{"id":61064,"date":"2024-10-11T14:57:41","date_gmt":"2024-10-11T06:57:41","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61064"},"modified":"2024-10-11T14:57:41","modified_gmt":"2024-10-11T06:57:41","slug":"%e4%bd%a0%e8%bf%98%e5%9c%a8%e4%b8%ba%e5%8d%95%e7%bb%86%e8%83%9erna%e6%b5%8b%e5%ba%8f%e6%95%b0%e6%8d%ae%e5%8f%91%e6%84%81%e5%90%97%ef%bc%9f%e8%af%95%e8%af%95scone%e5%8c%85%ef%bc%8c%e8%ae%a9%e4%bd%a0","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61064","title":{"rendered":"\u4f60\u8fd8\u5728\u4e3a\u5355\u7ec6\u80deRNA\u6d4b\u5e8f\u6570\u636e\u53d1\u6101\u5417\uff1f\u8bd5\u8bd5SCONE\u5305\uff0c\u8ba9\u4f60\u8f7b\u677e\u8f7b\u677e\u6821\u6b63\u6570\u636e\u504f\u5dee\uff0c\u63a2\u7d22\u751f\u7269\u5b66\u65b0\u4e16\u754c\uff01"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

\u5927\u5bb6\u597d\uff01\u4eca\u5929\u6211\u60f3\u548c\u5927\u5bb6\u5206\u4eab\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u5355\u7ec6\u80deRNA\u6d4b\u5e8f\u6570\u636e\u6807\u51c6\u5316\u548c\u8d28\u91cf\u63a7\u5236\u7684R\u5305\u2014\u2014SCONE\u5305\u3002\u901a\u8fc7\u4f7f\u7528SCONE\u5305\uff0c\u6211\u4eec\u53ef\u4ee5\u7cfb\u7edf\u5316\u5730\u5904\u7406\u5355\u7ec6\u80deRNA\u6d4b\u5e8f\u6570\u636e\uff0c\u63ed\u793a\u6570\u636e\u4e2d\u7684\u6280\u672f\u504f\u5dee\u548c\u751f\u7269\u5b66\u5dee\u5f02\uff0c\u5e76\u8fdb\u884c\u8be6\u7ec6\u7684\u5206\u6790\u3002\u9664\u6b64\u4e4b\u5916\uff0cSCONE\u5305\u8fd8\u652f\u6301\u6280\u672f\u5dee\u5f02\u548c\u6279\u6b21\u6548\u5e94\u7684\u53ef\u89c6\u5316\u5206\u6790\u4ee5\u53ca\u6f0f\u68c0\u6548\u5e94\u7684\u8bc4\u4f30\uff0c\u5e2e\u52a9\u6211\u4eec\u6df1\u5165\u7406\u89e3\u548c\u6821\u6b63\u6570\u636e\u4e2d\u7684\u6280\u672f\u504f\u5dee\u3002\u4efb\u4f55\u751f\u4fe1\u5206\u6790\u5bf9\u751f\u4fe1\u9ad8\u624b\u5c0f\u5e08\u59b9\u6765\u8bf4\u90fd\u4e0d\u662f\u96be\u9898\uff0c\u8981\u662f\u540c\u5b66\u4eec\u6709\u81ea\u5df1\u505a\u4e0d\u4e86\u7684\u751f\u4fe1\u5206\u6790\uff0c\u6b22\u8fce\u968f\u65f6\u8054\u7cfb\u6211\uff01\uff01\uff01<\/strong><\/p>\n\n\n\n

\u4eca\u5929\uff0c\u5c0f\u5e08\u59b9\u5c06\u5e26\u9886\u5927\u5bb6\u4e00\u8d77\u63a2\u7d22\u5982\u4f55\u4f7f\u7528SCONE\u5305\u6765\u6807\u51c6\u5316\u548c\u5206\u6790\u5355\u7ec6\u80deRNA\u6d4b\u5e8f\u6570\u636e\u3002\u6211\u4eec\u5c06\u5b66\u4e60\u5982\u4f55\u5b89\u88c5SCONE\u5305\u3001\u52a0\u8f7d\u6570\u636e\u3001\u8fdb\u884c\u6570\u636e\u9884\u5904\u7406\uff0c\u5e76\u5206\u6790\u548c\u53ef\u89c6\u5316\u6280\u672f\u5dee\u5f02\u548c\u6279\u6b21\u6548\u5e94\u3002\u901a\u8fc7\u4eca\u5929\u7684\u5b66\u4e60\uff0c\u540c\u5b66\u4eec\u53ef\u4ee5\u638c\u63e1\u4f7f\u7528SCONE\u5305\u8fdb\u884c\u5355\u7ec6\u80deRNA\u6d4b\u5e8f\u6570\u636e\u5206\u6790\u7684\u57fa\u672c\u6280\u80fd\uff0c\u5e76\u4e14\u80fd\u591f\u5c06\u8fd9\u4e9b\u65b9\u6cd5\u5e94\u7528\u4e8e\u81ea\u5df1\u7684\u7814\u7a76\u5de5\u4f5c\u4e2d\uff0c\u662f\u4e0d\u662f\u5df2\u7ecf\u8feb\u4e0d\u53ca\u5f85\u4e86\uff0c\u8ddf\u8fdb\u5c0f\u5e08\u59b9\u7684\u6b65\u4f10\uff0c\u8ba9\u6211\u4eec\u4e00\u8d77\u5f00\u542f\u5bf9SCONE\u5305\u7684\u5b66\u4e60\u4e4b\u65c5\u5427\uff01<\/p>\n\n\n\n

\u672c\u6b21\u4ecb\u7ecd\u7684<\/strong>R\u5305<\/strong>\u9700\u8981\u8f83\u591a\u7684\u786c\u4ef6\u8d44\u6e90\uff0c\u5728<\/strong>\u670d\u52a1\u5668<\/strong>\u53ef\u4ee5\u66f4\u52a0\u6d41\u7545<\/strong>\u8fd0\u884c\uff0c<\/strong>\u540c\u5b66\u4eec<\/strong>\u5982\u679c\u6ca1\u6709\u81ea\u5df1\u7684\u670d\u52a1\u5668\u6b22\u8fce\u8054\u7cfb\u6211\u4eec<\/strong>\u8fdb\u884c<\/strong>\u670d\u52a1\u5668\u79df\u8d41~<\/strong><\/strong><\/p>\n\n\n\n

SCONE\u5305\u4ecb\u7ecd<\/p>\n\n\n\n

SCONE\u5305\uff08Single Cell Overview of Normalized Expression\uff09\u662f\u4e00\u4e2a\u7528\u4e8e\u5355\u7ec6\u80deRNA\u6d4b\u5e8f\u6570\u636e\u6807\u51c6\u5316\u548c\u8d28\u91cf\u63a7\u5236\u7684R\u5305\u3002\u901a\u8fc7\u7cfb\u7edf\u5316\u7684\u5206\u6790\u6d41\u7a0b\uff0cSCONE\u5305\u5e2e\u52a9\u7814\u7a76\u8005\u63ed\u793a\u6570\u636e\u4e2d\u7684\u6280\u672f\u504f\u5dee\u548c\u751f\u7269\u5b66\u5dee\u5f02\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0cSCONE\u5305\u63d0\u4f9b\u4e86\u591a\u79cd\u6807\u51c6\u5316\u65b9\u6cd5\uff0c\u5e76\u5305\u542b\u5bf9\u6280\u672f\u5dee\u5f02\u548c\u6279\u6b21\u6548\u5e94\u7684\u53ef\u89c6\u5316\u5206\u6790\u5de5\u5177\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u6761\u5f62\u56fe\u53ef\u89c6\u5316\u6bd4\u5bf9\u8d28\u91cf\u548c\u603b\u8bfb\u53d6\u6570\uff0c\u4ee5\u8bc4\u4f30\u5404\u4e2a\u6279\u6b21\u95f4\u7684\u5dee\u5f02\u3002\u6b64\u5916\uff0cSCONE\u5305\u8fd8\u5305\u542b\u6f0f\u68c0\u6548\u5e94\u7684\u8bc4\u4f30\u529f\u80fd\uff0c\u901a\u8fc7\u6a21\u578b\u5316\u5047\u9634\u6027\u7387\u66f2\u7ebf\uff0c\u5e2e\u52a9\u7814\u7a76\u8005\u7406\u89e3\u5e76\u6821\u6b63\u6570\u636e\u4e2d\u7684\u6280\u672f\u504f\u5dee\uff0c\u786e\u4fdd\u6570\u636e\u5206\u6790\u7684\u51c6\u786e\u6027\u3002<\/p>\n\n\n\n

SCONE\u5305\u7684\u5b89\u88c5<\/p>\n\n\n\n

\u9700\u8981R\u8bed\u8a00\u7248\u672c\u4e3a4.4\uff0c\u5728\u63a7\u5236\u53f0\u4e2d\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\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(“scone”) # \u5728BiocManager\u73af\u5883\u4e0b\u5b89\u88c5SCONE<\/p>\n\n\n\n

\u67e5\u770b\u662f\u5426\u5b89\u88c5\u6210\u529f<\/p>\n\n\n\n

packageVersion(“scone”) # \u67e5\u770bSCONE\u7248\u672c<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u663e\u793a\u4e3a1.28.0\u7248\u672c\uff0c\u5219\u8868\u793a\u5df2\u7ecf\u5b89\u88c5\u4e86scone\u5305\u3002<\/p>\n\n\n\n

\u9664\u6b64\u4e4b\u5916\uff0c\u540e\u7eed\u793a\u4f8b\u8fd8\u9700\u8981\u4f7f\u7528scRNAseq\u5305\uff0c\u6211\u4eec\u53ef\u4ee5\u63d0\u524d\u5b89\u88c5\uff0c\u5b89\u88c5\u547d\u4ee4\u5982\u4e0b\uff1a<\/p>\n\n\n\n

BiocManager::install(“scRNAseq”) # \u5728BiocManager\u73af\u5883\u4e0b\u5b89\u88c5scRNAseq<\/p>\n\n\n\n

packageVersion(“scRNAseq”) # \u67e5\u770bscRNAseq\u7248\u672c<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6570\u636e\u8f7d\u5165\u548c\u9884\u5904\u7406<\/p>\n\n\n\n

\u5305\u548c\u6570\u636e\u8f7d\u5165<\/strong><\/strong><\/p>\n\n\n\n

\u6211\u4eec\u5c06\u901a\u8fc7\u4f7f\u7528\u4e00\u4e2a\u65e9\u671f\u7684scRNA-Seq\u6570\u636e\u96c6\u6765\u6f14\u793a\u57fa\u672c\u7684SCONE\u5de5\u4f5c\u6d41\u3002\u6211\u4eec\u5173\u6ce8\u4ece\u56db\u79cd\u751f\u7269\u6761\u4ef6\u4e2d\u91c7\u6837\u768465\u4e2a\u4eba\u7c7b\u7ec6\u80de\uff0c\u5206\u522b\u4e3a\u7531\u591a\u80fd\u5e72\u7ec6\u80de\u884d\u751f\u7684\u57f9\u517b\u795e\u7ecf\u524d\u4f53\u7ec6\u80de\uff08\u201cNPC\u201d\uff09\uff0c\u5728\u5b55\u546816\u548c21\u7684\u521d\u7ea7\u76ae\u8d28\u6837\u672c\uff08\u5206\u522b\u4e3a\u201cGW16\u201d\u548c\u201cGW21\u201d\uff09\uff0c\u4ee5\u53ca\u57f9\u517b\u4e863\u5468\u7684\u665a\u671f\u76ae\u8d28\u6837\u672c\uff08\u201cGW21+3\u201d\uff09\u3002\u8fd9\u4e9b\u7ec6\u80de\u7684\u57fa\u56e0\u6c34\u5e73\u8868\u8fbe\u6570\u636e\u53ef\u4ee5\u76f4\u63a5\u4eceBioconductor\u4e0a\u7684scRNAseq\u5305\u4e2d\u52a0\u8f7d\u3002<\/p>\n\n\n\n

\u9996\u5148\u6211\u4eec\u9700\u8981\u8f7d\u5165SCONE\u5305\u4ee5\u53cascRNAseq\u5305<\/p>\n\n\n\n

library(scone) #\u8f7d\u5165SCONE\u5305<\/p>\n\n\n\n

library(scRNAseq) #\u8f7d\u5165scRNAseq\u5305<\/p>\n\n\n\n

library(RColorBrewer) #\u8f7d\u5165RColorBrewer\u5305<\/p>\n\n\n\n

\u968f\u540e\u6211\u4eec\u9700\u8981\u8f7d\u5165\u793a\u4f8b\u6570\u636e<\/p>\n\n\n\n

fluidigm <- ReprocessedFluidigmData(assays = “rsem_counts”)<\/p>\n\n\n\n

assay(fluidigm) <- as.matrix(assay(fluidigm)) #\u52a0\u8f7d\u793a\u4f8b\u6570\u636e<\/p>\n\n\n\n

\u6570\u636e\u9884\u5904\u7406<\/strong><\/strong><\/p>\n\n\n\n

\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u6211\u4eec\u5c06\u8fdb\u884c\u4e00\u4e9b\u521d\u6b65\u7b5b\u9009\uff0c\u4ee5\u53bb\u9664\u4f4e\u8986\u76d6\u7387\u91cd\u590d\u6837\u672c\u548c\u672a\u68c0\u6d4b\u5230\u7684\u57fa\u56e0\u7279\u5f81\uff0c\u76f8\u5173\u7684\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n

# \u521d\u6b65\u6837\u672c\u7b5b\u9009\uff1a\u4ec5\u9ad8\u8986\u76d6\u7387<\/p>\n\n\n\n

is_select = colData(fluidigm)$Coverage_Type == “High”<\/p>\n\n\n\n

fluidigm = fluidigm[,is_select]<\/p>\n\n\n\n

# \u4fdd\u7559\u4ec5\u68c0\u6d4b\u5230\u7684\u8f6c\u5f55\u672c<\/p>\n\n\n\n

fluidigm = fluidigm[which(apply(assay(fluidigm) > 0,1,any)),]<\/p>\n\n\n\n

\u4f7f\u7528SCONE\u5305\u5bf9\u5355\u7ec6\u80de\u6570\u636e\u8fdb\u884c\u8bc4\u4f30<\/p>\n\n\n\n

\u6280\u672f\u5dee\u5f02\u548c\u6279\u6b21\u6548\u5e94\u7684\u53ef\u89c6\u5316\u5206\u6790<\/strong><\/strong><\/p>\n\n\n\n

\u6211\u4eec\u8861\u91cf\u6bd4\u5bf9\u8d28\u91cf\u7684\u8bfb\u6570\u4e4b\u4e00\u662f\u4e0e\u8f6c\u5f55\u7ec4\u5bf9\u9f50\u7684\u8bfb\u6570\u7684\u6bd4\u4f8b\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u7b80\u5355\u7684\u6761\u5f62\u56fe\u6765\u53ef\u89c6\u5316\u8be5\u6307\u6807\u4e0e\u751f\u7269\u6279\u6b21\u7684\u5173\u7cfb\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n

# \u5b9a\u4e49\u989c\u8272\u65b9\u6848<\/p>\n\n\n\n

cc <- c(brewer.pal(9, “Set1”))<\/p>\n\n\n\n

# \u6bcf\u4e2a\u751f\u7269\u5b66\u6761\u4ef6\u4e00\u4e2a\u6279\u6b21<\/p>\n\n\n\n

batch = factor(colData(fluidigm)$Biological_Condition)<\/p>\n\n\n\n

# \u6bd4\u5bf9\u8d28\u91cf\u6307\u6807<\/p>\n\n\n\n

qc = colData(fluidigm)[,metadata(fluidigm)$which_qc]<\/p>\n\n\n\n

# \u4eba\u7c7b\u8f6c\u5f55\u7ec4\u6620\u5c04\u8bfb\u53d6\u6bd4\u4f8b\u7684\u6761\u5f62\u56fe<\/p>\n\n\n\n

ralign = qc$RALIGN<\/p>\n\n\n\n

o = order(ralign)[order(batch[order(ralign)])] # \u6309\u6279\u6b21\u7136\u540e\u6309\u503c\u6392\u5e8f<\/p>\n\n\n\n

barplot(ralign[o], col=cc[batch][o],  border=cc[batch][o], main=”\u6620\u5c04\u5230\u4eba\u7c7b\u8f6c\u5f55\u7ec4\u7684\u8bfb\u53d6\u767e\u5206\u6bd4”)<\/p>\n\n\n\n

legend(“bottomleft”, legend=levels(batch), fill=cc, cex=0.4)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6279\u6b21\u4e4b\u95f4\u5b58\u5728\u9002\u5ea6\u5dee\u5f02\uff0c\u5e76\u4e14\u6211\u4eec\u770b\u5230\u6709\u4e00\u4e2aGW21\u7ec6\u80de\u7684\u6bd4\u5bf9\u7387\u76f8\u5bf9\u4e8e\u5176\u4ed6GW21\u6279\u6b21\u7279\u522b\u4f4e\u3002\u8fd9\u4e9b\u7c7b\u578b\u7684\u89c2\u5bdf\u53ef\u4ee5\u544a\u77e5\u6211\u4eec\u201c\u8d28\u91cf\u8f83\u5dee\u201d\u7684\u6587\u5e93\u6216\u6279\u6b21\u3002\u6211\u4eec\u540c\u6837\u4e5f\u53ef\u4ee5\u8bc4\u4f30\u4e00\u4e0b\u6bcf\u4e2a\u6587\u5e93\u7684\u8bfb\u53d6\u6570\u91cf\uff0c\u76f8\u5173\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n

# \u603b\u8bfb\u53d6\u6570\u7684\u6761\u5f62\u56fe<\/p>\n\n\n\n

nreads = qc$NREADS<\/p>\n\n\n\n

o = order(nreads)[order(batch[order(nreads)])] # \u6309\u6279\u6b21\u7136\u540e\u6309\u503c\u6392\u5e8f<\/p>\n\n\n\n

barplot(nreads[o], col=cc[batch][o], border=cc[batch][o], main=”\u603b\u8bfb\u53d6\u6570”)<\/p>\n\n\n\n

legend(“topright”, legend=levels(batch), fill=cc, cex=0.4)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6211\u4eec\u770b\u5230\u8bfb\u53d6\u8986\u76d6\u7387\u5728\u6279\u6b21\u4e4b\u95f4\u4ee5\u53ca\u6279\u6b21\u5185\u90e8\u5dee\u5f02\u663e\u8457\u3002\u8fd9\u4e9b\u8986\u76d6\u7387\u5dee\u5f02\u548c\u5176\u4ed6\u6280\u672f\u7279\u5f81\u53ef\u4ee5\u5bfc\u81f4\u5bf9\u8868\u8fbe\u4f30\u8ba1\u7684\u975e\u76f4\u89c2\u504f\u5dee\u3002\u5c3d\u7ba1\u4e00\u4e9b\u504f\u5dee\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u6587\u5e93\u5927\u5c0f\u5f52\u4e00\u5316\u548c\u7ec6\u80de\u7b5b\u9009\u6765\u89e3\u51b3\uff0c\u4f46\u5bf9\u591a\u7ec6\u80de\u6570\u91cf\u7684\u9700\u6c42\u53ef\u80fd\u9700\u8981\u66f4\u590d\u6742\u7684\u5f52\u4e00\u5316\u65b9\u6cd5\uff0c\u4ee5\u4fbf\u6bd4\u8f83\u591a\u4e2a\u6279\u6b21\u7684\u7ec6\u80de\u3002<\/p>\n\n\n\n

\u6f0f\u68c0\u8bc4\u4f30<\/strong><\/strong><\/p>\n\n\n\n

\u5355\u7ec6\u80de\u7279\u6709\u7684\u4e00\u4e2a\u95ee\u9898\u5c31\u662f\u6f0f\u68c0\u6548\u5e94\u3002\u5728\u5efa\u6a21\u6f0f\u68c0\u6548\u5e94\u65f6\u9762\u4e34\u7684\u6700\u5927\u6311\u6218\u4e4b\u4e00\u662f\u540c\u65f6\u5efa\u6a21\u6280\u672f\u6f0f\u68c0\u548c\u751f\u7269\u8868\u8fbe\u5f02\u8d28\u6027\u3002\u7b80\u5316\u95ee\u9898\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u5173\u6ce8\u90a3\u4e9b\u6211\u4eec\u786e\u4fe1\u771f\u6b63\u8868\u8fbe\u7684\u57fa\u56e0\u3002SCONE\u5305\u5305\u542b\u4e86\u4e00\u4e9b\u57fa\u56e0\u5217\u8868\uff0c\u8fd9\u4e9b\u57fa\u56e0\u88ab\u8ba4\u4e3a\u5728\u6240\u6709\u4eba\u7c7b\u7ec4\u7ec7\u4e2d\u666e\u904d\u4e14\u5747\u5300\u5730\u8868\u8fbe\u3002\u5982\u679c\u6211\u4eec\u5047\u8bbe\u8fd9\u4e9b\u57fa\u56e0\u5728\u6240\u6709\u7ec6\u80de\u4e2d\u90fd\u771f\u6b63\u8868\u8fbe\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u6240\u6709\u96f6\u4e30\u5ea6\u89c2\u5bdf\u6807\u8bb0\u4e3a\u6f0f\u68c0\u4e8b\u4ef6\u3002\u6211\u4eec\u5c06\u68c0\u6d4b\u5931\u8d25\u5efa\u6a21\u4e3a\u5e73\u5747\u8868\u8fbe\u7684\u903b\u8f91\u51fd\u6570\uff0c\u4e0e\u8be5\u9886\u57df\u4f7f\u7528\u7684\u6807\u51c6\u903b\u8f91\u6a21\u578b\u4fdd\u6301\u4e00\u81f4\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n

# \u63d0\u53d6\u7ba1\u5bb6\u57fa\u56e0<\/p>\n\n\n\n

data(housekeeping)<\/p>\n\n\n\n

hk = intersect(housekeeping$V1, rownames(assay(fluidigm)))<\/p>\n\n\n\n

# \u5e73\u5747log10(x+1)\u8868\u8fbe<\/p>\n\n\n\n

mu_obs = rowMeans(log10(assay(fluidigm)[hk,]+1))<\/p>\n\n\n\n

# \u5047\u5b9a\u7684\u5047\u9634\u6027<\/p>\n\n\n\n

drop_outs = assay(fluidigm)[hk,] == 0<\/p>\n\n\n\n

# \u5931\u8d25\u7684\u903b\u8f91\u56de\u5f52\u6a21\u578b<\/p>\n\n\n\n

ref.glms = list()<\/p>\n\n\n\n

for (si in 1:dim(drop_outs)[2]){<\/p>\n\n\n\n

  fit = glm(cbind(drop_outs[,si], 1 – drop_outs[,si]) ~ mu_obs,<\/p>\n\n\n\n

            family=binomial(logit))<\/p>\n\n\n\n

  ref.glms[[si]] = fit$coefficients<\/p>\n\n\n\n

}<\/p>\n\n\n\n

\u5217\u8868ref.glm\u5305\u542b\u4e86\u6bcf\u4e2a\u62df\u5408\u7684\u622a\u8ddd\u548c\u659c\u7387\u3002\u6211\u4eec\u73b0\u5728\u53ef\u4ee5\u53ef\u89c6\u5316\u62df\u5408\u66f2\u7ebf\u548c\u76f8\u5e94\u7684\u66f2\u7ebf\u4e0b\u9762\u79ef\uff08AUC\uff09\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n

par(mfrow=c(1,2))<\/p>\n\n\n\n

# \u7ed8\u5236\u5931\u8d25\u66f2\u7ebf\u5e76\u8ba1\u7b97AUC<\/p>\n\n\n\n

plot(NULL, main = “\u5047\u9634\u6027\u7387\u66f2\u7ebf”,<\/p>\n\n\n\n

     ylim = c(0,1), xlim = c(0,6),<\/p>\n\n\n\n

     ylab = “\u5931\u8d25\u6982\u7387”, xlab = “\u5e73\u5747log10\u8868\u8fbe”)<\/p>\n\n\n\n

x = (0:60)\/10<\/p>\n\n\n\n

AUC = NULL<\/p>\n\n\n\n

for(si in 1:ncol(assay(fluidigm))){<\/p>\n\n\n\n

  y = 1\/(exp(-ref.glms[[si]][1] – ref.glms[[si]][2] * x) + 1)<\/p>\n\n\n\n

  AUC[si] = sum(y)\/10<\/p>\n\n\n\n

  lines(x, 1\/(exp(-ref.glms[[si]][1] – ref.glms[[si]][2] * x) + 1),<\/p>\n\n\n\n

        type = ‘l’, lwd = 2, col = cc[batch][si])<\/p>\n\n\n\n

}<\/p>\n\n\n\n

# FNR AUC\u7684\u6761\u5f62\u56fe<\/p>\n\n\n\n

o = order(AUC)[order(batch[order(AUC)])]<\/p>\n\n\n\n

barplot(AUC[o], col=cc[batch][o], border=cc[batch][o], main=”FNR AUC”)<\/p>\n\n\n\n

legend(“topright”, legend=levels(batch), fill=cc, cex=0.4)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u7531\u56fe\u53ef\u77e5\uff0c\u5de6\u56fe\u5c55\u793a\u4e86\u4e0d\u540c\u7ec6\u80de\u6837\u672c\u7684\u5047\u9634\u6027\u7387\uff08\u6f0f\u68c0\u6982\u7387\uff09\u968f\u5e73\u5747log10\u8868\u8fbe\u503c\u7684\u53d8\u5316\u60c5\u51b5\u3002\u66f2\u7ebf\u7684\u6a2a\u8f74\u8868\u793a\u57fa\u56e0\u7684\u5e73\u5747log10\u8868\u8fbe\u503c\uff0c\u7eb5\u8f74\u8868\u793a\u6f0f\u68c0\u6982\u7387\u3002\u4e0d\u540c\u989c\u8272\u7684\u66f2\u7ebf\u4ee3\u8868\u4e0d\u540c\u7684\u751f\u7269\u6761\u4ef6\u3002\u968f\u7740\u8868\u8fbe\u503c\u7684\u589e\u52a0\uff0c\u6f0f\u68c0\u6982\u7387\u9010\u6e10\u4e0b\u964d\uff0c\u8fd9\u8868\u660e\u8868\u8fbe\u6c34\u5e73\u8d8a\u9ad8\uff0c\u57fa\u56e0\u88ab\u6f0f\u68c0\u7684\u6982\u7387\u8d8a\u4f4e\u3002\u53f3\u56fe\u5c55\u793a\u4e86\u6bcf\u4e2a\u7ec6\u80de\u6837\u672c\u7684\u5047\u9634\u6027\u7387\u66f2\u7ebf\u4e0b\u9762\u79ef\uff08FNR AUC\uff09\u7684\u6761\u5f62\u56fe\u3002FNR AUC\u8868\u793a\u6f0f\u68c0\u7387\u66f2\u7ebf\u4e0b\u9762\u7684\u9762\u79ef\uff0c\u662f\u8bc4\u4ef7\u6f0f\u68c0\u6548\u5e94\u7684\u4e00\u4e2a\u7efc\u5408\u6307\u6807\u3002\u6761\u5f62\u56fe\u7684\u989c\u8272\u5bf9\u5e94\u5de6\u56fe\u4e2d\u7684\u66f2\u7ebf\u989c\u8272\uff0c\u8868\u793a\u4e0d\u540c\u7684\u751f\u7269\u6761\u4ef6\u3002\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0cGW16\uff08\u7ea2\u8272\uff09\u548cGW21\uff08\u84dd\u8272\uff09\u6837\u672c\u7684FNR AUC\u8f83\u9ad8\uff0c\u8868\u660e\u8fd9\u4e9b\u6837\u672c\u7684\u6f0f\u68c0\u6548\u5e94\u8f83\u5f3a\u3002\u800cGW21+3\uff08\u7eff\u8272\uff09\u548cNPC\uff08\u7d2b\u8272\uff09\u6837\u672c\u7684FNR AUC\u8f83\u4f4e\uff0c\u8868\u660e\u8fd9\u4e9b\u6837\u672c\u7684\u6f0f\u68c0\u6548\u5e94\u8f83\u5f31\u3002<\/p>\n\n\n\n

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