/p>\n X <- nutrimouse$gene<\/p>\n \u7136\u540e\u4f7f\u7528\u4ee5\u4e0b\u6b65\u9aa4\uff1a<\/p>\n MyResult.pca <- pca(X) # 1 \u8fd0\u884cPCA\u65b9\u6cd5<\/p>\n plotIndiv(MyResult.pca) # 2 \u7ed8\u5236\u6837\u672c\u56fe<\/p>\n plotVar(MyResult.pca) # 3 \u7ed8\u5236\u53d8\u91cf\u56fe<\/p>\n \u6309\u7167\u6211\u4eec\u6b64\u5904\u7684\u793a\u4f8b\uff0c\u53ef\u4ee5\u5e94\u7528\u7a00\u758f PCA \u6765\u9009\u62e9\u5bf9 PCA \u4e2d\u7684\u4e24\u4e2a\u5206\u91cf\u505a\u51fa\u8d21\u732e\u7684\u524d 5 \u4e2a\u53d8\u91cf\u3002\u7528\u6237\u6307\u5b9a\u5728\u6bcf\u4e2a\u7ec4\u4ef6\u4e0a\u9009\u62e9\u7684\u53d8\u91cf\u6570\u91cf\uff0c\u4f8b\u5982\uff0c\u8fd9\u91cc\u5728\u524d\u4e24\u4e2a\u7ec4\u4ef6\u4e0a\u5206\u522b\u9009\u62e9 5 \u4e2a\u53d8\u91cf (keepX=c(5,5))\uff1a<\/p>\n MyResult.spca <- spca(X, keepX=c(5,5)) # 1 \u8fd0\u884cPCA\u65b9\u6cd5<\/p>\n plotIndiv(MyResult.spca) # 2 \u7ed8\u5236\u6837\u672c\u56fe<\/p>\n plotVar(MyResult.spca) # 3 \u7ed8\u5236\u53d8\u91cf\u56fe<\/p>\n \u53ef\u4ee5\u770b\u5230\u6211\u4eec\u5df2\u7ecf\u5927\u5927\u51cf\u5c11\u4e86\u76f8\u5173\u5706\u56fe\u4e2d\u7684\u57fa\u56e0\u6570\u91cf<\/p>\n \u4e3a\u4e86\u6f14\u793aPCA\u548c\u5176\u53d8\u4f53\uff0c\u6211\u4eec\u5c06\u5206\u6790multidrug\u5305\u4e2d\u63d0\u4f9b\u7684\u6848\u4f8b\u7814\u7a76\u3002\u8fd9\u9879\u836f\u7269\u57fa\u56e0\u7ec4\u5b66\u7814\u7a76\u8c03\u67e5\u4e86\u764c\u7ec6\u80de\u7cfb\u4e2d\u7684\u836f\u7269\u6d3b\u6027\u6a21\u5f0f\u3002\u8fd9\u4e9b\u7ec6\u80de\u7cfb\u6765\u81ea\u7f8e\u56fd\u56fd\u5bb6\u764c\u75c7\u7814\u7a76\u6240(NCI)\u53d1\u80b2\u6cbb\u7597\u8ba1\u5212\u5efa\u7acb\u7684NCI-60\u4eba\u7c7b\u80bf\u7624\u7ec6\u80de\u7cfb\uff0c<\/a>\u7528\u4e8e\u7b5b\u9009\u4e0d\u540c\u764c\u7ec6\u80de\u7cfb\u4e2d\u5316\u5b66\u5316\u5408\u7269\u5e93\u7684\u6bd2\u6027\u3002NCI-60\u5305\u62ec\u6e90\u81ea\u7ed3\u76f4\u80a0\u764c\uff087\u4e2a\u7ec6\u80de\u7cfb\uff09\u3001\u80be\u764c\uff088\u4e2a\u7ec6\u80de\u7cfb\uff09\u3001\u5375\u5de2\u764c\uff086\u4e2a\u7ec6\u80de\u7cfb\uff09\u3001\u4e73\u817a\u764c\uff088\u4e2a\u7ec6\u80de\u7cfb\uff09\u3001\u524d\u5217\u817a\u764c\uff082\u4e2a\u7ec6\u80de\u7cfb\uff09\u3001\u80ba\u764c\uff089\u4e2a\u7ec6\u80de\u7cfb\uff09\u548c\u4e2d\u67a2\u795e\u7ecf\u7cfb\u7edf\u6765\u6e90\u7684\u764c\u75c7\uff086\u4e2a\u7ec6\u80de\u7cfb\uff09\uff0c\u4ee5\u53ca\u767d\u8840\u75c5\uff086\u4e2a\u7ec6\u80de\u7cfb\uff09\u548c\u9ed1\u8272\u7d20\u7624\uff088\u4e2a\u7ec6\u80de\u7cfb\uff09\u3002<\/p>\n \u6211\u4eec\u6f14\u793a\u4e3b\u8981\u4f7f\u7528multidrug\u4e2d\u7684\u4ee5\u4e0b\u6570\u636e\uff1a<\/p>\n $ABC.trans\uff1a\u5305\u542b\u901a\u8fc7\u5b9a\u91cf\u5b9e\u65f6 PCR (RT-PCR) \u5bf9\u6bcf\u4e2a\u7ec6\u80de\u7cfb\u6d4b\u91cf\u7684 48 \u79cd\u4eba\u7c7b ABC \u8f6c\u8fd0\u86cb\u767d\u7684\u8868\u8fbe\u3002<\/p>\n $compound\uff1a\u5305\u542b 1,429 \u79cd\u836f\u7269\u7684\u6d3b\u6027\uff0c\u4ee5 GI50 \u8868\u793a\uff0c\u5373\u5bf9\u6d4b\u8bd5\u7ec6\u80de\u7cfb\u7684\u7ec6\u80de\u751f\u957f\u4ea7\u751f 50% \u6291\u5236\u7684\u836f\u7269\u6d53\u5ea6\u3002<\/p>\n $comp.name\uff1a 1,429 \u79cd\u5316\u5408\u7269\u7684\u540d\u79f0\u3002<\/p>\n $cell.line\uff1a\u6709\u5173\u7ec6\u80de\u7cfb\u540d\u79f0 (\u00a0$Sample) \u548c\u7ec6\u80de\u7cfb\u7c7b\u578b (\u00a0$Class) \u7684\u4fe1\u606f\u3002<\/p>\n 3.1\u52a0\u8f7d\u6570\u636e<\/p>\n \u8f93\u5165\u7684\u6570\u636e\u662f\u77e9\u9635\u7c7b\u578b\uff0c\u5176\u4e2d\u884c\u540d\u4e3a\u6bcf\u4e2a\u6837\u672c\u7684\u540d\u79f0\uff0c\u5217\u540d\u662f\u53d8\u91cf\u7684\u540d\u79f0\uff08\u4f8b\u5982\u57fa\u56e0\uff09\uff0c\u6211\u4eec\u4ece\u6570\u636e$ABC.trans\u5f00\u59cb\uff1a<\/p>\n library(mixOmics)<\/p>\n data(multidrug)<\/p>\n X <- multidrug$ABC.trans<\/p>\n dim(X) # \u68c0\u67e5\u6570\u636e\u7684\u7ef4\u5ea6<\/p>\n \u7ed3\u679c\uff1a<\/p>\n ## [1] 60 48<\/p>\n 3.2\u4e3b\u6210\u5206\u5206\u6790<\/p>\n 3.2.1\u9009\u62e9\u4e3b\u6210\u5206\u6570\u91cf<\/p>\n tune.pca()\u8ba1\u7b97\u5927\u91cf\u4e3b\u6210\u5206\u7684\u89e3\u91ca\u65b9\u5dee\u7684\u7d2f\u79ef\u6bd4\u4f8b\uff08\u8fd9\u91cc\u6211\u4eec\u8bbe\u7f6encomp = 10\uff09\u3002\u8f93\u51fa\u89e3\u91ca\u65b9\u5dee\u76f8\u5bf9\u4e8e\u6bcf\u4e2a\u4e3b\u6210\u5206\u6570\u636e\u65b9\u5dee\u603b\u91cf\u7684\u6bd4\u4f8b\u7684\u7ec6\u7ebf\u56fe\uff1a<\/p>\n tune.pca.multi <- tune.pca(X, ncomp = 10, scale = TRUE)<\/p>\n plot(tune.pca.multi)<\/p>\n \u56fe\u793a\u4e3aABC \u8f6c\u8fd0\u86cb\u767d\u6570\u636e\u4e0a\u6bcf\u4e2a\u4e3b\u6210\u5206\u7684\u89e3\u91ca\u65b9\u5dee\u91cf<\/p>\n tune.pca.multidrug$cum.var # \u8f93\u51fa\u7d2f\u8ba1\u5dee\u5f02\u6bd4\u4f8b<\/p>\n \u6211\u4eec\u6839\u636e\u4e0a\u56fe\u7684\u4e0b\u964d\u8d8b\u52bf\uff0c\u6216\u8005\u6839\u636e\u4e0a\u4e00\u6761\u7ed9\u51fa\u4ee3\u7801\u8ba1\u7b97\u7684\u5177\u4f53\u6bd4\u4f8b\u6570\u503c\uff0c\u786e\u5b9a\u524d\u4e24\u4e2a\u6216\u8005\u524d\u4e09\u4e2a\u4e3b\u6210\u5206\u53ef\u4ee5\u4fdd\u7559\u4e0b\u6765\u3002<\/p>\n 3.2.2\u8fd0\u884c\u4e09\u4e2a\u4e3b\u6210\u5206\u7684PCA<\/p>\n final.pca.multi <- pca(X, ncomp = 3, center = TRUE, scale = TRUE)<\/p>\n \u8fd9\u91cc\u6570\u636e\u7684\u603b\u65b9\u5dee\u662f\uff1a<\/p>\n final.pca.multi$var.tot<\/p>\n \u7ed3\u679c\uff1a<\/p>\n ## [1] 47.98305<\/p>\n \u901a\u8fc7\u5bf9\u6240\u6709\u53ef\u80fd\u5206\u91cf\u89e3\u91ca\u7684\u65b9\u5dee\u6c42\u548c\uff0c\u6211\u4eec\u5c06\u83b7\u5f97\u76f8\u540c\u6570\u91cf\u7684\u89e3\u91ca\u65b9\u5dee\u3002\u6bcf\u4e2a\u5206\u91cf\u7684\u89e3\u91ca\u65b9\u5dee\u6bd4\u4f8b\u4e3a\uff1a<\/p>\n final.pca.multi$prop_expl_var$X<\/p>\n ## PC1 PC2 PC3<\/p>\n ## 0.12677541 0.10194929 0.07011818<\/p>\n \u8fd8\u53ef\u4ee5\u63d0\u53d6\u89e3\u91ca\u7684\u65b9\u5dee\u7d2f\u79ef\u6bd4\u4f8b\uff1a<\/p>\n final.pca.multi$cum.var<\/p>\n \u7ed3\u679c\uff1a<\/p>\n ## PC1 PC2 PC3<\/p>\n ## 0.1267754 0.2287247 0.2988429<\/p>\n 3.2.3\u8bc6\u522b\u6709\u4ef7\u503c\u7684\u53d8\u91cf<\/p>\n \u4e3a\u4e86\u8ba1\u7b97\u5206\u91cf\uff0c\u6211\u4eec\u4f7f\u7528\u52a0\u8f7d\u5411\u91cf\u4e2d\u6307\u793a\u7684\u53ef\u53d8\u7cfb\u6570\u6743\u91cd\u3002\u56e0\u6b64\uff0c\u52a0\u8f7d\u5411\u91cf\u4e2d\u7cfb\u6570\u7684\u7edd\u5bf9\u503c\u544a\u8bc9\u6211\u4eec\u6bcf\u4e2a\u53d8\u91cf\u5bf9\u6bcf\u4e2a\u5206\u91cf\u7684\u5b9a\u4e49\u7684\u91cd\u8981\u6027\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u51fd\u6570\u63d0\u53d6\u6b64\u4fe1\u606f\uff0cselectVar()\u8be5\u51fd\u6570\u6839\u636e\u6bcf\u4e2a\u4e3b\u6210\u5206\u7684\u7edd\u5bf9\u8d1f\u8f7d\u6743\u91cd\u503c\u6309\u964d\u5e8f\u6392\u5217\u6700\u91cd\u8981\u7684\u53d8\u91cf\u3002<\/p>\n #\u53ea\u6839\u636e\u7b2c\u4e00\u4e3b\u6210\u5206\u6392\u5e8f\u53d8\u91cf:<\/p>\n head(selectVar(final.pca.multi, comp = 1)$value)<\/p>\n \u7ed3\u679c\uff1a<\/p>\n ## value.var<\/p>\n ## ABCE1 0.3242162<\/p>\n ## ABCD3 0.2647565<\/p>\n ## ABCF3 0.2613074<\/p>\n ## ABCA8 -0.2609394<\/p>\n ## ABCB7 0.2493680<\/p>\n ## ABCF1 0.2424253<\/p>\n 3.2.4\u6837\u672c\u56fe\u7ed8\u5236<\/p>\n \u6211\u4eec\u5c06\u6837\u672c\u6295\u5f71\u5230\u4e3b\u6210\u5206\u6240\u5728\u7684\u7a7a\u95f4\u4e2d\uff0c\u4ee5\u53ef\u89c6\u5316\u6837\u672c\u5982\u4f55\u805a\u7c7b\u5e76\u8bc4\u4f30\u6570\u636e\u4e2d\u7684\u751f\u7269\u6216\u6280\u672f\u53d8\u5316\u3002<\/p>\n plotIndiv(final.pca.multi,<\/p>\n comp = c(1, 2), # Specify components to plot<\/p>\n ind.names = TRUE, # Show row names of samples<\/p>\n group = multidrug$cell.line$Class,<\/p>\n title = ‘ABC transporters, PCA comp 1 – 2’,<\/p>\n legend = TRUE, legend.title = ‘Cell line’)<\/p>\n \u6837\u672c\u88ab\u6295\u5f71\u5230\u7531\u524d\u4e24\u4e2a\u4e3b\u6210\u5206\u8de8\u8d8a\u7684\u7a7a\u95f4\u4e2d\uff0c\u5e76\u6839\u636e\u7ec6\u80de\u7cfb\u7c7b\u578b\u7740\u8272\u3002\u6570\u5b57\u8868\u793a\u6570\u636e\u7684\u884c\u540d\u79f0\u3002\u6211\u4eec\u4e5f\u53ef\u4ee5\u8fdb\u4e00\u6b65\u68c0\u67e5\u7b2c\u4e09\u4e2a\u4e3b\u6210\u5206\uff0c\u53ea\u9700\u4fee\u6539comp = c(1, 3)\u5373\u53ef\uff0c\u6216\u8005\u4f7f\u75283D\u4ea4\u4e92\u56fe\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n # \u4ea4\u4e92\u5f0f3D\u7ed8\u56fe\u5c06\u52a0\u8f7drgl\u5e93.<\/p>\n plotIndiv(final.pca.multi, style = ‘3d’,<\/p>\n group = multidrug$cell.line$Class,<\/p>\n title = ‘ABC transporters, PCA comp 1 – 3’)<\/p>\n \u5927\u5bb6\u505a\u8fc7\u7b2c\u4e09\u4e2a\u4e3b\u6210\u5206\u7ed8\u56fe\u540e\u53ef\u53d1\u73b0\uff0c\u524d\u4e24\u4e2a\u5206\u91cf\u89e3\u91ca\u7684\u6700\u5927\u53d8\u5f02\u6e90\u53ef\u5f52\u56e0\u4e8e\u9ed1\u8272\u7d20\u7624\u7ec6\u80de\u7cfb\uff08MELAN\uff09\uff0c\u7b2c\u4e09\u4e2a\u5206\u91cf\u53ea\u7a81\u51fa\u663e\u793a\u4e86\u5355\u4e2a\u5f02\u5e38\u6837\u672c\u3002\u56e0\u6b64\uff0c\u5bf9\u6570\u636e\u7684\u89e3\u91ca\u4e3b\u8981\u96c6\u4e2d\u5728\u524d\u4e24\u4e2a\u4e3b\u6210\u5206\u3002<\/p>\n 3.2.5\u53d8\u91cf\u56fe\uff1a\u76f8\u5173\u5706\u56fe<\/p>\n \u76f8\u5173\u5706\u56fe\u4f7f\u7528\u8be5\u51fd\u6570\u6307\u793a\u6bcf\u4e2a\u53d8\u91cf\u5bf9\u6bcf\u4e2a\u5206\u91cf\u7684\u8d21\u732eplotVar()\uff0c\u4ee5\u53ca\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff08\u7531\u53d8\u91cf\u201c\u7c07\u201d\u8868\u793a\uff09\u3002<\/p>\n plotVar(final.pca.multi, comp = c(1, 2),<\/p>\n var.names = TRUE,<\/p>\n cex = 3, # To change the font size<\/p>\n # cutoff = 0.5, # For further cutoff<\/p>\n title = ‘Multidrug transporter, PCA comp 1 – 2’)<\/p>\n \u56fe\u4e2d\u7a81\u51fa\u663e\u793a\u4e86\u4e00\u7ec4\u5bf9PC1\u6709\u8d21\u732e\u7684ABC\u8f6c\u8fd0\u86cb\u767d\uff1aABCE1\uff0c\u5e76\u4e14\u5728\u67d0\u79cd\u7a0b\u5ea6\u4e0a\u4e0eABCB8\u805a\u96c6\u7684\u7ec4\u5bf9PC1\u6709\u6b63\u5411\u8d21\u732e\uff0c\u800cABCA8\u6709\u8d1f\u5411\u8d21\u732e\u3002\u6211\u4eec\u8fd8\u89c2\u5bdf\u5230\u4e00\u7ec4\u5bf9 PC1 \u548c PC2 \u90fd\u6709\u8d21\u732e\u7684\u8f6c\u8fd0\u86cb\u767d\uff1a\u4e0eABCC2\u805a\u96c6\u7684\u4e00\u7ec4\u5bf9 PC2 \u6709\u6b63\u5411\u8d21\u732e\uff0c\u5bf9 PC1 \u6709\u8d1f\u5411\u8d21\u732e\uff0c\u800cABCC12\u548cABCD2\u7c07\u5bf9 PC1 \u548c PC2 \u90fd\u6709\u8d1f\u5411\u8d21\u732e\u3002<\/p>\n 3.2.6 Biplot\uff1a\u6837\u672c\u548c\u53d8\u91cf<\/p>\n \u53cc\u56fe\u5141\u8bb8\u6211\u4eec\u540c\u65f6\u663e\u793a\u6837\u672c\u548c\u53d8\u91cf\uff0c\u4ee5\u8fdb\u4e00\u6b65\u4e86\u89e3\u5b83\u4eec\u7684\u5173\u7cfb\u3002\u6837\u672c\u663e\u793a\u4e3a\u70b9\uff0c\u800c\u53d8\u91cf\u663e\u793a\u5728\u7bad\u5934\u7684\u5c16\u7aef\u3002\u4e0e\u76f8\u5173\u5706\u56fe\u7c7b\u4f3c\uff0c\u6570\u636e\u5fc5\u987b\u5c45\u4e2d\u5e76\u7f29\u653e\u4ee5\u89e3\u91ca\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff08\u4f5c\u4e3a\u53d8\u91cf\u7bad\u5934\u4e4b\u95f4\u7684\u4f59\u5f26\u89d2\uff09\u3002<\/p>\n biplot(final.pca.multi, group = multidrug$cell.line$Class,<\/p>\n legend.title = ‘Cell line’)<\/p>\n Biplot\u663e\u793a\uff0c\u9ed1\u8272\u7d20\u7624\u7ec6\u80de\u7cfb\uff08MELAN\uff09\u4f3c\u4e4e\u4ee5\u8f6c\u8fd0\u86cb\u767d\u5b50\u96c6\u4e3a\u7279\u5f81\uff0c\u4f8b\u5982ABCC2\u5468\u56f4\u7684\u7c07\uff0c\u5982\u4e4b\u524d\u5728\u6837\u672c\u56fe\u4e2d\u7a81\u51fa\u663e\u793a\u7684\u90a3\u6837\u3002\u8fdb\u4e00\u6b65\u68c0\u67e5\u6570\u636e\uff0c\u4f8b\u5982\u7bb1\u7ebf\u56fe\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u9610\u660e\u8fd9\u4e9b\u7279\u5b9a\u6837\u672c\u7684\u8f6c\u8fd0\u86cb\u767d\u8868\u8fbe\u6c34\u5e73\u3002<\/p>\n ABCC2.scale <- scale(X[, ‘ABCC2’], center = TRUE, scale = TRUE)<\/p>\n boxplot(ABCC2.scale ~<\/p>\n multidrug$cell.line$Class, col = color.mixo(1:9),<\/p>\n xlab = ‘Cell lines’, ylab = ‘Expression levels, scaled’,<\/p>\n par(cex.axis = 0.5), # Font size<\/p>\n main = ‘ABCC2 transporter’)<\/p>\n \u4ecePCA\u76f8\u5173\u5706\u56fe\u548cBiplot\u9274\u5b9a\u7684\u8f6c\u8fd0\u86cb\u767dABCC2\u7684\u7bb1\u7ebf\u56fe\u663e\u793a\u4e0e\u7ec6\u80de\u7cfb\u7c7b\u578b\u76f8\u5173\u7684ABCC2\u8868\u8fbe\u6c34\u5e73\u3002<\/p>\n \u4eca\u5929\u5c0f\u679c\u7ed9\u5927\u5bb6\u5c55\u793a\u7684\u662fmixOmics\u5305\u7684\u5b89\u88c5\uff0c\u5feb\u901f\u4f7f\u7528\u4ee5\u53caPCA\u7684\u793a\u4f8b\u5206\u6790\u3002\u5927\u5bb6\u53ef\u4ee5\u5728\u81ea\u5df1\u7684\u7ec4\u5b66\u6570\u636e\u4e0a\u8fdb\u884c\u5c1d\u8bd5\uff0c\u4eca\u5929\u53ea\u7ed9\u5927\u5bb6\u5c55\u793a\u4e86\u5355\u4e00\u7ec4\u5b66PCA\u7684\u793a\u4f8b\u5206\u6790\uff0c\u5927\u5bb6\u5982\u679c\u5bf9\u5176\u4ed6\u4e24\u79cd\u6a21\u5f0f\u5206\u6790\u6709\u5174\u8da3\u7684\u8bdd\uff0c\u53ef\u4ee5\u767b\u9646\u8be5R\u5305\u7684\u5b98\u65b9\u6587\u6863\u6216\u8005\u539f\u59cb\u6587\u732e\u518d\u8be6\u7ec6\u7814\u7a76\u4e00\u4e0b\u54e6\uff5e<\/p>\n \u5982\u679c\u9700\u8981\u6211\u4eec\u5e2e\u5fd9\u6765\u590d\u73b0\u8be5\u7bc7\u6587\u732e\u6216\u8005\u5206\u6790\u81ea\u5df1\u7684\u6570\u636e\u90fd\u53ef\u4ee5\u6765\u627e\u5c0f\u679c\u54e6\uff01\u4eca\u5929\u5c0f\u679c\u7684\u5206\u4eab\u5c31\u5230\u8fd9\u91cc\uff0c\u5982\u679c\u5c0f\u4f19\u4f34\u6709\u5176\u4ed6\u6570\u636e\u7684\u5206\u6790\u9700\u6c42\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u672c\u516c\u53f8\u65b0\u5f00\u53d1\u7684\u751f\u4fe1\u5206\u6790\u5c0f\u5de5\u5177\u4e91\u54c1\u53f0\uff0c\u91cc\u9762\u6709\u6210\u719f\u7684\u4ee3\u7801\uff0c\u53ea\u8981\u8f93\u5165\u5408\u9002\u7684\u6570\u636e\u5c31\u53ef\u4ee5\u76f4\u63a5\u51fa\u60f3\u8981\u7684\u56fe\u5462\uff0c\u94fe\u63a5\uff1ahttp:\/\/www.biocloudservice.com\/home.html<\/a>\u3002<\/p>\n","protected":false},"excerpt":{"rendered":" \u6211\u4eec\u751f\u4fe1\u4eba\u5728\u505a\u5206\u6790\u7684\u65f6\u5019\uff0c\u5982\u679c\u9047\u5230\u7684\u662f\u5355\u4e00\u7c7b\u578b\u7684\u6570\u636e\uff0c\u5982\u7532\u57fa\u5316\u6570\u636e\uff0c\u8f6c\u5f55\u7ec4\u5b66\u6570\u636e\u3001\u4ee3\u8c22\u7ec4\u5b66\u6570\u636e\u7b49\uff0c\u6211\u4eec\u53ef\u80fd\u662f\u624b […]<\/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\/28200"}],"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=28200"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28200\/revisions"}],"predecessor-version":[{"id":28226,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28200\/revisions\/28226"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=28200"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=28200"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=28200"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28200-3.png?resize=640%2C632\")
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