[d90d15]: / supplementary_and_QC / Geelehers_method_on_expressions.ipynb

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages(library(\"ridge\"))\n",
    "suppressPackageStartupMessages(library(\"sva\"))\n",
    "suppressPackageStartupMessages(library(\"car\"))\n",
    "suppressPackageStartupMessages(library(\"preprocessCore\"))\n",
    "suppressPackageStartupMessages(library(\"ROCR\"))\n",
    "suppressPackageStartupMessages(library(\"GEOquery\"))\n",
    "suppressPackageStartupMessages(library(\"MLmetrics\"))\n",
    "suppressPackageStartupMessages(library(\"PRROC\"))\n",
    "suppressPackageStartupMessages(library(\"plyr\"))\n",
    "source(\"../Geeleher_with_GDSCr6/scripts/compute_phenotype_function.R\")\n",
    "source(\"../Geeleher_with_GDSCr6/scripts/summarizeGenesByMean.R\")\n",
    "source(\"../Geeleher_with_GDSCr6/scripts/homogenize_data.R\")\n",
    "source(\"../Geeleher_with_GDSCr6/scripts/do_variable_selection.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "root_dir <- \"../../v2/\"\n",
    "# You can play with the preprocessing but the following is what Geeleher et al. 2014 used:\n",
    "powTransP=TRUE\n",
    "lowVarGeneThr=0.2\n",
    "# Number of permutations <---- not used  \n",
    "NUM_PERM=10000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "group_compare_plot <- function(predictedResp,knownRespGroups, \n",
    "                        plot_title=\"\",plot_ylab=\"logIC50\",cohort=\"\"){\n",
    "    # predictedResp is a list of predicted IC50 with sample names in names\n",
    "    # knownRespGroups is a table with samples in rownames and column \"response\" with R and S\n",
    "\n",
    "    R_samples <- row.names(knownRespGroups[knownRespGroups[\"response\"] == \"R\",])\n",
    "    S_samples <-  row.names(knownRespGroups[knownRespGroups[\"response\"] == \"S\",])\n",
    "    predictedRespGrouped <- list(\"Resistant\"=predictedResp[R_samples],\n",
    "                                  \"Sensitive\"=predictedResp[S_samples])\n",
    "    \n",
    "    boxplot(predictedRespGrouped, outline=FALSE, border=\"grey\", \n",
    "            ylab=\"logIC50\",\n",
    "            main=plot_title)\n",
    "    stripchart(predictedRespGrouped , vertical=TRUE, pch=20, method=\"jitter\", add=TRUE)\n",
    "    # T-test whether logIC50 in R is greater than in S group\n",
    "    #ttest_res <- t.test(predictedRespGrouped$\"Resistant\",predictedRespGrouped$\"Sensitive\", alternative=\"greater\")\n",
    "    #cat(cohort,\"\\n\\tT-test p-value:\", ttest_res$p.value,\n",
    "    #\"\\n\\tCI95%:\",ttest_res$conf.int,\n",
    "    #\"\\n\\tmeans:\",ttest_res$estimate,\"\\n\")\n",
    "    return (predictedRespGrouped)\n",
    "}\n",
    "\n",
    "run_Geelehers_method <- function(testDataFile,testResponseFile,trainingDataFile,trainingResponseFile,\n",
    "                       powTransP=TRUE,lowVarGeneThr=0.2,cohort=cohort,drug=drug){\n",
    "    # read training data and reorder \n",
    "    trainingData <- read.csv(trainingDataFile, as.is=TRUE, check.names=FALSE,sep = \"\\t\", row.names = 1, header= TRUE)\n",
    "    trainingResponse <- read.csv(trainingResponseFile, as.is=TRUE, check.names=FALSE,sep = \"\\t\", row.names = 1, header= TRUE)\n",
    "    trainingData <- as.matrix(trainingData[,row.names(trainingResponse)])\n",
    "    #trainingData <- 2^trainingData\n",
    "    trainingI50 <- trainingResponse$logIC50\n",
    "    names(trainingI50 ) <- rownames(trainingResponse)\n",
    "    \n",
    "    \n",
    "    # read testing data and reorder \n",
    "    testData <- read.table(testDataFile,sep = \"\\t\", row.names = 1, header= TRUE,as.is=TRUE, check.names=FALSE)\n",
    "    testResponse <- read.csv(testResponseFile, as.is=TRUE, check.names=FALSE,sep = \"\\t\", row.names = 1, header= TRUE)\n",
    "    testData <- as.matrix(testData[,row.names(testResponse)])\n",
    "    #testData <- 2^testData\n",
    "    \n",
    "    # statistics\n",
    "    shared_genes = intersect(row.names(trainingData),row.names(testData))\n",
    "    cat(paste0(cohort,\" \",drug,\"\\n\"))\n",
    "    cat(paste0(\"genes in training cohort: \", dim(trainingData)[1],\"\\tsamples: \", dim(trainingData)[2],\"\\n\",\n",
    "               \"genes in testing cohort: \", dim(testData)[1],\"\\tsamples: \", dim(testData)[2],\"\\n\",\n",
    "               \"shared:\", length(shared_genes),\"\\n\"))\n",
    "    \n",
    "    # predict response for testing cohort\n",
    "    sink(\"/dev/null\") \n",
    "    predictedIC50 <- suppressMessages(calcPhenotype(testData, trainingData, trainingI50,\n",
    "                               powerTransformPhenotype=powTransP,\n",
    "                               removeLowVaryingGenes=lowVarGeneThr,minNumSamples=2))\n",
    "    sink()\n",
    "    \n",
    "    options(repr.plot.width=15, repr.plot.height=5)\n",
    "    par(mfrow=c(1,4))\n",
    "\n",
    "    sink(\"/dev/null\") \n",
    "    \n",
    "    tmp<-group_compare_plot(trainingI50,trainingResponse, \n",
    "                plot_title=paste0(\"Training: \",\"GDSC\"),cohort = \"GDSC\")\n",
    "    sink()\n",
    "    tmp <-0\n",
    "    \n",
    "    # testing \n",
    "\n",
    "    predictedRespGrouped <- group_compare_plot(predictedIC50,testResponse, \n",
    "                plot_title=paste0(\"Prediction: \",cohort),cohort = cohort)\n",
    "\n",
    "    # ROC curve and AUC\n",
    "    AUC_list  <- getAUC(predictedRespGrouped,NUM_PERM=NUM_PERM)\n",
    "    performanceObj  <- AUC_list$\"performanceObj\"\n",
    "    plot(performanceObj, main=paste0(\"ROC \",\"for \",drug,\" in \",cohort))\n",
    "    abline(0, 1, col=\"grey\", lty=2)\n",
    "    \n",
    "    # area under PR curve\n",
    "    AUPRC_list <- getAUPRC(predictedRespGrouped,NUM_PERM=NUM_PERM)\n",
    "\n",
    "    cat(\"\\n\\n\")\n",
    "    return(\n",
    "        c(list(\"predResp\"=predictedRespGrouped,\"trueResp\"=testResponse),AUC_list,AUPRC_list))\n",
    "    #return (list(\"testExprs\"=testData,\"testResp\"=testResponse, \n",
    "    #                \"trainingExprs\"=trainingData,\"trainingResp\"=trainingResponse))\n",
    "} \n",
    "\n",
    "getAUC <- function(predictedRespGrouped,NUM_PERM=1000){\n",
    "    # calculate AUC on the real data \n",
    "    predictedResp <- c(predictedRespGrouped$\"Sensitive\",predictedRespGrouped$\"Resistant\")\n",
    "    cat(paste0(\"S:\",length(predictedRespGrouped$\"Sensitive\"),\" R:\",length(predictedRespGrouped$\"Resistant\")))\n",
    "    trueResp <- c(rep(\"Sensitive\", length(predictedRespGrouped$\"Sensitive\")), \n",
    "                  rep(\"Resistant\", length(predictedRespGrouped$\"Resistant\")))\n",
    "    \n",
    "    predictionResult <- prediction(predictedResp, trueResp, label.ordering=c(\"Sensitive\", \"Resistant\"))\n",
    "    \n",
    "    performanceObj <- performance(predictionResult, measure = \"tpr\", x.measure = \"fpr\")\n",
    "    realDataAUC <- performance(predictionResult, measure = \"auc\")@\"y.values\"[[1]]\n",
    "    cat(paste0(\"\\nAUC:    \", realDataAUC,\"\\n\"))\n",
    "    \n",
    "    # permute true response labels NUM_PERM and calculate AUCs\n",
    "    AUCs <- numeric()\n",
    "    for(i in 1:NUM_PERM)\n",
    "    {\n",
    "      # permute response labels\n",
    "      permutedTrueResp <- sample(trueResp)\n",
    "      predPerm <- prediction(predictedResp,permutedTrueResp,label.ordering=c(\"Sensitive\", \"Resistant\"))\n",
    "      AUCs[i] <- performance(predPerm, measure = \"auc\")@\"y.values\"[[1]]\n",
    "    }\n",
    "    permutationPvalue <- sum(AUCs  > realDataAUC)/NUM_PERM\n",
    "    cat(paste0(\"ROC Permuatation p-value:    \", permutationPvalue,\"\\n\"))\n",
    "    cat(paste0(\"average AUC in permutations:    \", mean(AUCs),\"\\n\"))\n",
    "    return(list(\"performanceObj\"=performanceObj,\n",
    "                \"AUC\"=realDataAUC,\"AUC_perm_avg\"=mean(AUCs),\"AUC_pval\"=permutationPvalue,\n",
    "                \"S\"=length(predictedRespGrouped$\"Sensitive\"),\"R\"=length(predictedRespGrouped$\"Resistant\")))\n",
    "}\n",
    "\n",
    "getAUPRC <- function(predictedRespGrouped,NUM_PERM=1000){\n",
    "    # calculate AUC on the real data \n",
    "    predictedResp <- c(predictedRespGrouped$\"Sensitive\",predictedRespGrouped$\"Resistant\")\n",
    "    true_labels <- c(rep(1,length(predictedRespGrouped$\"Sensitive\")),rep(0,length(predictedRespGrouped$\"Resistant\")))\n",
    "    \n",
    "    \n",
    "    pred_obj <- ROCR::prediction(predictedResp, true_labels)\n",
    "    perf_obj <- ROCR::performance(pred_obj, measure = \"prec\", x.measure = \"rec\")\n",
    "    ROCR::plot(perf_obj, ylim = c(0,1), xlim = c(0,1))\n",
    "\n",
    "    #realDataAUPRC <- MLmetrics::PRAUC(y_pred = predictedResp, y_true = true_labels)\n",
    "    realDataAUPRC <- PRROC::pr.curve(predictedResp,weights.class0 = true_labels)$auc.davis.goadrich\n",
    "    cat(paste0(\"\\nAUPRC:    \", realDataAUPRC,\"\\n\"))\n",
    "    \n",
    "    # permute true response labels NUM_PERM and calculate AUCs\n",
    "    AUPRCs <- numeric()\n",
    "    for(i in 1:NUM_PERM)\n",
    "    {\n",
    "      # permute response labels\n",
    "      permuted_labels <- sample(true_labels)\n",
    "      AUPRCs[i] <- PRROC::pr.curve(predictedResp,weights.class0 = permuted_labels)$auc.davis.goadrich\n",
    "      #  MLmetrics::PRAUC(y_pred = predictedResp, y_true = permuted_labels)\n",
    "    }\n",
    "    permutationPvalue <- sum(AUPRCs  > realDataAUPRC)/NUM_PERM\n",
    "    cat(paste0(\"AUPRC Permuatation p-value:    \", permutationPvalue,\"\\n\"))\n",
    "    P_freq <- round(length(predictedRespGrouped$\"Sensitive\")/length(true_labels),2)\n",
    "    cat(paste0(\"average AUPRC in permutations:    \", round(mean(AUPRCs),2),\"; P/(P+N):\",P_freq,\"\\n\"))\n",
    "    return(list(\"AUPRC\"=realDataAUPRC,\"AUPRC_perm_avg\"=mean(AUPRCs),\n",
    "                \"AUPRC_pval\"=permutationPvalue,\"P_freq\"=P_freq))\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expression only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Table 2 cohorts \n",
    "# ,list(\"drug\"=\"Cisplatin\",\"cohort\"=\"GSE18864,GSE23554,TCGA\")\n",
    "pairs <- list(list(\"drug\"=\"Bortezomib\",\"cohort\"=\"GSE55145,GSE9782-GPL96\"),\n",
    "             list(\"drug\"=\"Cisplatin\",\"cohort\"=\"GSE18864,GSE23554,TCGA\"),\n",
    "             list(\"drug\"=\"Docetaxel\",\"cohort\"=\"GSE6434,GSE25065,GSE28796,TCGA\"),\n",
    "             list(\"drug\"=\"Paclitaxel\",\"cohort\"=\"GSE15622,GSE22513,GSE25065,TCGA,PDX\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSE55145,GSE9782-GPL96 Bortezomib\n",
      "genes in training cohort: 11609\tsamples: 391\n",
      "genes in testing cohort: 11609\tsamples: 236\n",
      "shared:11609\n",
      "S:124 R:112\n",
      "AUC:    0.478326612903226\n",
      "ROC Permuatation p-value:    0.7129\n",
      "average AUC in permutations:    0.499711578341014\n",
      "\n",
      "AUPRC:    0.544462367043294\n",
      "AUPRC Permuatation p-value:    0.3207\n",
      "average AUPRC in permutations:    0.53; P/(P+N):0.53\n",
      "\n",
      "\n",
      "GSE18864,GSE23554,TCGA Cisplatin\n",
      "genes in training cohort: 11768\tsamples: 829\n",
      "genes in testing cohort: 11768\tsamples: 118\n",
      "shared:11768\n",
      "S:94 R:24\n",
      "AUC:    0.584219858156028\n",
      "ROC Permuatation p-value:    0.0973\n",
      "average AUC in permutations:    0.499438874113475\n"
     ]
    },
    {
     "data": {
      "image/png": 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/7Pbvh6zDa1X0YR8g4rEQ4LLTaqRL+e+f5wXaXdRXgxHwRvRhHyXoxizzK8R9AP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\nAUXICu5EaJ5UsbrMDyb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0uHQQEQa7me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\nAACArYEIAQAA2Jr/BzyMBflQC/uYAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “ROC for Bortezomib in GSE55145,GSE9782-GPL96”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "AUPRC:    0.748065669139916\n",
      "AUPRC Permuatation p-value:    0.9286\n",
      "average AUPRC in permutations:    0.8; P/(P+N):0.8\n",
      "\n",
      "\n",
      "GSE6434,GSE25065,GSE28796,TCGA Docetaxel\n",
      "genes in training cohort: 8119\tsamples: 829\n",
      "genes in testing cohort: 8119\tsamples: 103\n",
      "shared:8119\n",
      "S:65 R:38\n",
      "AUC:    0.554251012145749\n",
      "ROC Permuatation p-value:    0.1757\n",
      "average AUC in permutations:    0.499267611336032\n"
     ]
    },
    {
     "data": {
      "image/png": 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RyAB6HmoAj5x7cIj6dkFKZwjCDv8CfCE7sI6SqmDTzahb60N/4dJo1hdBBal2wh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QaMdsxsz3hn/wrKYCNh9GK2fjARVFeHA9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\nTkSuQmCC",
      "text/plain": [
       "Plot with title “ROC for Cisplatin in GSE18864,GSE23554,TCGA”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "AUPRC:    0.607461560292718\n",
      "AUPRC Permuatation p-value:    0.7285\n",
      "average AUPRC in permutations:    0.64; P/(P+N):0.63\n",
      "\n",
      "\n",
      "GSE15622,GSE22513,GSE25065,TCGA,PDX Paclitaxel\n",
      "genes in training cohort: 11731\tsamples: 389\n",
      "genes in testing cohort: 11731\tsamples: 196\n",
      "shared:11731\n",
      "S:105 R:91\n",
      "AUC:    0.533437990580848\n",
      "ROC Permuatation p-value:    0.2094\n",
      "average AUC in permutations:    0.499912087912088\n"
     ]
    },
    {
     "data": {
      "image/png": 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0axuhPvEeQGj0Ro3H9I9i8zFCHv8CrClW/FSTNp9kdtZ4ASKmSnZXgvRjkQIfYs\n4x94JMLzO9NlJ4wi5B1ORbgZIPi0OJejGG0FkF/z5SpbeC9GORAhNp/wDzwSYZKCDqpQhLzD\nqQinAsBKcS5HMbqzbMx89tlpGt6LURQh7xHkBk9rjcoGRcg7nIrwXCGoeF+c46AYVRlWJ+Gt\nr1/vNnTqLSdbUIQoQr8ARYjkAqciJPd2PJRmOChGVYbRSbg+sumwscOaR2513KRuMYo9y6AI\nvYR7EZ6MV5QwipB3WInQOKPzF/fokdbZcZNqtUYvniEoQmYnYa154mT5Y46bdF+MYs8ynJL1\n2Xu2vOFOhBe3PlSUD4qQd1iJcGKp8U+1yiQXnLxbLRHOCITxhJhkdY7rjzA6CfPdFCcpMY6b\n9FqMWsGeZTjlEjzR2paOD1y+/V6ssgtCFCH3sBJhqX0kq+1nXhVhTYDC6qTMF4xOwrZDhS4Y\nk99t67hJr8Wot0ARqsVFOC/j3ek7zijMB0XIO6xEGEV/M58tdstrIrzxQuNmAE9mr9g0SaXn\n4JqH0Ul4vnFIjaa1wupfcdyEPcuoC4pQLeSJ8PhBpUciipB3WInwyfFZhIxrf0p9EW5ZS3Mi\nAwEixr533bp2PUB+hbc1eIfVSWg6vGzmEqedaui+1qjKoAjVQp4Ik2WMSW8PipB3WInwaIHI\niyS1fbTNu1e8JFGgoeK9c8JYgN508jKAQRy+QOxZ5k7vsgCwiWU+/MB7McqBCLFnGU6RJ0Ll\noAh5h1nzicQNiYQY13+cvWaruapWvkpKd84ZtQCi6eRopYivhcVz3Z6ed5QMpxqEsgks8+EH\ntifh4XGO63QvQmw+wSkoQsQzmLYj7OB0LduTcBhAJ5vFpwUFzulNX4rdZ5kNR7A9CVdXcVyH\nIkQR8onnIszafzcP+aAIeYepCKOdrmV7Emb8/INtU5+qggjbHA0FqC0uJ7SM6K/5cpUt6p2E\n8+tLBJdVKwcBFCGKUC08F+Hx3Zl5yAdFyDvcidCW+IdkYVgAwPvkUPMn9oqrplEvxqmVnzZh\nehI2uG6zcHiSRH4nV4nswJ5lUIRq4bEIL8cl5SUfFCHvMBXhAqdrVTsJ34DoOBL/62c/2fyW\n+4GKcJ9K+WkURifhxyLhb3/suEmvxaiVHD3LHD3FNnkUoVp4KsIHcXn7NYYi5B0v9DXK/iRM\nEV8f0GvBF3L2LJM+qNbnrLPTOIxOwtbQonPnzsFtnXSSp9di1Ip9zzJjASYzTR5FqBaeinDf\nv3nLB0XIOzyKcGhAhf/oJLMwwFvSmsQscvrxkrMZ58MJjE5C4xcVtxJSyEl7et0Wo7lQHKAq\n0wRRhGrhqQhT89inA4qQdzgU4RkAEPsB2/vKa7tSOxjaprwG+b7sBRCsYERNP4DZSbivyrgM\nH4iQu55l2gJ0Z5ogilAtsPkE4hn8idD0p4GaUKwasykc6tP5b4Sao08BhKUwzYgX2J2EiX0b\nRnhfhBzUGrXn9oRPXPfdLBcUoVp4JEIGv8NQhLzDnwgHC9qDJcJsb3EWfgujL/nqVf+VaT7c\nwPIkXNLXWWsq3YsQe5bhFE9EmLItTxVGRVCEvMOfCPMDhEItYaQE8jlA0XerjzP9EUVNqNNT\nne1J6I0uEXLCgQix+QSneCBC477Deb8kRBHyDn8ibAlQOyBijTCbMXX4MVqM3vplf3mAJkxz\n4QiWJ6G3W4IKoAhRhGrhgQhP7lLc1XY2KELe4U+EdydNDQZobVlM2PK5AQzfNW1xhGkuHIEi\nVB0UIae4F+HVuEQG+aAIeYc/EdJjtyzAQMtCwspAABh76cky3zPOhhdYnoRe7hJBBHuWQRGq\nhXsRbrvGIh8UIe9wKMJekL/jKLGH7SsnqQhXB1ERbhwMYNBpr9vcFaOX92j+EjAHN1GEfOJe\nhHnpYTQbFCHv8CfCy9R7/xPnFhhgGDHdmVOh2h9CXdIgFKFKsI3gqmC78UN4wL5nGeawiuCu\nn28lj+sw00ndDxShuvgigihClvAnwuT8AB+Kc80Bgp9vtFqcv/x0uR+YZsMPvInwRfpL5jbL\nBLmHUQS/j4yqNWrcmIJfOG5CETqFQTUZCV9EEEXIEv5ESLb3HJ8qztCrwIIAkWlsk+cO3kT4\nEUCZLJtlrfcsc6z7QJWfYjKKYIW4/UJHE5srOm5CETrjTiwrE/oigihClnAoQkLuLxg/h7ow\n6eO3OwEY8t4clm94E2HGt++etV3Weq3RKgAvqZsDowiG3n4YkE7IvUjHTShCJ6RuP8cqH19E\nEEXIEh5FmPooAPQWZ49VjshE7RAAACAASURBVHzbRM43iul4lXEm/MCbCHOidRFGATTiomeZ\nWt/OhZ8JmdXUcROK0BHjfgYt6c34IoIoQpbwKMLDQr9qFcglsY9toRgdSJfLa7s0VREUobpM\nNkQu4KLW6IrAmLhizZpEOdlZFKEjp3akM8vHFxFEEbKERxEml6Tie6c7xOwhUjE6VDDjLca5\ncAOKUGXup3DSjvBWMrk6e+ZlJ1tQhA5kbGXYc7ovIogiZAmPIiRXpn0Vd4G6rx+RitFrdQBa\nEfJw1T/kys4stx/3M1CEqsOJCHMHRegIyypa2I6Qd7gUoUBiFMAndJqwafH3SeTkhgySVQcM\nE0KglbbrILKH92IUe5ZhG8HD47Lnl7SWCC3HMgd+4K0doYRnEUQRsoRbEZLtvSYIDScSXgZo\nKa44S68RK9K/i6pkp114FyEHcNWzzOoq2fO735PIV5llDvyQqwgfss3HFxFEEbKEQxGm2PUg\nY3ocIFi8t5ZaHKA7QHG9tStEEaoOJz3L5I5eI5ibCC9uY5sP3hrlHf5E+Ec+w+e2y58CdBFn\nukLAiKzZ7/7LNDcOwGKUd5hVtfj69W5DpzqrNabXCOYiwnuxN9nm44sIoghZwp8IWwBE2NWu\niFsl9g+REgDQlmlOnMC7CLXes0xOVry9nXGKjCK4PrLpsLHDmkduddzk5yI0/bXUOTOdijBt\n+xnGO+CLCKIIWcKfCF8BeNTphpoA48jvXT/VW7VR3kXIQa1RWzYAhJx1/zY5sGqOPU+cLH/M\ncZOfi/AExBRwTqm7ju82HjzI+qeXLyKIImQJfyKMH9TtqM1i2iHLQX3949kZ54IA5jLNTvv4\njQhPjV+s0WvDBJueZb4EgD/YJs8ogvmk+30pMY6b/FyE/4AT3+VK8h7m1Qh8EUEUIUv4E6GF\nq/PF0sn+emIHLaUmqJKdduFQhAf22CxYIphYWLM/YmybT5wpAI8ybIotwCiCbYcKg60nv+vk\nAQGKUF18EUEUIUu4FeGtwhC0l+QUYWZbKIPNJ1jDOoITAEZmL1kieIz+iHmNbUassGtHeGc7\n47r3rCJ4vnFIjaa1wupfcdyEIlQXX0QQRcgSbkW4jhabnxLHJ0zXmI0xxgv8ibAyQPHsJUsE\nMxpASBzbjFjBSYN60+FlM5cccnZ7GUVoIStBjR3wRQRRhCzhVoTxBSBoF7EUoymLt6iRCRfw\nJ8IeAM9lL1l7lkmLvcQ2H2ZwIsLcQRFaOHZAjR3AdoS8w60IyYXvDwkTSYQtAKZKq+//eU2V\n7LQLf8Vo8tQvGT9lUxmuepZxBorQzOU4VYYvRRHyDr8iNGO6Q8jD18DShvBOKch3Us38tIef\nFaObF7F+BJd3bHuWOdpz+B3GyftZBL2PxyJ8EKdOx7YoQt7hToRfl+9gLofEO+mJHYqOJJ8I\nwzB9QUj8X9dX07kvWeanffyrGJ0G8IT3cvMM09ddF1oXygP0ZZy+f0XQB3gqwvQdp9XZARQh\n7/AmwosgNJunGHsbGt4mZApd3jOcvsw2kdggCPgiHIBxP4Jah/di1L5nmWdpLBPVzE4By+k+\nHTHPZ4UCPM04fd4j6HM8FeH9oyr13YAi5B3eRHielkljhBmhxeAkQqbTyYHTZWEQXfUkna9+\n8MNNDLPjAd6LUft6v18BNFIzNyVMpQfWWsvCBwGRa129WQG8R9Dn+EvzCRegCNWFNxGSz0u0\nuS1MhVZn3xPysNsjHxBiShYzoqteYJkXH/BejNqL0PTXXK1dEJIbFaFZqnXpFvPqFrxH0Oeg\nCJE8wp0IrcxoMjyDkEPTl1iL0U4ARVjXY+AA3otRDvoavbpa1b7feI+gz/FIhKn33b9HKShC\n3uFRhF89MdpSdG4zQLR10JIbwwaeY5wVD/BejHIgQmxHqG08EWHWXhVrk6MIeYdDEe4GgF/N\n8xPp/Aa2yXMH78UoipD3CPocT0R4cpeKfU6hCHmHQxH+SeU30zy/3QAxt9kmzx28F6NGdZp2\nsQRFqG08EOGVODUfPaMIeYdDEWZ0DnjC2mHgkW+Xav56QmX4LUaPx3ESO+xZRtu4F2Fi3HU1\nd8C3Ivzt15zbELlwKEJCMs3TtDHPrRF6liEP48Xl4056bfd/uC1GfwB40Wbx63qDmY8Txwjb\nnmVUgNsIagX3Irytbu0B34qwQx+1c/d/uBShha8AQgQFxkXDG3QyCIIWuvuIH8JtMdoKIDBb\nfUcA4DtV8tE83EZQK+i8+URlFGGe4VqEb9Gy8xidvgAQUKfDuUCA0j9p/4ETa7gtRkcDPEas\nPcsIPSRMViUfzcNtBLWCvkWYFYIizDMci3BOQUMg9BAK0ZFUhAC9QoQuR2Pm6G1AQm6L0YzZ\nnwrX81Kt0euNgw3NVWzppWW4jaBWcCPCB+re2SY+FuE5QBHmGX5FmCp471GSdshEEt+uS+e7\n1gcRjY5yrhq8F6OSCOnlIajUI3LeSTiuavK8R9BXnDpg5leXIkzZpvo4lz4V4d8owrzDrwjT\nw2nR2VAqRtMK0Pl99SQR1lAlO+3CezEqifBDgECtjsuLzSc0yQm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3IlxGvdd4/Z/iiCu/0fmVE+hLH4hczzITrkARuhNhyiXFXPxPeN1A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sf+Rs7dVroqtRBNU7oU0n1K9IpPRfahN9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\nQmCC",
      "text/plain": [
       "Plot with title “ROC for Docetaxel in GSE6434,GSE25065,GSE28796,TCGA”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "AUPRC:    0.518488778640108\n",
      "AUPRC Permuatation p-value:    0.7294\n",
      "average AUPRC in permutations:    0.54; P/(P+N):0.54\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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GiphWzAy5IIJfzmEjLUOBkkXK/nPW+c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\ngKkhCAEAwNT+H4GsV0EdZpEKAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “ROC for Paclitaxel in GSE15622,GSE22513,GSE25065,TCGA,PDX”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_table <- list()\n",
    "ds_names <- c()\n",
    "for (pair in pairs){\n",
    "    drug<- pair$\"drug\"\n",
    "    cohort <- pair$\"cohort\"\n",
    "    testDataFile <- paste0(root_dir,\"/exprs/\",cohort,\"_exprs.z.\",drug,\".tsv\")\n",
    "    testResponseFile <- paste0(root_dir,\"/response/\",cohort,\"_response.\",drug,\".tsv\")\n",
    "\n",
    "    trainingDataFile <- paste0(root_dir,\"/exprs/\",\"GDSC\",\"_exprs.z.\",drug,\".tsv\")\n",
    "    trainingResponseFile <- paste0(root_dir,\"/response/\",\"GDSC\",\"_response.\",drug,\".tsv\")\n",
    "\n",
    "    res <- run_Geelehers_method(testDataFile,testResponseFile,trainingDataFile,trainingResponseFile,\n",
    "                           powTransP=powTransP,lowVarGeneThr=lowVarGeneThr,cohort=cohort,drug=drug)\n",
    "    res <- c(res$\"AUC\",res$\"AUC_pval\",res$\"AUPRC\",res$\"AUPRC_pval\",res$\"P_freq\",res$\"S\",res$\"R\")\n",
    "    ds_names <- c(ds_names, paste0(cohort,\"_\",drug))\n",
    "    results_table  <- c(results_table , list(res))\n",
    "}\n",
    "\n",
    "results_table  <- plyr::ldply(results_table)\n",
    "colnames(results_table) <- c(\"AUC\",\"AUC_pval\",\"AUPRC\",\"AUPRC_pval\",\"P_freq\",\"S\",\"R\")\n",
    "row.names(results_table) <-  ds_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 4 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>AUC</th><th scope=col>AUC_pval</th><th scope=col>AUPRC</th><th scope=col>AUPRC_pval</th><th scope=col>P_freq</th><th scope=col>S</th><th scope=col>R</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>GSE55145,GSE9782-GPL96_Bortezomib</th><td>0.4783266</td><td>0.7129</td><td>0.5444624</td><td>0.3207</td><td>0.53</td><td>124</td><td>112</td></tr>\n",
       "\t<tr><th scope=row>GSE18864,GSE23554,TCGA_Cisplatin</th><td>0.5842199</td><td>0.0973</td><td>0.7480657</td><td>0.9286</td><td>0.80</td><td> 94</td><td> 24</td></tr>\n",
       "\t<tr><th scope=row>GSE6434,GSE25065,GSE28796,TCGA_Docetaxel</th><td>0.5542510</td><td>0.1757</td><td>0.6074616</td><td>0.7285</td><td>0.63</td><td> 65</td><td> 38</td></tr>\n",
       "\t<tr><th scope=row>GSE15622,GSE22513,GSE25065,TCGA,PDX_Paclitaxel</th><td>0.5334380</td><td>0.2094</td><td>0.5184888</td><td>0.7294</td><td>0.54</td><td>105</td><td> 91</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & AUC & AUC\\_pval & AUPRC & AUPRC\\_pval & P\\_freq & S & R\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tGSE55145,GSE9782-GPL96\\_Bortezomib & 0.4783266 & 0.7129 & 0.5444624 & 0.3207 & 0.53 & 124 & 112\\\\\n",
       "\tGSE18864,GSE23554,TCGA\\_Cisplatin & 0.5842199 & 0.0973 & 0.7480657 & 0.9286 & 0.80 &  94 &  24\\\\\n",
       "\tGSE6434,GSE25065,GSE28796,TCGA\\_Docetaxel & 0.5542510 & 0.1757 & 0.6074616 & 0.7285 & 0.63 &  65 &  38\\\\\n",
       "\tGSE15622,GSE22513,GSE25065,TCGA,PDX\\_Paclitaxel & 0.5334380 & 0.2094 & 0.5184888 & 0.7294 & 0.54 & 105 &  91\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 7\n",
       "\n",
       "| <!--/--> | AUC &lt;dbl&gt; | AUC_pval &lt;dbl&gt; | AUPRC &lt;dbl&gt; | AUPRC_pval &lt;dbl&gt; | P_freq &lt;dbl&gt; | S &lt;dbl&gt; | R &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| GSE55145,GSE9782-GPL96_Bortezomib | 0.4783266 | 0.7129 | 0.5444624 | 0.3207 | 0.53 | 124 | 112 |\n",
       "| GSE18864,GSE23554,TCGA_Cisplatin | 0.5842199 | 0.0973 | 0.7480657 | 0.9286 | 0.80 |  94 |  24 |\n",
       "| GSE6434,GSE25065,GSE28796,TCGA_Docetaxel | 0.5542510 | 0.1757 | 0.6074616 | 0.7285 | 0.63 |  65 |  38 |\n",
       "| GSE15622,GSE22513,GSE25065,TCGA,PDX_Paclitaxel | 0.5334380 | 0.2094 | 0.5184888 | 0.7294 | 0.54 | 105 |  91 |\n",
       "\n"
      ],
      "text/plain": [
       "                                               AUC       AUC_pval AUPRC    \n",
       "GSE55145,GSE9782-GPL96_Bortezomib              0.4783266 0.7129   0.5444624\n",
       "GSE18864,GSE23554,TCGA_Cisplatin               0.5842199 0.0973   0.7480657\n",
       "GSE6434,GSE25065,GSE28796,TCGA_Docetaxel       0.5542510 0.1757   0.6074616\n",
       "GSE15622,GSE22513,GSE25065,TCGA,PDX_Paclitaxel 0.5334380 0.2094   0.5184888\n",
       "                                               AUPRC_pval P_freq S   R  \n",
       "GSE55145,GSE9782-GPL96_Bortezomib              0.3207     0.53   124 112\n",
       "GSE18864,GSE23554,TCGA_Cisplatin               0.9286     0.80    94  24\n",
       "GSE6434,GSE25065,GSE28796,TCGA_Docetaxel       0.7285     0.63    65  38\n",
       "GSE15622,GSE22513,GSE25065,TCGA,PDX_Paclitaxel 0.7294     0.54   105  91"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.4.1 (2017-06-30)\n",
       "Platform: x86_64-pc-linux-gnu (64-bit)\n",
       "Running under: Ubuntu 16.04.1 LTS\n",
       "\n",
       "Matrix products: default\n",
       "BLAS: /home/olga/anaconda2/lib/R/lib/libRblas.so\n",
       "LAPACK: /home/olga/anaconda2/lib/R/lib/libRlapack.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] parallel  stats     graphics  grDevices utils     datasets  methods  \n",
       "[8] base     \n",
       "\n",
       "other attached packages:\n",
       " [1] plyr_1.8.4            PRROC_1.3.1           MLmetrics_1.1.1      \n",
       " [4] GEOquery_2.46.15      Biobase_2.38.0        BiocGenerics_0.24.0  \n",
       " [7] ROCR_1.0-7            gplots_3.0.1.1        preprocessCore_1.40.0\n",
       "[10] car_2.1-4             sva_3.26.0            BiocParallel_1.12.0  \n",
       "[13] genefilter_1.60.0     mgcv_1.8-28           nlme_3.1-141         \n",
       "[16] ridge_2.2            \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] tidyr_0.8.3          bit64_0.9-7          jsonlite_1.6        \n",
       " [4] splines_3.4.1        gtools_3.8.1         assertthat_0.2.1    \n",
       " [7] stats4_3.4.1         blob_1.2.0           pillar_1.4.2        \n",
       "[10] RSQLite_2.1.2        backports_1.1.4      lattice_0.20-38     \n",
       "[13] quantreg_5.51        glue_1.3.1           limma_3.34.9        \n",
       "[16] uuid_0.1-2           digest_0.6.20        minqa_1.2.4         \n",
       "[19] htmltools_0.3.6      Matrix_1.2-17        XML_3.98-1.20       \n",
       "[22] pkgconfig_2.0.2      SparseM_1.77         purrr_0.3.2         \n",
       "[25] xtable_1.8-4         gdata_2.18.0         lme4_1.1-21         \n",
       "[28] MatrixModels_0.4-1   tibble_2.1.3         annotate_1.56.2     \n",
       "[31] IRanges_2.12.0       repr_1.0.1           nnet_7.3-12         \n",
       "[34] pbkrtest_0.4-7       survival_2.44-1.1    magrittr_1.5        \n",
       "[37] crayon_1.3.4         memoise_1.1.0        evaluate_0.14       \n",
       "[40] MASS_7.3-51.4        xml2_1.2.2           tools_3.4.1         \n",
       "[43] hms_0.5.0            matrixStats_0.54.0   S4Vectors_0.16.0    \n",
       "[46] AnnotationDbi_1.40.0 compiler_3.4.1       caTools_1.17.1.2    \n",
       "[49] rlang_0.4.0          grid_3.4.1           RCurl_1.95-4.12     \n",
       "[52] nloptr_1.2.1         pbdZMQ_0.3-3         IRkernel_1.0.2      \n",
       "[55] bitops_1.0-6         base64enc_0.1-3      boot_1.3-23         \n",
       "[58] DBI_1.0.0            R6_2.4.0             dplyr_0.8.3         \n",
       "[61] bit_1.1-14           zeallot_0.1.0        KernSmooth_2.23-15  \n",
       "[64] readr_1.3.1          IRdisplay_0.7.0      Rcpp_1.0.2          \n",
       "[67] vctrs_0.2.0          tidyselect_0.2.5    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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