{"id":61078,"date":"2024-10-17T09:48:13","date_gmt":"2024-10-17T01:48:13","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61078"},"modified":"2024-10-17T09:48:13","modified_gmt":"2024-10-17T01:48:13","slug":"%e5%88%ab%e5%86%8d%e9%94%99%e8%bf%87%ef%bc%81ace%e5%8c%85%ef%bc%8c%e5%9f%ba%e5%9b%a0%e7%bb%84%e6%8b%b7%e8%b4%9d%e6%95%b0%e5%8f%98%e5%bc%82%e5%88%86%e6%9e%90%e5%88%a9%e5%99%a8%ef%bc%8c%e5%b8%a6","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61078","title":{"rendered":"\u522b\u518d\u9519\u8fc7\uff01ACE\u5305\uff0c\u57fa\u56e0\u7ec4\u62f7\u8d1d\u6570\u53d8\u5f02\u5206\u6790\u5229\u5668\uff0c\u5e26\u4f60\u8f7b\u677e\u62df\u5408\u6a21\u578b\u3001\u5206\u6790\u53d8\u5f02\uff01"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

\u4eca\u5929\u5927\u6d77\u54e5\u8981\u5411\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2a\u5728\u57fa\u56e0\u7ec4\u62f7\u8d1d\u6570\u53d8\u5f02\u5206\u6790\u9886\u57df\u4e2d\u975e\u5e38\u91cd\u8981\u7684R\u5305\u2014\u2014ACE\u5305\uff01ACE\u5305\u662f\u4e13\u95e8\u7528\u4e8e\u5206\u6790\u57fa\u56e0\u7ec4\u62f7\u8d1d\u6570\u53d8\u5f02\u7684\u5f3a\u5927\u5de5\u5177\u3002\u8981\u77e5\u9053\uff0c\u57fa\u56e0\u7ec4\u62f7\u8d1d\u6570\u53d8\u5f02\u5bf9\u4e8e\u7406\u89e3\u80bf\u7624\u7684\u53d1\u751f\u548c\u53d1\u5c55\u81f3\u5173\u91cd\u8981\uff0c\u800cACE\u5305\u63d0\u4f9b\u4e86\u9ad8\u6548\u4e14\u51c6\u786e\u7684\u65b9\u6cd5\u6765\u8fdb\u884c\u8fd9\u65b9\u9762\u7684\u5206\u6790\u3002\u901a\u8fc7ACE\u5305\uff0c\u6211\u4eec\u53ef\u4ee5\u6784\u5efa\u62f7\u8d1d\u6570\u6a21\u578b\uff0c\u8bc4\u4f30\u6837\u672c\u4e2d\u7684\u7ec6\u80de\u6bd4\u4f8b\u548c\u62df\u5408\u8bef\u5dee\uff0c\u5e76\u53ef\u89c6\u5316\u57fa\u56e0\u7ec4\u4e2d\u7684\u62f7\u8d1d\u6570\u53d8\u5f02\u60c5\u51b5\u3002\u5927\u6d77\u54e5\u53ef\u662f\u5389\u5bb3\u7684\u751f\u4fe1\u9ad8\u624b\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><\/strong><\/p>\n\n\n\n

\u5728\u672c\u6b21\u5b66\u4e60\u4e2d\uff0c\u6211\u4eec\u4e0d\u4ec5\u5c06\u5b66\u4f1a\u5982\u4f55\u5b89\u88c5\u548c\u4f7f\u7528ACE\u5305\uff0c\u8fd8\u5c06\u638c\u63e1\u57fa\u56e0\u7ec4\u62f7\u8d1d\u6570\u6570\u636e\u7684\u9884\u5904\u7406\u3001\u6a21\u578b\u6784\u5efa\u3001\u4ee5\u53ca\u53d8\u5f02\u5206\u6790\u7684\u65b9\u6cd5\u3002\u901a\u8fc7\u5b9e\u9645\u793a\u4f8b\u7684\u6f14\u793a\uff0c\u6211\u4eec\u5c06\u66f4\u6df1\u5165\u5730\u7406\u89e3\u57fa\u56e0\u7ec4\u62f7\u8d1d\u6570\u53d8\u5f02\u7684\u6a21\u5f0f\u548c\u5f71\u54cd\uff0c\u4e3a\u6211\u4eec\u89e3\u8bfb\u80bf\u7624\u7ec6\u80de\u7684\u57fa\u56e0\u7ec4\u7279\u5f81\u63d0\u4f9b\u91cd\u8981\u7ebf\u7d22\u3002\u6211\u76f8\u4fe1\uff0c\u901a\u8fc7\u4eca\u5929\u7684\u5b66\u4e60\uff0c\u540c\u5b66\u4eec\u90fd\u80fd\u591f\u66f4\u52a0\u719f\u7ec3\u5730\u5e94\u7528ACE\u5305\uff0c\u4e3a\u57fa\u56e0\u7ec4\u53d8\u5f02\u5206\u6790\u7684\u7814\u7a76\u8fdb\u6b65\u8d21\u732e\u81ea\u5df1\u7684\u4e00\u4efd\u529b\u91cf\uff01\u63a5\u4e0b\u6765\uff0c\u5c31\u8ba9\u5927\u6d77\u54e5\u5e26\u9886\u5927\u5bb6\u6b63\u5f0f\u5f00\u542f\u5bf9ACE\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\u4f7f\u7528\u670d\u52a1\u5668\u79df\u8d41~<\/strong><\/strong><\/p>\n\n\n\n

ACE\u5305\u7b80\u4ecb<\/p>\n\n\n\n

ACE\u5305\u662f\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u5206\u6790\u57fa\u56e0\u7ec4\u62f7\u8d1d\u6570\u53d8\u5f02\u7684R\u8bed\u8a00\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u80bf\u7624\u7814\u7a76\u7b49\u9886\u57df\u3002\u901a\u8fc7ACE\uff0c\u7528\u6237\u53ef\u4ee5\u6784\u5efa\u62f7\u8d1d\u6570\u6a21\u578b\uff0c\u8bc4\u4f30\u6837\u672c\u4e2d\u7684\u7ec6\u80de\u6bd4\u4f8b\u548c\u62df\u5408\u8bef\u5dee\uff0c\u5e76\u53ef\u89c6\u5316\u57fa\u56e0\u7ec4\u4e2d\u7684\u62f7\u8d1d\u6570\u53d8\u5f02\u60c5\u51b5\u3002\u8be5\u5de5\u5177\u63d0\u4f9b\u4e86\u591a\u79cd\u529f\u80fd\u5f3a\u5927\u7684\u51fd\u6570\u548c\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u5e2e\u52a9\u7814\u7a76\u4eba\u5458\u6df1\u5165\u7406\u89e3\u80bf\u7624\u7ec6\u80de\u4e2d\u7684\u62f7\u8d1d\u6570\u53d8\u5f02\u6a21\u5f0f\uff0c\u8bc6\u522b\u6f5c\u5728\u7684\u57fa\u56e0\u6269\u589e\u6216\u7f3a\u5931\u533a\u57df\uff0c\u5e76\u63a2\u7d22\u8fd9\u4e9b\u53d8\u5f02\u4e0e\u80bf\u7624\u53d1\u5c55\u53ca\u6cbb\u7597\u53cd\u5e94\u4e4b\u95f4\u7684\u5173\u7cfb\u3002ACE\u7684\u4f7f\u7528\u4e0d\u4ec5\u7b80\u5316\u4e86\u6570\u636e\u5206\u6790\u6d41\u7a0b\uff0c\u4e5f\u4e3a\u57fa\u56e0\u7ec4\u5b66\u7814\u7a76\u63d0\u4f9b\u4e86\u6709\u529b\u7684\u5de5\u5177\u652f\u6301\u3002<\/p>\n\n\n\n

ACE\u5305\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(“ACE”) # \u5728BiocManager\u73af\u5883\u4e0b\u5b89\u88c5ACE<\/p>\n\n\n\n

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

packageVersion(“ACE”) # \u67e5\u770bACE\u7248\u672c<\/p>\n\n\n\n

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

\u663e\u793a\u4e3a1.22.0\u7248\u672c\uff0c\u5219\u8868\u793a\u5df2\u7ecf\u5b89\u88c5\u4e86ACE\u5305\u3002<\/p>\n\n\n\n

\u6570\u636e\u51c6\u5907<\/p>\n\n\n\n

\u60f3\u8981\u4f7f\u7528ACE\u5305\u5206\u6790\u7ec6\u80de\u57fa\u56e0\u7ec4\u62f7\u8d1d\u6570\u53d8\u5f02\uff0c\u6211\u4eec\u9996\u5148\u9700\u8981\u8f7d\u5165\u9700\u8981\u7528\u5230\u7684\u5305\u548c\u793a\u4f8b\u6570\u636e\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528ACE\u81ea\u5e26\u76842N\u80bf\u7624\u7ec6\u80de\u6570\u636e\u4f5c\u4e3a\u6211\u4eec\u7684\u793a\u4f8b\u6570\u636e\uff0c\u76f8\u5173\u7684\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n

library(ACE) #\u8f7d\u5165ACE\u5305<\/p>\n\n\n\n

# \u52a0\u8f7dQDNAseq\u5305\u4e2d\u7684\u793a\u4f8b\u6570\u636e\u96c6 “copyNumbersSegmented”<\/p>\n\n\n\n

data(“copyNumbersSegmented”)<\/p>\n\n\n\n

# \u5c06\u52a0\u8f7d\u7684\u6570\u636e\u96c6\u8d4b\u503c\u7ed9\u53d8\u91cf object<\/p>\n\n\n\n

object <- copyNumbersSegmented<\/p>\n\n\n\n

object # \u6253\u5370\u5bf9\u8c61 object \u7684\u5185\u5bb9\u548c\u57fa\u672c\u4fe1\u606f<\/p>\n\n\n\n

# \u663e\u793a\u5bf9\u8c61\u7684\u7c7b\u578b\u548c\u4e3b\u8981\u5c5e\u6027\uff0c\u5305\u62ecassayData\u3001featureData\u548cphenoData\u7684\u7b80\u8981\u6982\u8ff0<\/p>\n\n\n\n

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

\u7531\u4e0a\u56fe\u53ef\u77e5\uff0c\u793a\u4f8b\u6570\u636e\u5171\u5305\u62ec2\u4e2a\u6570\u636e\u6837\u672c\uff0c\u6bcf\u4e2a\u6570\u636e\u6837\u672c\u4e2d\u67093113\u4e2a\u7279\u5f81\u57fa\u56e0\uff0c\u5728\u672c\u4e3a\u4e2d\uff0c\u6211\u4eec\u53ea\u4f7f\u7528sample1\u4f5c\u4e3a\u793a\u4f8b\u6570\u636e\uff0c\u540c\u5b66\u4eec\u5982\u679c\u6709\u5174\u8da3\u53ef\u4ee5\u5728\u5b8c\u6210\u672c\u6587\u5b66\u4e60\u4e4b\u540e\u81ea\u884c\u5206\u6790sample2\u3002<\/p>\n\n\n\n

\u4f7f\u7528ACE\u5305\u6784\u5efa\u80bf\u7624\u7ec6\u80de\u62f7\u8d1d\u6570\u6a21\u578b<\/p>\n\n\n\n

\u81f3\u6b64\uff0c\u6211\u4eec\u5df2\u7ecf\u8f7d\u5165\u4e86\u9700\u8981\u7528\u5230\u7684\u5305\u548c\u6570\u636e\uff0c\u63a5\u4e0b\u6765\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528ACE\u5305\u4e2d\u7684singlemodel()\u51fd\u6570\uff0c\u6784\u5efa\u7ec6\u80de\u7684\u62f7\u8d1d\u6570\u6a21\u578b\uff0c\u76f8\u5173\u7684\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n

model1 <- singlemodel(object, QDNAseqobjectsample = 1) # \u521b\u5efa\u5355\u6837\u672c\u6a21\u578b<\/p>\n\n\n\n

bestfit1 <- model1$minima[tail(which(model1$rerror == min(model1$rerror)), 1)]<\/p>\n\n\n\n

besterror1 <- min(model1$rerror) # \u627e\u5230\u6700\u4f73\u62df\u5408\u7684\u62df\u5408\u503c\u548c\u9519\u8bef\u503c<\/p>\n\n\n\n

lastfit1 <- tail(model1$minima, 1)<\/p>\n\n\n\n

lasterror1 <- tail(model1$rerror, 1) # \u627e\u5230\u6700\u540e\u4e00\u4e2a\u62df\u5408\u7684\u62df\u5408\u503c\u548c\u9519\u8bef\u503c<\/p>\n\n\n\n

# \u7ed8\u5236\u5355\u6837\u672c\u7684\u62f7\u8d1d\u6570\u6570\u636e\u56fe<\/p>\n\n\n\n

singleplot(object,                        # \u4f7f\u7528\u7684\u6570\u636e\u5bf9\u8c61<\/p>\n\n\n\n

           QDNAseqobjectsample = 1,       # \u6307\u5b9a\u7684\u6837\u672c\u7f16\u53f7<\/p>\n\n\n\n

           cellularity = bestfit1,        # \u6700\u4f73\u62df\u5408\u7684\u7ec6\u80de\u6bd4\u4f8b\u503c<\/p>\n\n\n\n

           error = besterror1,            # \u6700\u4f73\u62df\u5408\u7684\u8bef\u5dee\u503c<\/p>\n\n\n\n

           standard = model1$standard,    # \u6a21\u578b\u7684\u6807\u51c6\u4fe1\u606f<\/p>\n\n\n\n

           title = “sample1 – binsize 1000 kbp – 379776 reads – 2N fit 1”)<\/p>\n\n\n\n

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

\u56fe\u4e3a\u6700\u4f73\u62df\u5408\u7ed3\u679c\uff0c\u5728\u56fe\u4e2d\uff0cX\u8f74\u8868\u793a\u67d3\u8272\u4f53\u4f4d\u7f6e\uff0cY\u8f74\u8868\u793a\u6bcf\u4e2a\u4f4d\u7f6e\u7684\u62f7\u8d1d\u6570\u3002\u6bcf\u4e2a\u9ed1\u8272\u70b9\u8868\u793a\u4e00\u4e2a\u5343\u78b1\u57fa\u5bf9\uff08kbp\uff09\u7247\u6bb5\u7684\u62f7\u8d1d\u6570\u503c\uff0c\u6a59\u8272\u7684\u6c34\u5e73\u7ebf\u8868\u793a\u5728\u8fd9\u4e9b\u4f4d\u7f6e\u7684\u62f7\u8d1d\u6570\u5e73\u5747\u503c\u3002\u53ef\u4ee5\u770b\u5230\uff0c\u67d3\u8272\u4f538\u4e0a\u6709\u660e\u663e\u7684\u62f7\u8d1d\u6570\u589e\u52a0\uff0c\u8fbe\u523011\u4e2a\u62f7\u8d1d\uff0c\u800c\u5728\u5176\u4ed6\u5927\u90e8\u5206\u67d3\u8272\u4f53\u4f4d\u7f6e\uff0c\u62f7\u8d1d\u6570\u7ef4\u6301\u57282\u5de6\u53f3\u3002\u8fd9\u8868\u660esample1\u4e2d\u7684\u67d0\u4e9b\u533a\u57df\u5b58\u5728\u663e\u8457\u7684\u57fa\u56e0\u6269\u589e\u73b0\u8c61\uff0c\u800c\u5176\u4ed6\u533a\u57df\u5219\u4fdd\u6301\u76f8\u5bf9\u7a33\u5b9a\u3002Cellularity\u4e3a0.08\uff0c\u8868\u793a\u80bf\u7624\u7ec6\u80de\u5728\u6837\u672c\u4e2d\u7684\u6bd4\u4f8b\u76f8\u5bf9\u8f83\u4f4e\uff0c\u800cRelative error\u4e3a0.172\uff0c\u8868\u793a\u6a21\u578b\u62df\u5408\u7684\u8bef\u5dee\u3002\u8fd9\u4e9b\u4fe1\u606f\u8868\u660e\uff0c\u6837\u672c\u4e2d\u7684\u80bf\u7624\u7ec6\u80de\u6bd4\u4f8b\u867d\u7136\u8f83\u4f4e\uff0c\u4f46\u67d3\u8272\u4f537\u7684\u57fa\u56e0\u6269\u589e\u975e\u5e38\u663e\u8457\uff0c\u53ef\u80fd\u5bf9\u80bf\u7624\u7684\u53d1\u751f\u548c\u53d1\u5c55\u6709\u91cd\u8981\u4f5c\u7528\u3002<\/p>\n\n\n\n

# \u7ed8\u5236\u5355\u6837\u672c\u7684\u62f7\u8d1d\u6570\u6570\u636e\u56fe<\/p>\n\n\n\n

singleplot(object,                        # \u4f7f\u7528\u7684\u6570\u636e\u5bf9\u8c61<\/p>\n\n\n\n

           QDNAseqobjectsample = 1,       # \u6307\u5b9a\u7684\u6837\u672c\u7f16\u53f7<\/p>\n\n\n\n

           cellularity = lastfit1,        # \u6700\u540e\u4e00\u4e2a\u62df\u5408\u7684\u7ec6\u80de\u6bd4\u4f8b\u503c<\/p>\n\n\n\n

           error = lasterror1,            # \u6700\u540e\u4e00\u4e2a\u62df\u5408\u7684\u8bef\u5dee\u503c<\/p>\n\n\n\n

           standard = model1$standard,    # \u6a21\u578b\u7684\u6807\u51c6\u4fe1\u606f<\/p>\n\n\n\n

           title = “sample1 – binsize 1000 kbp – 379776 reads – 2N fit 7”) # \u56fe\u8868\u7684\u6807\u9898<\/p>\n\n\n\n

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

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