{"id":61361,"date":"2024-10-30T15:27:12","date_gmt":"2024-10-30T07:27:12","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61361"},"modified":"2024-10-30T15:32:58","modified_gmt":"2024-10-30T07:32:58","slug":"%e5%9f%ba%e5%9b%a0%e5%88%86%e6%9e%90%e7%a5%9e%e5%99%a8%ef%bc%81multistateqtl-%e5%8c%85%ef%bc%8c%e5%bc%80%e5%90%af%e5%a4%9a%e7%8a%b6%e6%80%81-qtl-%e6%8e%a2%e7%b4%a2%e4%b9%8b%e6%97%85%ef%bc%81","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61361","title":{"rendered":"\u57fa\u56e0\u5206\u6790\u795e\u5668\uff01multistateQTL \u5305\uff0c\u5f00\u542f\u591a\u72b6\u6001 QTL \u63a2\u7d22\u4e4b\u65c5\uff01"},"content":{"rendered":"\n
\"\"\/<\/figure>\n\n\n\n

\u563f\uff0c\u79d1\u7814\u5c0f\u4f19\u4f34\u4eec\uff01\u5c0f\u5e08\u59b9\u53c8\u6765\u7ed9\u5927\u5bb6\u5e26\u6765\u4e00\u4e2a\u8d85\u68d2\u7684\u751f\u7269\u4fe1\u606f\u5b66\u5de5\u5177\u2014\u2014multistateQTL\u5305\u3002\u8fd9\u4e2a\u5de5\u5177\u53ef\u4ee5\u8ba9\u4f60\u6df1\u5165\u63a2\u7d22\u57fa\u56e0\u5982\u4f55\u5728\u4e0d\u540c\u7ec4\u7ec7\u6216\u73af\u5883\u6761\u4ef6\u4e0b\u5f71\u54cd\u590d\u6742\u7684\u6570\u91cf\u6027\u72b6\uff0c\u4ece\u800c\u4e3a\u7cbe\u51c6\u533b\u5b66\u548c\u4e2a\u6027\u5316\u6cbb\u7597\u63d0\u4f9b\u79d1\u5b66\u4f9d\u636e\u3002<\/p>\n\n\n\n

QTL\uff08\u6570\u91cf\u6027\u72b6\u4f4d\u70b9\uff09\u662f\u57fa\u56e0\u7ec4\u4e2d\u5f71\u54cd\u6570\u91cf\u6027\u72b6\u7684\u533a\u57df\uff0c\u800c\u6570\u91cf\u6027\u72b6\u662f\u6307\u90a3\u4e9b\u5728\u4e00\u4e2a\u7fa4\u4f53\u4e2d\u4ee5\u8fde\u7eed\u6216\u91cf\u5316\u7684\u65b9\u5f0f\u8868\u73b0\u7684\u6027\u72b6\uff08\u8eab\u9ad8\u3001\u4f53\u91cd\u6216\u8005\u75be\u75c5\u6613\u611f\u6027\u7b49\uff09\u3002\u901a\u8fc7\u7814\u7a76QTL\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u54ea\u4e9b\u57fa\u56e0\u53d8\u5f02\u5f71\u54cd\u8fd9\u4e9b\u6027\u72b6\uff0c\u5e76\u63a2\u7d22\u5b83\u4eec\u5982\u4f55\u5728\u4e0d\u540c\u7684\u751f\u7269\u5b66\u80cc\u666f\u4e0b\u53d1\u6325\u4f5c\u7528\u3002<\/p>\n\n\n\n

multistateQTL\u5305\u5c31\u662f\u4e3a\u6b64\u800c\u751f\u7684\uff0c\u5b83\u4e13\u95e8\u7528\u6765\u5206\u6790\u4e0d\u540c\u72b6\u6001\u4e0b\u7684QTL\u6c47\u603b\u7edf\u8ba1\u6570\u636e\uff0c\u6bd4\u5982\u4e0d\u540c\u7ec4\u7ec7\u3001\u7ec6\u80de\u7c7b\u578b\u6216\u73af\u5883\u6761\u4ef6\u4e0b\u7684\u57fa\u56e0\u8868\u8fbe\u3002\u60f3\u8c61\u4e00\u4e0b\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u5bf9\u6210\u5343\u4e0a\u4e07\u7684\u57fa\u56e0\u3001\u8f6c\u5f55\u672c\u6216\u57fa\u56e0\u7ec4\u533a\u57df\u5728\u4e0d\u540c\u72b6\u6001\u4e0b\u7684QTL\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u6d4b\u8bd5\u3001\u603b\u7ed3\u548c\u53ef\u89c6\u5316\uff0c\u8fd9\u662f\u4e0d\u662f\u542c\u8d77\u6765\u5c31\u5f88\u9177\uff1f<\/p>\n\n\n\n

\u522b\u6025\uff0c\u5c0f\u5e08\u59b9\u73b0\u5728\u5c31\u5e26\u4f60\u4e00\u8d77\u63a2\u7d22multistateQTL\u7684\u795e\u5947\u529f\u80fd\uff0c\u8ba9\u4f60\u7684\u7814\u7a76\u5de5\u4f5c\u66f4\u52a0\u5f97\u5fc3\u5e94\u624b\u3002\u6ce8\u610f\u54e6\uff0c\u8fd9\u4e2a<\/strong>R\u5305\u64cd\u4f5c\u5360\u7528\u5185\u5b58\u6bd4\u8f83\u5927\uff0c\u5efa\u8bae\u4f7f\u7528\u670d\u52a1\u5668<\/strong>\u54e6<\/strong>\uff0c\u6b22\u8fce\u8054\u7cfb\u5c0f\u5e08\u59b9\u79df\u8d41\u6027\u4ef7\u6bd4\u9ad8\u7684\u670d\u52a1\u5668<\/strong>~<\/strong><\/p>\n\n\n\n

\u51c6\u5907\u597d\u4e86\u5417\uff1f\u8ba9\u6211\u4eec\u4e00\u8d77\u5f00\u542f\u8fd9\u573a\u8de8\u72b6\u6001QTL\u5206\u6790\u7684\u63a2\u7d22\u4e4b\u65c5\u5427\uff01\u5982\u679c\u4f60\u5728\u64cd\u4f5c\u8fc7\u7a0b\u4e2d\u9047\u5230\u4efb\u4f55\u95ee\u9898\uff0c\u522b\u72b9\u8c6b\uff0c\u8bb0\u5f97\u8054\u7cfb\u5c0f\u5e08\u59b9\u54e6~<\/strong><\/p>\n\n\n\n

\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u5f00\u59cb\u5b89\u88c5multistateQTL\u5305\u548c\u53e6\u5916\u9700\u8981\u7528\u5230\u7684\u5305\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(c(“QTLExperiment”, “multistateQTL”), version=”devel”)<\/p>\n\n\n\n

\u5b89\u88c5\u597d\u4e86\u4e4b\u540e\uff0c\u8bb0\u5f97\u52a0\u8f7d\u8fd9\u4e9b\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n\n\n\n

library(QTLExperiment)<\/p>\n\n\n\n

library(multistateQTL)<\/p>\n\n\n\n

\u597d\u5566\uff0c\u5927\u5bb6\u51c6\u5907\u597d\u4e86\u5417\uff1f\u8ba9\u6211\u4eec\u73b0\u5728\u5c31\u542f\u7a0b\uff0c\u4e00\u8d77\u8e0f\u4e0a\u8fd9\u573a\u6fc0\u52a8\u4eba\u5fc3\u7684\u6570\u636e\u5206\u6790\u4e4b\u65c5\u5427\uff01<\/p>\n\n\n\n

\u4e00\u3001<\/strong> \u6a21\u62df\u591a\u72b6\u6001QTL\u6570\u636e<\/strong><\/strong><\/p>\n\n\n\n

    \n
  1.  \u4eceGTEx\u4f30\u8ba1\u53c2\u6570<\/strong><\/strong><\/li>\n<\/ol>\n\n\n\n

    multistateQTL \u5305\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u7528\u4e8e\u6a21\u62df\u57fa\u4e8e\u771f\u5b9eQTL\u6c47\u603b\u7edf\u8ba1\u6570\u636e\u7684\u591a\u72b6\u6001QTL\u6570\u636e\u3002\u8fd9\u4e00\u8fc7\u7a0b\u9996\u5148\u9700\u8981\u4eceGTEx (v8) \u516c\u5f00\u7684\u6c47\u603b\u7edf\u8ba1\u6570\u636e\u4e2d\u4f30\u8ba1\u5173\u952e\u53c2\u6570\uff0c\u8fd9\u4e9b\u53c2\u6570\u5c06\u7528\u4e8e\u540e\u7eed\u7684\u6570\u636e\u6a21\u62df\u3002<\/p>\n\n\n\n

    \u4e3a\u4e86\u6a21\u62df\u5177\u6709\u591a\u4e2a\u751f\u7269\u5b66\u72b6\u6001\uff08\u4f8b\u5982\u4e0d\u540c\u7ec4\u7ec7\uff09\u7684QTL\u6548\u5e94\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528 sumstats2qtle \u51fd\u6570\u5c06GTEx\u7684\u6c47\u603b\u7edf\u8ba1\u6570\u636e\u8f6c\u6362\u4e3a QTLExperiment \u5bf9\u8c61\u3002\u8fd9\u4e9b\u6570\u636e\u5305\u62ec\u4e86\u5206\u5b50\u7279\u5f81ID\u3001\u53d8\u5f02ID\u3001\u6548\u5e94\u5927\u5c0f\uff08betas\uff09\u548c\u6807\u51c6\u8bef\u5dee\uff08errors\uff09\uff0c\u53ef\u9009\u5730\u8fd8\u5305\u62ecp\u503c\u6216\u4f3c\u7136\u6bd4\uff08lfsr\uff09\u3002<\/p>\n\n\n\n

    input_path <- system.file(“extdata”, package=”multistateQTL”)<\/p>\n\n\n\n

    state <- c(“lung”, “thyroid”, “spleen”, “blood”)<\/p>\n\n\n\n

    input <- data.frame(<\/p>\n\n\n\n

        state=state,<\/p>\n\n\n\n

        path=paste0(input_path, “\/GTEx_tx_”, state, “.tsv”))<\/p>\n\n\n\n

    gtex <- sumstats2qtle(<\/p>\n\n\n\n

        input,<\/p>\n\n\n\n

        feature_id=”molecular_trait_id”,<\/p>\n\n\n\n

        variant_id=”rsid”,<\/p>\n\n\n\n

        betas=”beta”,<\/p>\n\n\n\n

        errors=”se”,<\/p>\n\n\n\n

        pvalues=”pvalue”,<\/p>\n\n\n\n

        verbose=TRUE)<\/p>\n\n\n\n

    gtex<\/p>\n\n\n\n

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

    head(betas(gtex))<\/p>\n\n\n\n

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

    \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f7f\u7528 qtleEstimate \u51fd\u6570\u6765\u4f30\u8ba1\u663e\u8457\u548c\u975e\u663e\u8457QTL\u7684\u53c2\u6570\uff0c\u5305\u62ec\u5f62\u72b6\u548c\u901f\u7387\u53c2\u6570\uff0c\u8fd9\u4e9b\u53c2\u6570\u5b9a\u4e49\u4e86\u7528\u4e8e\u6a21\u62df\u6548\u5e94\u5927\u5c0f\u548c\u53d8\u5f02\u7cfb\u6570\uff08cv\uff09\u7684\u4f3d\u9a6c\u5206\u5e03\u3002<\/p>\n\n\n\n

    params <- qtleEstimate(gtex, threshSig=0.05, threshNull=0.5)<\/p>\n\n\n\n

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

    \u8fd9\u4e9b\u53c2\u6570\u5141\u8bb8\u6211\u4eec\u6a21\u62df\u51fa\u5728\u81f3\u5c11\u4e00\u4e2a\u72b6\u6001\u4e0b\u663e\u8457\u7684QTL\u7684\u5e73\u5747\u6548\u5e94\u5927\u5c0f\uff0c\u4ee5\u53ca\u5728\u663e\u8457\u548c\u975e\u663e\u8457\u72b6\u6001\u4e0b\u7684QTL\u7684cv\u3002\u901a\u8fc7\u8fd9\u4e9b\u6a21\u62df\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u6df1\u5165\u5730\u7406\u89e3QTL\u5728\u4e0d\u540c\u751f\u7269\u5b66\u72b6\u6001\u4e0b\u7684\u4f5c\u7528\u6a21\u5f0f\u3002<\/p>\n\n\n\n

    \u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u8fd9\u4e9b\u53c2\u6570\u5982\u4f55\u5f71\u54cd\u6a21\u62df\u6570\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 plotSimulationParams \u51fd\u6570\u6765\u53ef\u89c6\u5316\u7531\u8fd9\u4e9b\u53c2\u6570\u5b9a\u4e49\u7684\u4f3d\u9a6c\u5206\u5e03\u3002<\/p>\n\n\n\n

    plotSimulationParams(params=params)<\/p>\n\n\n\n

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

    \u8fd9\u6709\u52a9\u4e8e\u6211\u4eec\u4e86\u89e3\u663e\u8457QTL\u7684\u6a21\u62df\u6548\u5e94\u5927\u5c0f\u503e\u5411\u4e8e\u66f4\u5927\uff0c\u800c\u975e\u663e\u8457QTL\u7684\u6a21\u62dfcv\u503c\u5219\u76f8\u5bf9\u8f83\u5c0f\u3002\u8fd9\u79cd\u53ef\u89c6\u5316\u65b9\u6cd5\u4e3a\u6211\u4eec\u63d0\u4f9b\u4e86\u4e00\u4e2a\u76f4\u89c2\u7684\u65b9\u5f0f\u6765\u8bc4\u4f30\u6a21\u62df\u6570\u636e\u7684\u8d28\u91cf\u548c\u7279\u6027\u3002<\/p>\n\n\n\n

      \n
    • \u6a21\u62df\u4e0d\u540c\u7c7b\u578b\u7684\u5173\u8054<\/strong><\/strong><\/li>\n<\/ul>\n\n\n\n

      \u60f3\u8c61\u4e00\u4e0b\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u7535\u8111\u4e0a\u6a21\u62df\u51fa\u57fa\u56e0\u5728\u4e0d\u540c\u8eab\u4f53\u90e8\u4f4d\u6216\u4e0d\u540c\u73af\u5883\u4e0b\u7684\u4f5c\u7528\u53d8\u5316\uff0c\u8fd9\u542c\u8d77\u6765\u662f\u4e0d\u662f\u5f88\u9177\uff1fmultistateQTL\u8fd9\u4e2a\u5de5\u5177\u5c31\u80fd\u5e2e\u6211\u4eec\u505a\u5230\u8fd9\u4e00\u70b9\u3002<\/p>\n\n\n\n

      \u8fd9\u4e2a\u5de5\u5177\u53ef\u4ee5\u6a21\u62df\u51fa\u56db\u79cd\u60c5\u51b5\uff1a<\/p>\n\n\n\n

        \n
      • \u5168\u5c40\u6548\u5e94\uff1a\u5c31\u662f\u57fa\u56e0\u5728\u6240\u6709\u60c5\u51b5\u4e0b\u90fd\u6709\u5dee\u4e0d\u591a\u7684\u5f71\u54cd\u3002<\/li>\n\n\n\n
      • \u72ec\u7279\u6548\u5e94\uff1a\u67d0\u4e2a\u57fa\u56e0\u53ea\u5728\u7279\u5b9a\u60c5\u51b5\u4e0b\u8d77\u4f5c\u7528\u3002<\/li>\n\n\n\n
      • \u591a\u72b6\u6001\u6548\u5e94\uff1a\u57fa\u56e0\u5728\u51e0\u4e2a\u7279\u5b9a\u60c5\u51b5\u4e0b\u6709\u5f71\u54cd\u3002<\/li>\n\n\n\n
      • \u65e0\u6548\u5e94\uff1a\u57fa\u56e0\u5728\u4efb\u4f55\u60c5\u51b5\u4e0b\u90fd\u4e0d\u53d1\u6325\u4f5c\u7528\u3002<\/li>\n<\/ul>\n\n\n\n

        \u6211\u4eec\u8981\u505a\u7684\u5c31\u662f\u8bbe\u5b9a\u4e00\u4e9b\u53c2\u6570\uff0c\u544a\u8bc9\u7535\u8111\u6211\u4eec\u60f3\u8981\u6a21\u62df\u591a\u5c11\u57fa\u56e0\u53d8\u5316\uff0c\u4ee5\u53ca\u5b83\u4eec\u5c5e\u4e8e\u4e0a\u8ff0\u54ea\u4e00\u79cd\u60c5\u51b5\u3002\u7136\u540e\uff0c\u7535\u8111\u5c31\u4f1a\u968f\u673a\u751f\u6210\u4e00\u5927\u5806\u6570\u636e\uff0c\u6a21\u62df\u51fa\u57fa\u56e0\u5728\u4e0d\u540c\u60c5\u51b5\u4e0b\u7684\u4f5c\u7528\u3002<\/p>\n\n\n\n

        # \u8fd9\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u6a21\u62df\u793a\u4f8b\uff0c\u4e00\u534a\u7684\u57fa\u56e0\u53d8\u5316\u5728\u6240\u6709\u60c5\u51b5\u4e0b\u90fd\u6709\u5f71\u54cd<\/p>\n\n\n\n

        sim <- qtleSimulate(nTests=1000, nStates=6, global=0.5)<\/p>\n\n\n\n

        sim<\/p>\n\n\n\n

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

        head(rowData(sim))<\/p>\n\n\n\n

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

        \u5982\u679c\u4f60\u60f3\u6a21\u62df\u66f4\u590d\u6742\u7684\u60c5\u51b5\uff0c\u6bd4\u5982\u57fa\u56e0\u53d8\u5316\u5728\u4e0d\u540c\u60c5\u51b5\u4e0b\u7684\u6bd4\u4f8b\u4e0d\u540c\uff0c\u4e5f\u53ef\u4ee5\u8f7b\u677e\u505a\u5230\uff1a<\/p>\n\n\n\n

        # \u8fd9\u4e2a\u6a21\u62df\u4e2d\uff0c\u57fa\u56e0\u53d8\u5316\u670920%\u662f\u5168\u5c40\u7684\uff0c40%\u662f\u591a\u72b6\u6001\u7684\uff0c20%\u662f\u72ec\u7279\u7684\uff0c20%\u662f\u65e0\u5f71\u54cd\u7684<\/p>\n\n\n\n

        sim <- qtleSimulate(<\/p>\n\n\n\n

            nStates=10, nFeatures=100, nTests=1000,<\/p>\n\n\n\n

            global=0.2, multi=0.4, unique=0.2, k=2)<\/p>\n\n\n\n

        # \u552f\u4e00\u578bQTL\u7684\u6a21\u62df\u5173\u952e\u6307\u6807\u5feb\u7167<\/p>\n\n\n\n

        head(rowData(subset(sim, QTL == “unique”)))<\/p>\n\n\n\n

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

        # \u591a\u72b6\u6001\u578bQTL\u7684\u6a21\u62df\u5173\u952e\u6307\u6807\u5feb\u7167<\/p>\n\n\n\n

        head(rowData(subset(sim, QTL == “multistate”)))<\/p>\n\n\n\n

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

        # \u6bcf\u4e2a\u72b6\u6001\u7ec4\u7279\u5b9a\u7684QTL\u6570\u91cf<\/p>\n\n\n\n

        table(rowData(subset(sim, QTL == “multistate”))$multistateGroup)<\/p>\n\n\n\n

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

        \u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u6a21\u62df\u51fa\u57fa\u56e0\u5728\u4e0d\u540c\u73af\u5883\u4e0b\u7684\u4f5c\u7528\uff0c\u8fd9\u5bf9\u4e8e\u7814\u7a76\u57fa\u56e0\u5982\u4f55\u5f71\u54cd\u75be\u75c5\u548c\u8eab\u4f53\u529f\u80fd\u975e\u5e38\u6709\u5e2e\u52a9\u3002\u8fd9\u79cd\u6a21\u62df\u65b9\u6cd5\u4e3a\u7814\u7a76\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7075\u6d3b\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u6839\u636e\u7814\u7a76\u7684\u5177\u4f53\u9700\u6c42\u8c03\u6574\u6a21\u62df\u53c2\u6570\u3002<\/p>\n\n\n\n

        \u4e8c\u3001<\/strong>\u5904\u7406\u7f3a\u5931\u6570\u636e<\/strong><\/p>\n\n\n\n

        \u5728\u5206\u6790\u8fc7\u7a0b\u4e2d\uff0c\u7f3a\u5931\u6570\u636e\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u95ee\u9898\u3002multistateQTL\u5de5\u5177\u5305\u63d0\u4f9b\u4e86\u4e24\u4e2a\u5b9e\u7528\u7684\u51fd\u6570\u6765\u5e2e\u52a9\u6211\u4eec\u5904\u7406\u8fd9\u4e2a\u95ee\u9898\uff1agetComplete\u548creplaceNAs\u3002<\/p>\n\n\n\n

        \u4e0b\u9762\u662f\u5982\u4f55\u5728\u6a21\u62df\u6570\u636e\u4e2d\u5e94\u7528\u8fd9\u4e9b\u51fd\u6570\u7684\u4e00\u4e2a\u793a\u4f8b\u3002\u9996\u5148\uff0c\u6211\u4eec\u4eba\u4e3a\u5730\u5728\u6a21\u62df\u6570\u636e\u4e2d\u6dfb\u52a0\u4e86\u4e00\u4e9b\u7f3a\u5931\u503c\uff08NA\uff09\uff1a<\/p>\n\n\n\n

        na_pattern <- sample(seq(1, ncol(sim)*nrow(sim)), 1000)<\/p>\n\n\n\n

        sim_na <- sim<\/p>\n\n\n\n

        assay(sim_na, “betas”)[na_pattern] <- NA<\/p>\n\n\n\n

        assay(sim_na, “errors”)[na_pattern] <- NA<\/p>\n\n\n\n

        assay(sim_na, “lfsrs”)[na_pattern] <- NA<\/p>\n\n\n\n

        message(“Number of simulated tests: “, nrow(sim_na))<\/p>\n\n\n\n

        head(betas(sim_na))<\/p>\n\n\n\n

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

        \u7136\u540e\uff0c\u6211\u4eec\u4f7f\u7528getComplete\u51fd\u6570\u6765\u79fb\u9664\u90a3\u4e9b\u7f3a\u5931\u6570\u636e\u8fc7\u591a\u7684\u884c\uff0c\u63a5\u7740\u4f7f\u7528replaceNAs\u51fd\u6570\u6765\u586b\u8865\u5269\u4f59\u7684\u7f3a\u5931\u503c\u3002<\/p>\n\n\n\n

        sim_na <- getComplete(sim_na, n=0.5, verbose=TRUE)<\/p>\n\n\n\n

        sim_na <- replaceNAs(sim_na, verbose=TRUE)<\/p>\n\n\n\n

        head(betas(sim_na))<\/p>\n\n\n\n

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

        \u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u786e\u4fdd\u5206\u6790\u4e2d\u7684\u6570\u636e\u5c3d\u53ef\u80fd\u5b8c\u6574\uff0c\u4ece\u800c\u63d0\u9ad8\u5206\u6790\u7ed3\u679c\u7684\u53ef\u9760\u6027\u3002\u5904\u7406\u7f3a\u5931\u6570\u636e\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u73af\u8282\uff0cmultistateQTL\u5305\u63d0\u4f9b\u7684\u8fd9\u4e9b\u5de5\u5177\u4f7f\u5f97\u8fd9\u4e2a\u8fc7\u7a0b\u53d8\u5f97\u66f4\u52a0\u7b80\u5355\u9ad8\u6548\u3002<\/p>\n\n\n\n

        \u4e09\u3001<\/strong>\u786e\u5b9a\u663e\u8457\u6027<\/strong><\/p>\n\n\n\n

        multistateQTL\u5de5\u5177\u5305\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e2acallSignificance\u51fd\u6570\uff0c\u5b83\u4f7f\u7528\u5355\u4e00\u6216\u4e24\u6b65\u9608\u503c\u65b9\u6cd5\u5728\u6bcf\u4e2a\u72b6\u6001\u4e0b\u786e\u5b9aQTL\u6d4b\u8bd5\u7684\u663e\u8457\u6027\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u8bbe\u7f6e\u4e00\u4e2a\u5355\u4e00\u7684\u4f3c\u7136\u6bd4\uff08lfsr\uff09\u9608\u503c\u4e3a0.1\u6765\u786e\u5b9a\u6a21\u62dfQTL\u7684\u663e\u8457\u6027\u3002<\/p>\n\n\n\n

        sim <- callSignificance(sim, assay=”lfsrs”, thresh=0.05)<\/p>\n\n\n\n

        \u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u8ba1\u7b97\u6bcf\u4e2a\u72b6\u6001\u4e0b\u663e\u8457\u6d4b\u8bd5\u7684\u4e2d\u4f4d\u6570\u6570\u91cf\uff1a<\/p>\n\n\n\n

        message(“Median number of significant tests per state: “,<\/p>\n\n\n\n

                median(colData(sim)$nSignificant))<\/p>\n\n\n\n

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

        \u56e0\u4e3a\u6211\u4eec\u6709\u6a21\u62df\u7684\u57fa\u51c6\u771f\u503c\uff0c\u6240\u4ee5\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528simPerformance\u51fd\u6570\u5c06\u8fd9\u4e9b\u663e\u8457\u6027\u8c03\u7528\u4e0e\u6a21\u62df\u7ed3\u679c\u8fdb\u884c\u6bd4\u8f83\uff0c\u8be5\u51fd\u6570\u63d0\u4f9b\u4e86\u4ee5\u4e0b\u5168\u5c40\uff08\u5373\u8de8\u6240\u6709\u72b6\u6001\uff09\u7684\u6027\u80fd\u6307\u6807\uff1a<\/p>\n\n\n\n

        sim <- callSignificance(sim, assay=”lfsrs”, thresh=0.001)<\/p>\n\n\n\n

        perf_metrics <- simPerformance(sim)<\/p>\n\n\n\n

        lapply(perf_metrics, FUN=function(x) {round(x, 2)})<\/p>\n\n\n\n

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

        \u8fd9\u4e9b\u6307\u6807\u5305\u62ec\u51c6\u786e\u6027\u3001\u7cbe\u786e\u5ea6\u3001\u53ec\u56de\u7387\u548cF1\u5206\u6570\uff0c\u4ee5\u53ca\u4e00\u4e2a\u6df7\u6dc6\u77e9\u9635\u3002\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u771f\u6b63\u663e\u8457QTL\u7684\u53ec\u56de\u7387\u76f8\u5f53\u4f4e\u3002\u7136\u800c\uff0c\u5982\u679c\u6211\u4eec\u6539\u53d8\u663e\u8457\u6027\u8c03\u7528\u65b9\u6cd5\uff0c\u4f7f\u5176\u66f4\u52a0\u7075\u6d3b\uff1a<\/p>\n\n\n\n

        sim <- callSignificance(<\/p>\n\n\n\n

            sim, mode=”simple”, assay=”lfsrs”,<\/p>\n\n\n\n

            thresh=0.0001, secondThresh=0.0002)<\/p>\n\n\n\n

        simPerformance(sim)$recall<\/p>\n\n\n\n

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

        \u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u771f\u6b63\u663e\u8457QTL\u7684\u53ec\u56de\u7387\uff0c\u4ece\u800c\u66f4\u51c6\u786e\u5730\u8bc6\u522b\u51fa\u5728\u4e0d\u540c\u72b6\u6001\u4e0b\u8d77\u4f5c\u7528\u7684QTL\u3002\u8fd9\u5bf9\u4e8e\u7406\u89e3\u57fa\u56e0\u5982\u4f55\u5728\u4e0d\u540c\u751f\u7269\u5b66\u80cc\u666f\u4e0b\u53d1\u6325\u4f5c\u7528\u81f3\u5173\u91cd\u8981\u3002<\/p>\n\n\n\n

        \u56db\u3001<\/strong>\u53ef\u89c6\u5316\u591a\u72b6\u6001eQTL\u6570\u636e<\/strong><\/strong><\/p>\n\n\n\n

        eQTL\uff08Expression Quantitative Trait Loci\uff09\u662f\u6307\u8868\u8fbe\u91cf\u6570\u91cf\u6027\u72b6\u4f4d\u70b9\uff0c\u8fd9\u662f\u4e00\u79cd\u57fa\u56e0\u7ec4\u533a\u57df\uff0c\u5b83\u4eec\u5f71\u54cd\u57fa\u56e0\u8868\u8fbe\u7684\u6c34\u5e73\u3002\u7b80\u800c\u8a00\u4e4b\uff0ceQTL\u5173\u8054\u7814\u7a76\u5c31\u662f\u4e3a\u4e86\u627e\u51fa\u54ea\u4e9b\u57fa\u56e0\u7ec4\u4e2d\u7684\u4f4d\u70b9\uff08\u901a\u5e38\u662f\u5355\u6838\u82f7\u9178\u591a\u6001\u6027\uff0c\u5373SNPs\uff09\u4f1a\u5f71\u54cd\u57fa\u56e0\u7684\u8868\u8fbe\u91cf\u3002\u8fd9\u79cd\u7814\u7a76\u6709\u52a9\u4e8e\u6211\u4eec\u7406\u89e3\u57fa\u56e0\u8868\u8fbe\u7684\u9057\u4f20\u57fa\u7840\uff0c\u4e5f\u5c31\u662f\u4e3a\u4ec0\u4e48\u5728\u4e0d\u540c\u7684\u4e2a\u4f53\u6216\u7ec4\u7ec7\u4e2d\uff0c\u76f8\u540c\u57fa\u56e0\u7684\u8868\u8fbe\u6c34\u5e73\u4f1a\u6709\u6240\u4e0d\u540c\u3002<\/p>\n\n\n\n

        \u901a\u8fc7\u7814\u7a76eQTL\uff0c\u79d1\u5b66\u5bb6\u4eec\u53ef\u4ee5\u63ed\u793a\u57fa\u56e0\u8868\u8fbe\u7684\u9057\u4f20\u8c03\u63a7\u673a\u5236\uff0c\u8fd9\u5bf9\u4e8e\u7406\u89e3\u590d\u6742\u75be\u75c5\u7684\u9057\u4f20\u5b66\u3001\u4e2a\u4f53\u5bf9\u836f\u7269\u53cd\u5e94\u7684\u5dee\u5f02\uff0c\u4ee5\u53ca\u751f\u7269\u4f53\u5bf9\u73af\u5883\u53d8\u5316\u7684\u9002\u5e94\u6027\u90fd\u6709\u91cd\u8981\u610f\u4e49\u3002\u4f8b\u5982\uff0c\u67d0\u4e9beQTL\u53ef\u80fd\u4e0e\u7279\u5b9a\u75be\u75c5\u7684\u6613\u611f\u6027\u6216\u6cbb\u7597\u53cd\u5e94\u5c31\u53ef\u80fd\u6709\u76f4\u63a5\u7684\u8054\u7cfb\u3002\u56e0\u6b64\uff0ceQTL\u7814\u7a76\u662f\u57fa\u56e0\u7ec4\u5b66\u548c\u9057\u4f20\u5b66\u7814\u7a76\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u9886\u57df\u3002<\/p>\n\n\n\n

        multistateQTL \u5305\u63d0\u4f9b\u4e86\u4e94\u4e2a\u51fd\u6570\u6765\u53ef\u89c6\u5316\u591a\u72b6\u6001eQTL\u6570\u636e\u3002\u8fd9\u4e9b\u51fd\u6570\u57fa\u4e8e ggplot2 \u548c ComplexHeatmap R\u5305\u6784\u5efa\u3002\u8fd9\u4e94\u4e2a\u51fd\u6570\u5206\u522b\u662fplotPairwiseSharing\u3001plotQTLClusters\u3001plotUpSet\u3001plotCompareStates\u548cplotSimulationParams\u3002<\/p>\n\n\n\n

          \n
        1. \u6210\u5bf9\u5171\u4eab<\/strong><\/li>\n<\/ol>\n\n\n\n

          plotPairwiseSharing \u51fd\u6570\u5c55\u793a\u4e86\u6bcf\u5bf9\u72b6\u6001\u4e4b\u95f4\u663e\u8457\u547d\u4e2d\u7684\u6210\u5bf9\u5171\u4eab\u7a0b\u5ea6\u3002\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u5bf9\u8c61\u7684 colData \u4e2d\u7684\u6709\u6548\u5217\u540d\u6765\u6dfb\u52a0\u5217\u6ce8\u91ca\u3002<\/p>\n\n\n\n

          \u5728\u4e0b\u9762\u7684\u56fe\u4e2d\uff0c\u5217\u6839\u636e\u72b6\u6001\u7684\u5e7f\u6cdb\u7ec6\u80de\u7c7b\u578b\uff08multistateGroup\uff09\u6392\u5e8f\u3002\u6211\u4eec\u671f\u671b\u5c5e\u4e8e\u540c\u4e00\u591a\u72b6\u6001\u7ec4\u7684\u72b6\u6001\u66f4\u52a0\u76f8\u5173\uff0c\u5e76\u4e14\u5728\u663e\u8457 eQTLs \u4e0a\u6709\u66f4\u9ad8\u7684\u5171\u4eab\u7a0b\u5ea6\u3002\u5217\u6ce8\u91ca\u7528\u4e8e\u663e\u793a\u6bcf\u4e2a\u72b6\u6001\u7684\u663e\u8457 eQTL \u6570\u91cf\u3002<\/p>\n\n\n\n

          sim_sig <- getSignificant(sim)<\/p>\n\n\n\n

          sim_top <- getTopHits(sim_sig, assay=”lfsrs”, mode=”state”)<\/p>\n\n\n\n

          sim_top <- runPairwiseSharing(sim_top)<\/p>\n\n\n\n

          p1 <- plotPairwiseSharing(sim_top, annotate_cols=c(“nSignificant”, “multistateGroup”))<\/p>\n\n\n\n