12534 lines (12533 with data), 1.2 MB
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: MASS\n",
"\n",
"Loading required package: NMF\n",
"\n",
"Loading required package: pkgmaker\n",
"\n",
"Loading required package: registry\n",
"\n",
"Loading required package: rngtools\n",
"\n",
"Loading required package: cluster\n",
"\n",
"NMF - BioConductor layer [OK] | Shared memory capabilities [NO: bigmemory] | Cores 27/28\n",
"\n",
" To enable shared memory capabilities, try: install.extras('\n",
"NMF\n",
"')\n",
"\n",
"Loading required package: mclust\n",
"\n",
"Package 'mclust' version 5.4.6\n",
"Type 'citation(\"mclust\")' for citing this R package in publications.\n",
"\n",
"Loading required package: InterSIM\n",
"\n",
"Loading required package: tools\n",
"\n",
"Loading required package: ade4\n",
"\n",
"\n",
"Attaching package: ‘ade4’\n",
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"The following object is masked from ‘package:BiocGenerics’:\n",
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" score\n",
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"Attaching package: ‘GPArotation’\n",
"\n",
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"The following object is masked from ‘package:NMF’:\n",
"\n",
" entropy\n",
"\n",
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"Attaching package: ‘MOFAtools’\n",
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" featureNames, featureNames<-, predict, sampleNames, sampleNames<-\n",
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" predict\n",
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"Loading required package: JADE\n",
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"gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.\n",
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" logit\n",
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]
},
{
"ename": "ERROR",
"evalue": "Error in eval(expr, envir, enclos): 找不到对象'metadata'\n",
"output_type": "error",
"traceback": [
"Error in eval(expr, envir, enclos): 找不到对象'metadata'\nTraceback:\n"
]
}
],
"source": [
"library(\"ggplot2\")\n",
"library(\"clusterCrit\")\n",
"source(\"runfactorization.R\")\n",
"\n",
"# Parameters for the plots\n",
"dot_size <- 1.5\n",
"dot_alpha <- 1.0\n",
"xlabel <- \"Factor 1\"\n",
"ylabel <- \"Factor 2\"\n",
"\n",
"# Load annotations from the metadata\n",
"sample_annot <- metadata[, c(\"sample.rna\", \"celltype\")]\n",
"\n",
"# Folder for results\n",
"results_folder <- \"../results_single_cell/\"\n",
"# Create output folder\n",
"dir.create(results_folder, showWarnings = FALSE)\n",
"\n",
"# Run factorization methods\n",
"out <- runfactorization(\"../data/single-cell/\",\n",
" c(\"CellLines_RNAseqCounts.txt\", \"CellLines_ATACseqCounts.txt\"),\n",
" 2, \n",
" sep=\"\\t\", \n",
" filtering=\"stringent\")\n",
"\n",
"c_index <- numeric(0)\n",
"\n",
"# For each factorization method\n",
"for(i in 1:length(out$factorizations)){\n",
" \n",
" # Get factorization result\n",
" factors <- out$factorizations[[i]][[1]]\n",
"\n",
" # Delete NAs\n",
" factors <- factors[!is.na(factors[,1]) & !is.na(factors[,2]), ]\n",
" sample_annot <- sample_annot[!is.na(sample_annot[,1]) & !is.na(sample_annot[,2]), ]\n",
"\n",
" # Data to be plotted\n",
" df <- data.frame(x = factors[,1], y = factors[,2], color_by = sample_annot[,2])\n",
" # Plot results\n",
" p <- ggplot(df, aes_string(x = \"x\", y = \"y\")) + \n",
" geom_point(aes_string(color = \"color_by\"), size=dot_size, alpha=dot_alpha) + \n",
" xlab(xlabel) + ylab(ylabel) +\n",
" # scale_shape_manual(values=c(19,1,2:18)[seq_along(unique(shape_by))]) +\n",
" theme(plot.margin = margin(20, 20, 10, 10), \n",
" axis.text = element_text(size = rel(1), color = \"black\"), \n",
" axis.title = element_text(size = 16), \n",
" axis.title.y = element_text(size = rel(1.1), margin = margin(0, 10, 0, 0)), \n",
" axis.title.x = element_text(size = rel(1.1), margin = margin(10, 0, 0, 0)), \n",
" axis.line = element_line(color = \"black\", size = 0.5), \n",
" axis.ticks = element_line(color = \"black\", size = 0.5),\n",
" panel.border = element_blank(), \n",
" panel.grid.major = element_blank(),\n",
" panel.grid.minor = element_blank(), \n",
" panel.background = element_blank(),\n",
" legend.key = element_rect(fill = \"white\"),\n",
" legend.text = element_text(size = 16),\n",
" legend.title = element_text(size =16)\n",
" )\n",
" p + scale_color_manual(values=c(\"#0072B2\", \"#D55E00\", \"#CC79A7\"))\n",
" # Export plot as JPEG image\n",
" ggsave(paste0(results_folder, \"plot_\",out$method[i],\".jpg\"))\n",
"\n",
" # Encode cell type annotations by numeric codes\n",
" ann <- factor(sample_annot[,2], levels=c(\"HCT\", \"Hela\", \"K562\"))\n",
" ann <- as.integer(ann)\n",
" # Compare factors and annotations\n",
" c_index <- c(c_index, intCriteria(factors, as.integer(ann), crit=c(\"C_index\"))$c_index)\n",
"\n",
"}\n",
"\n",
"# Build output table\n",
"report_cindex <- data.frame(method=out$method, cindex=c_index)\n",
"\n",
"# Export results as one tab-separated table\n",
"write.table(report_cindex, file = paste0(results_folder, \"singlecell_cindex.txt\"), \n",
" sep=\"\\t\", col.names=FALSE, row.names=FALSE, quote=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"##### runfactoization runs all the considered multi-omics factorization\n",
"### the required inputs are:\n",
"### \"folder\" corresponding to the path to the folder where the input files are contained, in the idea that all omics matrices are organized inside a unique folder\n",
"### \"file.names\" corresponding to a vector containing the names of all the omics files\n",
"### \"num.factors\" containing the number of factors in which we desire to decompose the matrices\n",
"### \"sep=\" \"\" corresponding to the separator used in the omics files required to properly read them\n",
"### \"single.cell\" indicating if the data are single cell data. In this case the filtering of the data will be more intense in respect to other data types\n",
"### \"filtering\"\n",
"\n",
"### the input files need to be log2 transformed before running the analysis\n",
"\n",
"\n",
"library(\"RGCCA\")\n",
"library(\"r.jive\")\n",
"library(\"IntNMF\")\n",
"library(\"omicade4\")\n",
"library(\"MSFA\")\n",
"library(\"GPArotation\")\n",
"library(\"MOFAtools\")\n",
"library(\"tensorBSS\")\n",
"source(\"tICA.R\")\n",
"library(\"iCluster\")\n",
"\n",
"testrunfactorization <- function(folder,file.names,num.factors,sep=\" \",filtering=\"none\"){\n",
" factorizations<-list()\n",
" t<-1\n",
" method<-numeric(0)\n",
" \n",
" num.factors<-as.numeric(num.factors)\n",
"\n",
" \n",
" ##creating list of omics\n",
" omics <- list()\n",
" for(i in 1:length(file.names)){\n",
" omics[[i]]<-as.matrix(read.table(paste(folder,file.names[i],sep=\"/\"),sep=sep,row.names=1,header=T))\n",
" }\n",
" \n",
" ####\n",
" #omics<-lapply(omics, function(x) t(x))\n",
" ######\n",
" \n",
" ##restricting to common samples and filtering\n",
" samples<-colnames(omics[[1]])\n",
" for(j in 1:length(omics)){\n",
" samples<-intersect(samples,colnames(omics[[j]]))\n",
" }\n",
" for(j in 1:length(omics)){\n",
" omics[[j]]<-omics[[j]][,samples]\n",
" if(filtering!=\"none\"){\n",
" x<-apply( omics[[j]],1,sd)\n",
" x<-as.matrix(sort(x, decreasing = T))\n",
" w<-which(x>0)\n",
" if(filtering==\"stringent\"){\n",
" selected<-rownames(x)[1:min(w[length(w)],5000)]\n",
" }else{\n",
" selected<-rownames(x)[1:min(w[length(w)],6000)]\n",
" }\n",
" m<-match(rownames(omics[[j]]),selected)\n",
" w<-which(!is.na(m))\n",
" omics[[j]]<-omics[[j]][w,]\n",
" }else{\n",
" omics[[j]]<-omics[[j]][which(apply(omics[[j]],2,sd)>0),]\n",
" }\n",
" } \n",
" write.table(omics, file = \"omics.txt\", \n",
" sep=\"\\t\", col.names=FALSE, row.names=FALSE, quote=FALSE)\n",
"\n",
" \n",
" ##RGCCA \n",
" factorizations_RGCCA<-rgcca(lapply(omics, function(x) t(x)), ncomp = rep(num.factors, length(omics)), scheme = \"centroid\", scale = TRUE, init = \"svd\",bias = TRUE, tol = 1e-08, verbose = F)\n",
" factors_rgcca<-as.matrix(factorizations_RGCCA$Y[[1]])\n",
" metagenes_rgcca <- list()\n",
" for(j in 1:length(omics)){\n",
" metagenes_rgcca[[j]]<-as.matrix(factorizations_RGCCA$a[[j]])\n",
" rownames(metagenes_rgcca[[j]])<-rownames(omics[[j]])\n",
" colnames(metagenes_rgcca[[j]])<-1:num.factors\n",
" }\n",
" factorizations[[t]]<-list(factors_rgcca,metagenes_rgcca)\n",
" t<-t+1\n",
" method<-c(method,\"RGCCA\")\n",
" \n",
"# ###MCIA\n",
"# omics_pos<-list()\n",
"# for(j in 1:length(omics)){\n",
"# if(min(omics[[j]])<0){\n",
"# omics_pos[[j]]<-omics[[j]]+abs(min(omics[[j]]))\n",
"# }else{\n",
"# omics_pos[[j]]<-omics[[j]]\n",
"# }\n",
"# omics_pos[[j]]<-omics_pos[[j]]/max(omics_pos[[j]])\n",
"# }\n",
"# factorizations_mcia<-mcia(omics_pos, cia.nf = num.factors)\n",
"# factors_mcia<-as.matrix(factorizations_mcia$mcoa$SynVar)\n",
"# metagenes_mcia<-list()\n",
"# for(j in 1:length(omics)){\n",
"# metagenes_mcia[[j]]<-as.matrix(factorizations_mcia$mcoa$axis[1:dim(omics[[j]])[1],])\n",
"# rownames(metagenes_mcia[[j]])<-rownames(omics[[j]])\n",
"# colnames(metagenes_mcia[[j]])<-1:num.factors\n",
"# }\n",
"# factorizations[[t]]<-list(factors_mcia,metagenes_mcia)\n",
"# t<-t+1\n",
"# method<-c(method,\"MCIA\")\n",
" \n",
"# ###MOFA\n",
"# MOFAobject <- createMOFAobject(omics)\n",
"# DataOptions <- getDefaultDataOptions()\n",
"# ModelOptions <- getDefaultModelOptions(MOFAobject)\n",
"# ModelOptions$numFactors <- num.factors\n",
"# TrainOptions <- getDefaultTrainOptions()\n",
" \n",
"# MOFAobject <- prepareMOFA(\n",
"# MOFAobject,\n",
"# DataOptions = DataOptions,\n",
"# ModelOptions = ModelOptions,\n",
"# TrainOptions = TrainOptions\n",
"# )\n",
"# MOFAobject <- runMOFA(MOFAobject)\n",
"# metagenes_mofa <- getWeights(\n",
"# MOFAobject\n",
"# )\n",
" \n",
"# factors_mofa <- getFactors(\n",
"# MOFAobject\n",
"# )\n",
"# factorizations[[t]]<-list(factors_mofa,metagenes_mofa)\n",
"# t<-t+1\n",
"# method<-c(method,\"MOFA\")\n",
" \n",
"# # # ###MSFA\n",
"# # if(filtering==\"none\" || filtering==\"stringent\"){\n",
"# # start_value <- start_msfa(X_s = omics_pos, k = num.factors, j_s = c(num.factors,num.factors,num.factors))\n",
"# # mle <- ecm_msfa(omics_pos, start_value, trace = FALSE)\n",
"# # factors_msfa<-as.matrix(mle$Phi)\n",
"# # rownames(factors)<-colnames(omics[[1]])\n",
"# # colnames(factors)<-1:num.factors\n",
"# # metagenes_msfa<-list()\n",
"# # for(j in 1:length(omics)){\n",
"# # metagenes_msfa[[j]]<-t(omics[[j]]) %*% solve(cor(t(omics[[j]]))) %*% factors_msfa\n",
"# # rownames(metagenes_msfa[[j]])<-rownames(omics[[j]])\n",
"# # colnames(metagenes_msfa[[j]])<-1:num.factors\n",
"# # }\n",
"# # factorizations[[t]]<-list(factors_msfa,metagenes_msfa)\n",
"# # t<-t+1\n",
"# # method<-c(method,\"MSFA\")\n",
"# # }\n",
" \n",
"# ###iCluster\n",
"# factorizations_icluster<-iCluster2(lapply(omics, function(x) t(x)), k=num.factors+1)\n",
"# factors_icluster<-as.matrix(t(factorizations_icluster$expZ))\n",
"# metagenes_icluster<-list()\n",
"# for(j in 1:length(omics)){\n",
"# metagenes_icluster[[j]]<-as.matrix(factorizations_icluster$W[1:dim(omics[[j]])[1],])\n",
"# rownames(metagenes_icluster[[j]])<-rownames(omics[[j]])\n",
"# colnames(metagenes_icluster[[j]])<-1:num.factors\n",
"# }\n",
"# factorizations[[t]]<-list(factors_icluster,metagenes_icluster)\n",
"# t<-t+1\n",
"# method<-c(method,\"iCluster\")\n",
" \n",
"# ###intNMF\n",
"# factorizations_intnmf<-nmf.mnnals(dat=lapply(omics_pos, function(x) t(x)), k=num.factors)\n",
"# factors_intNMF<-as.matrix(factorizations_intnmf$W)\n",
"# colnames(factors_intNMF)<-1:num.factors\n",
"# metagenes_intNMF<-list()\n",
"# for(j in 1:length(omics)){\n",
"# metagenes_intNMF[[j]]<-t(factorizations_intnmf$H[[j]]) \n",
"# rownames(metagenes_intNMF[[j]])<-rownames(omics[[j]])\n",
"# colnames(metagenes_intNMF[[j]])<-1:num.factors\n",
"# }\n",
"# factorizations[[t]]<-list(factors_intNMF,metagenes_intNMF)\n",
"# t<-t+1\n",
"# method<-c(method,\"intNMF\")\n",
" \n",
" \n",
"# ### JIVE\n",
"# factorizations_jive<-jive(omics, rankJ=num.factors, rankA = rep(num.factors, length(omics)), method = \"given\", conv = \"default\", maxiter = 100, showProgress=FALSE)\n",
"# rankJV <- factorizations_jive$rankJ;\n",
"# rankIV.v <- factorizations_jive$rankA;\n",
"# J<-numeric(0)\n",
"# ng<-0\n",
"# metagenes_jive <- list();\n",
"# for(j in 1:length(omics)){\n",
"# J <- rbind(J,factorizations_jive$joint[[j]]);\n",
"# ng<-c(ng,dim(factorizations_jive$joint[[j]])[1])\n",
"# }\n",
"# svd.o <- svd(J);\n",
"# jV <- svd.o$v %*% diag(svd.o$d);\n",
"# for(j in 1:length(omics)){\n",
"# metagenes_jive[[j]] <- svd.o$u[(1+sum(ng[1:j])):sum(ng[1:j+1]),1:rankJV]; ###error in dimension\n",
"# rownames(metagenes_jive[[j]])<-rownames(omics[[j]])\n",
"# colnames(metagenes_jive[[j]])<-1:num.factors\n",
"# }\n",
"# factors_jive=jV[,1:rankJV]\n",
"# rownames(factors_jive)<-colnames(omics[[1]])\n",
"# colnames(factors_jive)<-1:num.factors\n",
"# factorizations[[t]]<-list(factors_jive,metagenes_jive)\n",
"# t<-t+1\n",
"# method<-c(method,\"JIVE\")\n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
"\n",
" \n",
"\n",
" \n",
"# ###tICA\n",
"# omics_tensor<-list()\n",
"# for(j in 1:length(omics)){\n",
"# omics_tensor[[j]]<-cor(omics[[j]], method = \"spearman\")\n",
"# }\n",
" \n",
"# S<-vector(length = dim(omics[[1]])[2]*dim(omics[[1]])[2]*length(omics))\n",
"# dim(S) <- c(length(omics), dim(omics[[1]])[2], dim(omics[[1]])[2])\n",
"# for(j in 1:length(omics)){\n",
"# S[j,,]<-t(omics_tensor[[j]])\n",
"# }\n",
"# tICA<-DoTICA(S,num.factors,method=\"FOBI\")\n",
"# factors_tica<-as.matrix(tICA$signals)\n",
"# rownames(factors_tica)<-colnames(omics[[1]])\n",
"# metagenes_tica<-list()\n",
"# for(j in 1:length(omics)){\n",
"# metagenes_tica[[j]]<-omics[[j]] %*% ginv(t(tICA$signals))\n",
"# rownames(metagenes_tica[[j]])<-rownames(omics[[j]])\n",
"# colnames(metagenes_tica[[j]])<-1:num.factors\n",
"# }\n",
"# factorizations[[t]]<-list(factors_tica,metagenes_tica)\n",
"# t<-t+1\n",
"# method<-c(method,\"tICA\")\n",
"\n",
"\n",
" \n",
"\n",
"\n",
" \n",
"# # ###scikit-fusion\n",
"# system(\"mkdir ../data/scikit/\")\n",
"# temp.folder<-'../data/scikit/'\n",
"# files<-\"\"\n",
"# for(j in 1:length(omics)){\n",
"# write.table(omics[[j]],paste(temp.folder,\"/omics\",j,\".txt\",sep=\"\"),sep=\" \",col.names=T, row.names=T)\n",
"# files<-paste(files,paste(\"omics\",j,\".txt\",sep=\"\"),sep=\" \")\n",
"# }\n",
"# system(paste(\"python ./scikit_fusion.py\", \"'../data/scikit/'\",\"'../data/scikit/'\",num.factors,\"' '\",files,sep=\" \"))\n",
"# factors_scikit<-as.matrix(read.table(paste(temp.folder,\"signals.txt\",sep=\"\"),sep=\"\\t\",header=F))\n",
"# colnames(factors_scikit)<-1:num.factors\n",
"# rownames(factors_scikit)<-colnames(omics[[1]])\n",
"# metagenes_scikit<-list()\n",
"# for(j in 1:length(omics)){\n",
"# metagenes_scikit[[j]]<-as.matrix(read.table(paste(temp.folder,\"projomics\",j,\".txt\",sep=\"\"),sep=\"\\t\",header=F))\n",
"# rownames(metagenes_scikit[[j]])<-rownames(omics[[j]])\n",
"# colnames(metagenes_scikit[[j]])<-1:num.factors\n",
"# }\n",
"# factorizations[[t]]<-list(factors_scikit,metagenes_scikit)\n",
"# t<-t+1\n",
"# method<-c(method,\"scikit-fusion\")\n",
"# system(\"rm -r ../data/scikit/\")\n",
" \n",
" \n",
" out<-list(factorizations=factorizations,method=method)\n",
" \n",
" return(out)\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"out <- testrunfactorization(\"../data/single-cell/\",\n",
" c(\"CellLines_RNAseqCounts.txt\", \"CellLines_ATACseqCounts.txt\"),\n",
" 2, \n",
" sep=\"\\t\", \n",
" filtering=\"stringent\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"<dl>\n",
"\t<dt>$factorizations</dt>\n",
"\t\t<dd><ol>\n",
"\t<li><ol>\n",
"\t<li><table>\n",
"<caption>A matrix: 206 × 2 of type dbl</caption>\n",
"<thead>\n",
"\t<tr><th></th><th scope=col>comp1</th><th scope=col>comp2</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_10</th><td>-0.603619302</td><td>-0.2329965</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_14</th><td>-0.632492584</td><td>-0.1884142</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_15</th><td>-0.758610238</td><td>-0.1918410</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_16</th><td>-0.369532528</td><td>-0.2553742</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_19</th><td>-0.798700859</td><td>-0.1613043</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_2</th><td>-0.778697261</td><td>-0.1842477</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_21</th><td>-0.680400157</td><td>-0.2063110</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_22</th><td>-0.910482370</td><td>-0.1192303</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_24</th><td>-0.660502514</td><td>-0.2012571</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_27</th><td>-0.677422847</td><td>-0.2139758</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_28</th><td> 0.043902569</td><td>-0.3470660</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_29</th><td> 0.022682913</td><td>-0.3759271</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_3</th><td>-0.630363953</td><td>-0.2160441</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_34</th><td>-0.631909751</td><td>-0.1856665</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_35</th><td> 0.008799254</td><td>-0.3601215</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_37</th><td>-0.144011648</td><td>-0.3134329</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_38</th><td>-0.748957989</td><td>-0.2064419</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_39</th><td>-0.518834437</td><td>-0.2525882</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_4</th><td>-0.185956750</td><td>-0.2837601</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_44</th><td>-0.354688160</td><td>-0.2644386</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_45</th><td>-0.047822974</td><td>-0.3455467</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_48</th><td>-0.853088112</td><td>-0.1394189</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L01_7</th><td>-0.539051277</td><td>-0.2259481</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L02_49</th><td> 0.677212516</td><td>-0.3745267</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L02_52</th><td>-0.253533343</td><td>-0.2902493</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L02_53</th><td> 0.396343303</td><td>-0.3539985</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L02_54</th><td>-0.681967479</td><td>-0.2130827</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L02_55</th><td>-0.451779929</td><td>-0.2428144</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L02_59</th><td>-0.554110697</td><td>-0.2617444</td></tr>\n",
"\t<tr><th scope=row>HCT_CL100049490_L02_61</th><td>-0.540449915</td><td>-0.2402839</td></tr>\n",
"\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L01_5</th><td> 0.40127262</td><td>0.3368034</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L01_6</th><td>-0.12129390</td><td>0.5291087</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L01_7</th><td> 0.04607682</td><td>0.4939682</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L01_9</th><td> 0.15609522</td><td>0.4098625</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_49</th><td>-0.02730089</td><td>0.4767028</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_51</th><td> 0.20785087</td><td>0.4087220</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_52</th><td>-0.06744686</td><td>0.5397458</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_53</th><td>-0.11326876</td><td>0.6072958</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_55</th><td> 0.04346271</td><td>0.4116644</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_57</th><td>-0.20343559</td><td>0.5564069</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_63</th><td>-0.01162893</td><td>0.4774225</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_66</th><td> 0.06700249</td><td>0.5013100</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_67</th><td> 0.29218496</td><td>0.3199849</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_70</th><td> 0.11527284</td><td>0.4289749</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_71</th><td> 0.21414290</td><td>0.4133064</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_72</th><td>-0.01905255</td><td>0.5592848</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_73</th><td>-0.01926271</td><td>0.4749965</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_74</th><td>-0.15351760</td><td>0.5652242</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_75</th><td>-0.11927437</td><td>0.4847888</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_76</th><td> 0.17409235</td><td>0.4915614</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_79</th><td> 0.03370218</td><td>0.4736911</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_81</th><td> 0.31950782</td><td>0.3477363</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_83</th><td> 0.30898912</td><td>0.3371745</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_84</th><td> 0.50802986</td><td>0.2540375</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_85</th><td> 0.18623889</td><td>0.3718062</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_86</th><td> 0.40216492</td><td>0.2846719</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_87</th><td> 0.11154862</td><td>0.3684545</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_88</th><td> 0.07699620</td><td>0.4872814</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_92</th><td> 0.02257859</td><td>0.4266070</td></tr>\n",
"\t<tr><th scope=row>K562_CL100050926_L02_93</th><td> 0.21579373</td><td>0.3457352</td></tr>\n",
"</tbody>\n",
"</table>\n",
"</li>\n",
"\t<li><ol>\n",
"\t<li><table>\n",
"<caption>A matrix: 5000 × 2 of type dbl</caption>\n",
"<thead>\n",
"\t<tr><th></th><th scope=col>1</th><th scope=col>2</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>ENSG00000000003</th><td>-0.0194675539</td><td>-2.310484e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000000457</th><td>-0.0135462145</td><td> 1.132626e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000000460</th><td>-0.0179017651</td><td> 1.412284e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000000971</th><td> 0.0024298197</td><td> 3.620967e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000001167</th><td>-0.0118871390</td><td>-5.055053e-04</td></tr>\n",
"\t<tr><th scope=row>ENSG00000001460</th><td>-0.0172038999</td><td> 2.391407e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000001461</th><td>-0.0169015945</td><td> 8.839285e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000001497</th><td>-0.0123823691</td><td>-3.919588e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000001561</th><td>-0.0165555150</td><td>-1.749135e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000001629</th><td>-0.0103663604</td><td> 6.470224e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000001631</th><td>-0.0179466500</td><td> 1.078110e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000002016</th><td>-0.0118580943</td><td>-5.246298e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000002587</th><td>-0.0181319944</td><td>-2.517125e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000002745</th><td>-0.0185095723</td><td>-2.263403e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000002822</th><td>-0.0142500834</td><td> 1.203101e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000002919</th><td>-0.0148761142</td><td> 1.636216e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000003147</th><td>-0.0201687769</td><td> 8.425888e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000003249</th><td>-0.0137883858</td><td>-6.171106e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000003393</th><td>-0.0174065454</td><td>-2.490121e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000003400</th><td>-0.0007799666</td><td> 2.441851e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000003402</th><td>-0.0192791908</td><td> 8.600155e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000003509</th><td>-0.0090055968</td><td> 1.331874e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000003756</th><td>-0.0130149280</td><td> 1.031357e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000004487</th><td>-0.0162811788</td><td>-5.955571e-05</td></tr>\n",
"\t<tr><th scope=row>ENSG00000004534</th><td>-0.0190989089</td><td> 6.246953e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000004700</th><td>-0.0122761722</td><td> 7.829217e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000004766</th><td>-0.0103215377</td><td> 7.009564e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000004799</th><td>-0.0027963643</td><td> 2.759022e-02</td></tr>\n",
"\t<tr><th scope=row>ENSG00000004864</th><td>-0.0097251768</td><td> 9.486824e-03</td></tr>\n",
"\t<tr><th scope=row>ENSG00000004866</th><td>-0.0045770365</td><td> 2.434532e-02</td></tr>\n",
"\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><th scope=row>ENSG00000263244</th><td>-0.0093672791</td><td> 0.0141358525</td></tr>\n",
"\t<tr><th scope=row>ENSG00000263711</th><td> 0.0042563311</td><td> 0.0351159552</td></tr>\n",
"\t<tr><th scope=row>ENSG00000263753</th><td> 0.0081431651</td><td> 0.0270350702</td></tr>\n",
"\t<tr><th scope=row>ENSG00000264247</th><td>-0.0164839650</td><td>-0.0064174099</td></tr>\n",
"\t<tr><th scope=row>ENSG00000264578</th><td>-0.0166216792</td><td> 0.0082167545</td></tr>\n",
"\t<tr><th scope=row>ENSG00000266642</th><td>-0.0062311988</td><td> 0.0127834594</td></tr>\n",
"\t<tr><th scope=row>ENSG00000266962</th><td>-0.0130063997</td><td>-0.0029643233</td></tr>\n",
"\t<tr><th scope=row>ENSG00000266976</th><td> 0.0007304652</td><td> 0.0388744487</td></tr>\n",
"\t<tr><th scope=row>ENSG00000267080</th><td>-0.0121886814</td><td> 0.0023167624</td></tr>\n",
"\t<tr><th scope=row>ENSG00000267100</th><td>-0.0135843697</td><td> 0.0107590595</td></tr>\n",
"\t<tr><th scope=row>ENSG00000267232</th><td>-0.0072129681</td><td> 0.0274794329</td></tr>\n",
"\t<tr><th scope=row>ENSG00000267453</th><td>-0.0184421541</td><td>-0.0221717372</td></tr>\n",
"\t<tr><th scope=row>ENSG00000267575</th><td>-0.0106293390</td><td> 0.0122894407</td></tr>\n",
"\t<tr><th scope=row>ENSG00000267577</th><td>-0.0142395230</td><td>-0.0101016144</td></tr>\n",
"\t<tr><th scope=row>ENSG00000267607</th><td> 0.0027208328</td><td> 0.0328242452</td></tr>\n",
"\t<tr><th scope=row>ENSG00000267680</th><td>-0.0011176447</td><td> 0.0109814468</td></tr>\n",
"\t<tr><th scope=row>ENSG00000268739</th><td>-0.0192734880</td><td>-0.0236676468</td></tr>\n",
"\t<tr><th scope=row>ENSG00000269343</th><td>-0.0124815997</td><td> 0.0027126993</td></tr>\n",
"\t<tr><th scope=row>ENSG00000269495</th><td>-0.0181453581</td><td>-0.0246676567</td></tr>\n",
"\t<tr><th scope=row>ENSG00000269604</th><td>-0.0157908194</td><td>-0.0040931947</td></tr>\n",
"\t<tr><th scope=row>ENSG00000269609</th><td>-0.0157587969</td><td> 0.0133707888</td></tr>\n",
"\t<tr><th scope=row>ENSG00000270228</th><td>-0.0151931728</td><td> 0.0024190930</td></tr>\n",
"\t<tr><th scope=row>ENSG00000271122</th><td>-0.0130051944</td><td>-0.0050009692</td></tr>\n",
"\t<tr><th scope=row>ENSG00000272159</th><td>-0.0177595017</td><td>-0.0231183114</td></tr>\n",
"\t<tr><th scope=row>ENSG00000272686</th><td>-0.0050063613</td><td> 0.0175759286</td></tr>\n",
"\t<tr><th scope=row>ENSG00000272758</th><td>-0.0180323164</td><td> 0.0105091410</td></tr>\n",
"\t<tr><th scope=row>ENSG00000272763</th><td>-0.0174015808</td><td>-0.0226860695</td></tr>\n",
"\t<tr><th scope=row>ENSG00000273015</th><td>-0.0059732457</td><td> 0.0206014436</td></tr>\n",
"\t<tr><th scope=row>ENSG00000273192</th><td>-0.0189750659</td><td>-0.0004423689</td></tr>\n",
"\t<tr><th scope=row>ENSG00000273344</th><td>-0.0072887441</td><td> 0.0252858786</td></tr>\n",
"</tbody>\n",
"</table>\n",
"</li>\n",
"\t<li><table>\n",
"<caption>A matrix: 5000 × 2 of type dbl</caption>\n",
"<thead>\n",
"\t<tr><th></th><th scope=col>1</th><th scope=col>2</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>Hela_peak_28b</th><td>0.01725913</td><td> 0.0040756034</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_28c</th><td>0.01516821</td><td> 0.0059456306</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_34a</th><td>0.01789808</td><td> 0.0028503418</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_41</th><td>0.01492479</td><td> 0.0095819055</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_47</th><td>0.01379148</td><td> 0.0127460495</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_53</th><td>0.01722123</td><td> 0.0038403405</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_69a</th><td>0.01778134</td><td>-0.0006469863</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_73</th><td>0.01413129</td><td> 0.0090056593</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_85c</th><td>0.01528905</td><td> 0.0038015508</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_87</th><td>0.01423577</td><td> 0.0084034122</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_101b</th><td>0.01734376</td><td> 0.0073712181</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_104a</th><td>0.01603512</td><td> 0.0041737312</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_118b</th><td>0.01876127</td><td> 0.0049053650</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_127</th><td>0.01788265</td><td> 0.0086111188</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_128</th><td>0.01385191</td><td> 0.0067127552</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_140b</th><td>0.01870552</td><td>-0.0260796399</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_141</th><td>0.01347466</td><td> 0.0106974000</td></tr>\n",
"\t<tr><th scope=row>HCT116_peak_221</th><td>0.02327259</td><td>-0.0200003599</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_146c</th><td>0.01869036</td><td> 0.0016566129</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_151b</th><td>0.01287070</td><td> 0.0048502650</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_165b</th><td>0.01498533</td><td> 0.0043384852</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_166a</th><td>0.01428953</td><td> 0.0102777979</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_182a</th><td>0.01676060</td><td> 0.0050847575</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_191a</th><td>0.01444036</td><td> 0.0084127239</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_195</th><td>0.01364276</td><td> 0.0061150608</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_231b</th><td>0.01594876</td><td> 0.0080887840</td></tr>\n",
"\t<tr><th scope=row>HCT116_peak_372a</th><td>0.01902758</td><td> 0.0032786119</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_245</th><td>0.01417920</td><td> 0.0162453705</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_252b</th><td>0.01891022</td><td> 0.0033845595</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_254b</th><td>0.01427603</td><td> 0.0076013328</td></tr>\n",
"\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><th scope=row>K562_peak_57467b</th><td>-7.469237e-04</td><td> 4.059980e-02</td></tr>\n",
"\t<tr><th scope=row>K562_peak_57467c</th><td>-1.093510e-03</td><td> 3.425520e-02</td></tr>\n",
"\t<tr><th scope=row>K562_peak_57509b</th><td> 3.261322e-04</td><td> 3.581221e-02</td></tr>\n",
"\t<tr><th scope=row>K562_peak_57574b</th><td>-2.172015e-03</td><td> 3.105348e-02</td></tr>\n",
"\t<tr><th scope=row>K562_peak_57574c</th><td>-2.928692e-03</td><td> 3.626421e-02</td></tr>\n",
"\t<tr><th scope=row>K562_peak_57574d</th><td>-2.471893e-03</td><td> 3.775303e-02</td></tr>\n",
"\t<tr><th scope=row>K562_peak_57574g</th><td>-1.683027e-03</td><td> 4.232230e-02</td></tr>\n",
"\t<tr><th scope=row>K562_peak_57574h</th><td> 5.272538e-05</td><td> 4.176382e-02</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138208</th><td> 3.269637e-03</td><td> 4.175070e-03</td></tr>\n",
"\t<tr><th scope=row>HCT116_peak_93629</th><td> 1.674785e-02</td><td> 8.764308e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138278b</th><td> 1.038759e-02</td><td> 2.993853e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138314</th><td> 1.222216e-02</td><td> 7.092110e-04</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138326c</th><td> 1.743609e-02</td><td> 4.639038e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138400</th><td> 1.100395e-02</td><td> 2.948213e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138542e</th><td> 1.135172e-02</td><td> 1.383667e-02</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138560c</th><td> 1.344246e-02</td><td> 9.839686e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138588a</th><td> 1.114320e-02</td><td> 6.886063e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138597a</th><td> 1.114535e-02</td><td>-8.043704e-04</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138597b</th><td> 1.184878e-02</td><td> 2.092852e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138604b</th><td> 1.162418e-02</td><td> 1.338468e-05</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138639a</th><td> 1.958263e-02</td><td>-1.645798e-02</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138662a</th><td> 7.402616e-03</td><td> 5.148582e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138687b</th><td> 1.632005e-02</td><td> 5.747770e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138702a</th><td> 9.331831e-03</td><td> 6.392104e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138730b</th><td> 1.033829e-02</td><td> 1.086851e-03</td></tr>\n",
"\t<tr><th scope=row>HCT116_peak_94117b</th><td> 1.178224e-02</td><td> 8.664913e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138765c</th><td> 1.216091e-02</td><td> 2.633533e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138772b</th><td> 9.304095e-03</td><td> 7.027795e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138792b</th><td> 1.232495e-02</td><td> 6.474760e-03</td></tr>\n",
"\t<tr><th scope=row>Hela_peak_138817c</th><td> 9.399972e-03</td><td> 1.240935e-02</td></tr>\n",
"</tbody>\n",
"</table>\n",
"</li>\n",
"</ol>\n",
"</li>\n",
"</ol>\n",
"</li>\n",
"</ol>\n",
"</dd>\n",
"\t<dt>$method</dt>\n",
"\t\t<dd>'RGCCA'</dd>\n",
"</dl>\n"
],
"text/latex": [
"\\begin{description}\n",
"\\item[\\$factorizations] \\begin{enumerate}\n",
"\\item \\begin{enumerate}\n",
"\\item A matrix: 206 × 2 of type dbl\n",
"\\begin{tabular}{r|ll}\n",
" & comp1 & comp2\\\\\n",
"\\hline\n",
"\tHCT\\_CL100049490\\_L01\\_10 & -0.603619302 & -0.2329965\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_14 & -0.632492584 & -0.1884142\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_15 & -0.758610238 & -0.1918410\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_16 & -0.369532528 & -0.2553742\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_19 & -0.798700859 & -0.1613043\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_2 & -0.778697261 & -0.1842477\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_21 & -0.680400157 & -0.2063110\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_22 & -0.910482370 & -0.1192303\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_24 & -0.660502514 & -0.2012571\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_27 & -0.677422847 & -0.2139758\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_28 & 0.043902569 & -0.3470660\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_29 & 0.022682913 & -0.3759271\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_3 & -0.630363953 & -0.2160441\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_34 & -0.631909751 & -0.1856665\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_35 & 0.008799254 & -0.3601215\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_37 & -0.144011648 & -0.3134329\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_38 & -0.748957989 & -0.2064419\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_39 & -0.518834437 & -0.2525882\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_4 & -0.185956750 & -0.2837601\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_44 & -0.354688160 & -0.2644386\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_45 & -0.047822974 & -0.3455467\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_48 & -0.853088112 & -0.1394189\\\\\n",
"\tHCT\\_CL100049490\\_L01\\_7 & -0.539051277 & -0.2259481\\\\\n",
"\tHCT\\_CL100049490\\_L02\\_49 & 0.677212516 & -0.3745267\\\\\n",
"\tHCT\\_CL100049490\\_L02\\_52 & -0.253533343 & -0.2902493\\\\\n",
"\tHCT\\_CL100049490\\_L02\\_53 & 0.396343303 & -0.3539985\\\\\n",
"\tHCT\\_CL100049490\\_L02\\_54 & -0.681967479 & -0.2130827\\\\\n",
"\tHCT\\_CL100049490\\_L02\\_55 & -0.451779929 & -0.2428144\\\\\n",
"\tHCT\\_CL100049490\\_L02\\_59 & -0.554110697 & -0.2617444\\\\\n",
"\tHCT\\_CL100049490\\_L02\\_61 & -0.540449915 & -0.2402839\\\\\n",
"\t⋮ & ⋮ & ⋮\\\\\n",
"\tK562\\_CL100050926\\_L01\\_5 & 0.40127262 & 0.3368034\\\\\n",
"\tK562\\_CL100050926\\_L01\\_6 & -0.12129390 & 0.5291087\\\\\n",
"\tK562\\_CL100050926\\_L01\\_7 & 0.04607682 & 0.4939682\\\\\n",
"\tK562\\_CL100050926\\_L01\\_9 & 0.15609522 & 0.4098625\\\\\n",
"\tK562\\_CL100050926\\_L02\\_49 & -0.02730089 & 0.4767028\\\\\n",
"\tK562\\_CL100050926\\_L02\\_51 & 0.20785087 & 0.4087220\\\\\n",
"\tK562\\_CL100050926\\_L02\\_52 & -0.06744686 & 0.5397458\\\\\n",
"\tK562\\_CL100050926\\_L02\\_53 & -0.11326876 & 0.6072958\\\\\n",
"\tK562\\_CL100050926\\_L02\\_55 & 0.04346271 & 0.4116644\\\\\n",
"\tK562\\_CL100050926\\_L02\\_57 & -0.20343559 & 0.5564069\\\\\n",
"\tK562\\_CL100050926\\_L02\\_63 & -0.01162893 & 0.4774225\\\\\n",
"\tK562\\_CL100050926\\_L02\\_66 & 0.06700249 & 0.5013100\\\\\n",
"\tK562\\_CL100050926\\_L02\\_67 & 0.29218496 & 0.3199849\\\\\n",
"\tK562\\_CL100050926\\_L02\\_70 & 0.11527284 & 0.4289749\\\\\n",
"\tK562\\_CL100050926\\_L02\\_71 & 0.21414290 & 0.4133064\\\\\n",
"\tK562\\_CL100050926\\_L02\\_72 & -0.01905255 & 0.5592848\\\\\n",
"\tK562\\_CL100050926\\_L02\\_73 & -0.01926271 & 0.4749965\\\\\n",
"\tK562\\_CL100050926\\_L02\\_74 & -0.15351760 & 0.5652242\\\\\n",
"\tK562\\_CL100050926\\_L02\\_75 & -0.11927437 & 0.4847888\\\\\n",
"\tK562\\_CL100050926\\_L02\\_76 & 0.17409235 & 0.4915614\\\\\n",
"\tK562\\_CL100050926\\_L02\\_79 & 0.03370218 & 0.4736911\\\\\n",
"\tK562\\_CL100050926\\_L02\\_81 & 0.31950782 & 0.3477363\\\\\n",
"\tK562\\_CL100050926\\_L02\\_83 & 0.30898912 & 0.3371745\\\\\n",
"\tK562\\_CL100050926\\_L02\\_84 & 0.50802986 & 0.2540375\\\\\n",
"\tK562\\_CL100050926\\_L02\\_85 & 0.18623889 & 0.3718062\\\\\n",
"\tK562\\_CL100050926\\_L02\\_86 & 0.40216492 & 0.2846719\\\\\n",
"\tK562\\_CL100050926\\_L02\\_87 & 0.11154862 & 0.3684545\\\\\n",
"\tK562\\_CL100050926\\_L02\\_88 & 0.07699620 & 0.4872814\\\\\n",
"\tK562\\_CL100050926\\_L02\\_92 & 0.02257859 & 0.4266070\\\\\n",
"\tK562\\_CL100050926\\_L02\\_93 & 0.21579373 & 0.3457352\\\\\n",
"\\end{tabular}\n",
"\n",
"\\item \\begin{enumerate}\n",
"\\item A matrix: 5000 × 2 of type dbl\n",
"\\begin{tabular}{r|ll}\n",
" & 1 & 2\\\\\n",
"\\hline\n",
"\tENSG00000000003 & -0.0194675539 & -2.310484e-02\\\\\n",
"\tENSG00000000457 & -0.0135462145 & 1.132626e-02\\\\\n",
"\tENSG00000000460 & -0.0179017651 & 1.412284e-02\\\\\n",
"\tENSG00000000971 & 0.0024298197 & 3.620967e-02\\\\\n",
"\tENSG00000001167 & -0.0118871390 & -5.055053e-04\\\\\n",
"\tENSG00000001460 & -0.0172038999 & 2.391407e-03\\\\\n",
"\tENSG00000001461 & -0.0169015945 & 8.839285e-03\\\\\n",
"\tENSG00000001497 & -0.0123823691 & -3.919588e-03\\\\\n",
"\tENSG00000001561 & -0.0165555150 & -1.749135e-02\\\\\n",
"\tENSG00000001629 & -0.0103663604 & 6.470224e-03\\\\\n",
"\tENSG00000001631 & -0.0179466500 & 1.078110e-02\\\\\n",
"\tENSG00000002016 & -0.0118580943 & -5.246298e-03\\\\\n",
"\tENSG00000002587 & -0.0181319944 & -2.517125e-02\\\\\n",
"\tENSG00000002745 & -0.0185095723 & -2.263403e-02\\\\\n",
"\tENSG00000002822 & -0.0142500834 & 1.203101e-02\\\\\n",
"\tENSG00000002919 & -0.0148761142 & 1.636216e-03\\\\\n",
"\tENSG00000003147 & -0.0201687769 & 8.425888e-03\\\\\n",
"\tENSG00000003249 & -0.0137883858 & -6.171106e-03\\\\\n",
"\tENSG00000003393 & -0.0174065454 & -2.490121e-03\\\\\n",
"\tENSG00000003400 & -0.0007799666 & 2.441851e-02\\\\\n",
"\tENSG00000003402 & -0.0192791908 & 8.600155e-03\\\\\n",
"\tENSG00000003509 & -0.0090055968 & 1.331874e-02\\\\\n",
"\tENSG00000003756 & -0.0130149280 & 1.031357e-02\\\\\n",
"\tENSG00000004487 & -0.0162811788 & -5.955571e-05\\\\\n",
"\tENSG00000004534 & -0.0190989089 & 6.246953e-03\\\\\n",
"\tENSG00000004700 & -0.0122761722 & 7.829217e-03\\\\\n",
"\tENSG00000004766 & -0.0103215377 & 7.009564e-03\\\\\n",
"\tENSG00000004799 & -0.0027963643 & 2.759022e-02\\\\\n",
"\tENSG00000004864 & -0.0097251768 & 9.486824e-03\\\\\n",
"\tENSG00000004866 & -0.0045770365 & 2.434532e-02\\\\\n",
"\t⋮ & ⋮ & ⋮\\\\\n",
"\tENSG00000263244 & -0.0093672791 & 0.0141358525\\\\\n",
"\tENSG00000263711 & 0.0042563311 & 0.0351159552\\\\\n",
"\tENSG00000263753 & 0.0081431651 & 0.0270350702\\\\\n",
"\tENSG00000264247 & -0.0164839650 & -0.0064174099\\\\\n",
"\tENSG00000264578 & -0.0166216792 & 0.0082167545\\\\\n",
"\tENSG00000266642 & -0.0062311988 & 0.0127834594\\\\\n",
"\tENSG00000266962 & -0.0130063997 & -0.0029643233\\\\\n",
"\tENSG00000266976 & 0.0007304652 & 0.0388744487\\\\\n",
"\tENSG00000267080 & -0.0121886814 & 0.0023167624\\\\\n",
"\tENSG00000267100 & -0.0135843697 & 0.0107590595\\\\\n",
"\tENSG00000267232 & -0.0072129681 & 0.0274794329\\\\\n",
"\tENSG00000267453 & -0.0184421541 & -0.0221717372\\\\\n",
"\tENSG00000267575 & -0.0106293390 & 0.0122894407\\\\\n",
"\tENSG00000267577 & -0.0142395230 & -0.0101016144\\\\\n",
"\tENSG00000267607 & 0.0027208328 & 0.0328242452\\\\\n",
"\tENSG00000267680 & -0.0011176447 & 0.0109814468\\\\\n",
"\tENSG00000268739 & -0.0192734880 & -0.0236676468\\\\\n",
"\tENSG00000269343 & -0.0124815997 & 0.0027126993\\\\\n",
"\tENSG00000269495 & -0.0181453581 & -0.0246676567\\\\\n",
"\tENSG00000269604 & -0.0157908194 & -0.0040931947\\\\\n",
"\tENSG00000269609 & -0.0157587969 & 0.0133707888\\\\\n",
"\tENSG00000270228 & -0.0151931728 & 0.0024190930\\\\\n",
"\tENSG00000271122 & -0.0130051944 & -0.0050009692\\\\\n",
"\tENSG00000272159 & -0.0177595017 & -0.0231183114\\\\\n",
"\tENSG00000272686 & -0.0050063613 & 0.0175759286\\\\\n",
"\tENSG00000272758 & -0.0180323164 & 0.0105091410\\\\\n",
"\tENSG00000272763 & -0.0174015808 & -0.0226860695\\\\\n",
"\tENSG00000273015 & -0.0059732457 & 0.0206014436\\\\\n",
"\tENSG00000273192 & -0.0189750659 & -0.0004423689\\\\\n",
"\tENSG00000273344 & -0.0072887441 & 0.0252858786\\\\\n",
"\\end{tabular}\n",
"\n",
"\\item A matrix: 5000 × 2 of type dbl\n",
"\\begin{tabular}{r|ll}\n",
" & 1 & 2\\\\\n",
"\\hline\n",
"\tHela\\_peak\\_28b & 0.01725913 & 0.0040756034\\\\\n",
"\tHela\\_peak\\_28c & 0.01516821 & 0.0059456306\\\\\n",
"\tHela\\_peak\\_34a & 0.01789808 & 0.0028503418\\\\\n",
"\tHela\\_peak\\_41 & 0.01492479 & 0.0095819055\\\\\n",
"\tHela\\_peak\\_47 & 0.01379148 & 0.0127460495\\\\\n",
"\tHela\\_peak\\_53 & 0.01722123 & 0.0038403405\\\\\n",
"\tHela\\_peak\\_69a & 0.01778134 & -0.0006469863\\\\\n",
"\tHela\\_peak\\_73 & 0.01413129 & 0.0090056593\\\\\n",
"\tHela\\_peak\\_85c & 0.01528905 & 0.0038015508\\\\\n",
"\tHela\\_peak\\_87 & 0.01423577 & 0.0084034122\\\\\n",
"\tHela\\_peak\\_101b & 0.01734376 & 0.0073712181\\\\\n",
"\tHela\\_peak\\_104a & 0.01603512 & 0.0041737312\\\\\n",
"\tHela\\_peak\\_118b & 0.01876127 & 0.0049053650\\\\\n",
"\tHela\\_peak\\_127 & 0.01788265 & 0.0086111188\\\\\n",
"\tHela\\_peak\\_128 & 0.01385191 & 0.0067127552\\\\\n",
"\tHela\\_peak\\_140b & 0.01870552 & -0.0260796399\\\\\n",
"\tHela\\_peak\\_141 & 0.01347466 & 0.0106974000\\\\\n",
"\tHCT116\\_peak\\_221 & 0.02327259 & -0.0200003599\\\\\n",
"\tHela\\_peak\\_146c & 0.01869036 & 0.0016566129\\\\\n",
"\tHela\\_peak\\_151b & 0.01287070 & 0.0048502650\\\\\n",
"\tHela\\_peak\\_165b & 0.01498533 & 0.0043384852\\\\\n",
"\tHela\\_peak\\_166a & 0.01428953 & 0.0102777979\\\\\n",
"\tHela\\_peak\\_182a & 0.01676060 & 0.0050847575\\\\\n",
"\tHela\\_peak\\_191a & 0.01444036 & 0.0084127239\\\\\n",
"\tHela\\_peak\\_195 & 0.01364276 & 0.0061150608\\\\\n",
"\tHela\\_peak\\_231b & 0.01594876 & 0.0080887840\\\\\n",
"\tHCT116\\_peak\\_372a & 0.01902758 & 0.0032786119\\\\\n",
"\tHela\\_peak\\_245 & 0.01417920 & 0.0162453705\\\\\n",
"\tHela\\_peak\\_252b & 0.01891022 & 0.0033845595\\\\\n",
"\tHela\\_peak\\_254b & 0.01427603 & 0.0076013328\\\\\n",
"\t⋮ & ⋮ & ⋮\\\\\n",
"\tK562\\_peak\\_57467b & -7.469237e-04 & 4.059980e-02\\\\\n",
"\tK562\\_peak\\_57467c & -1.093510e-03 & 3.425520e-02\\\\\n",
"\tK562\\_peak\\_57509b & 3.261322e-04 & 3.581221e-02\\\\\n",
"\tK562\\_peak\\_57574b & -2.172015e-03 & 3.105348e-02\\\\\n",
"\tK562\\_peak\\_57574c & -2.928692e-03 & 3.626421e-02\\\\\n",
"\tK562\\_peak\\_57574d & -2.471893e-03 & 3.775303e-02\\\\\n",
"\tK562\\_peak\\_57574g & -1.683027e-03 & 4.232230e-02\\\\\n",
"\tK562\\_peak\\_57574h & 5.272538e-05 & 4.176382e-02\\\\\n",
"\tHela\\_peak\\_138208 & 3.269637e-03 & 4.175070e-03\\\\\n",
"\tHCT116\\_peak\\_93629 & 1.674785e-02 & 8.764308e-03\\\\\n",
"\tHela\\_peak\\_138278b & 1.038759e-02 & 2.993853e-03\\\\\n",
"\tHela\\_peak\\_138314 & 1.222216e-02 & 7.092110e-04\\\\\n",
"\tHela\\_peak\\_138326c & 1.743609e-02 & 4.639038e-03\\\\\n",
"\tHela\\_peak\\_138400 & 1.100395e-02 & 2.948213e-03\\\\\n",
"\tHela\\_peak\\_138542e & 1.135172e-02 & 1.383667e-02\\\\\n",
"\tHela\\_peak\\_138560c & 1.344246e-02 & 9.839686e-03\\\\\n",
"\tHela\\_peak\\_138588a & 1.114320e-02 & 6.886063e-03\\\\\n",
"\tHela\\_peak\\_138597a & 1.114535e-02 & -8.043704e-04\\\\\n",
"\tHela\\_peak\\_138597b & 1.184878e-02 & 2.092852e-03\\\\\n",
"\tHela\\_peak\\_138604b & 1.162418e-02 & 1.338468e-05\\\\\n",
"\tHela\\_peak\\_138639a & 1.958263e-02 & -1.645798e-02\\\\\n",
"\tHela\\_peak\\_138662a & 7.402616e-03 & 5.148582e-03\\\\\n",
"\tHela\\_peak\\_138687b & 1.632005e-02 & 5.747770e-03\\\\\n",
"\tHela\\_peak\\_138702a & 9.331831e-03 & 6.392104e-03\\\\\n",
"\tHela\\_peak\\_138730b & 1.033829e-02 & 1.086851e-03\\\\\n",
"\tHCT116\\_peak\\_94117b & 1.178224e-02 & 8.664913e-03\\\\\n",
"\tHela\\_peak\\_138765c & 1.216091e-02 & 2.633533e-03\\\\\n",
"\tHela\\_peak\\_138772b & 9.304095e-03 & 7.027795e-03\\\\\n",
"\tHela\\_peak\\_138792b & 1.232495e-02 & 6.474760e-03\\\\\n",
"\tHela\\_peak\\_138817c & 9.399972e-03 & 1.240935e-02\\\\\n",
"\\end{tabular}\n",
"\n",
"\\end{enumerate}\n",
"\n",
"\\end{enumerate}\n",
"\n",
"\\end{enumerate}\n",
"\n",
"\\item[\\$method] 'RGCCA'\n",
"\\end{description}\n"
],
"text/markdown": [
"$factorizations\n",
": 1. 1. \n",
"A matrix: 206 × 2 of type dbl\n",
"\n",
"| <!--/--> | comp1 | comp2 |\n",
"|---|---|---|\n",
"| HCT_CL100049490_L01_10 | -0.603619302 | -0.2329965 |\n",
"| HCT_CL100049490_L01_14 | -0.632492584 | -0.1884142 |\n",
"| HCT_CL100049490_L01_15 | -0.758610238 | -0.1918410 |\n",
"| HCT_CL100049490_L01_16 | -0.369532528 | -0.2553742 |\n",
"| HCT_CL100049490_L01_19 | -0.798700859 | -0.1613043 |\n",
"| HCT_CL100049490_L01_2 | -0.778697261 | -0.1842477 |\n",
"| HCT_CL100049490_L01_21 | -0.680400157 | -0.2063110 |\n",
"| HCT_CL100049490_L01_22 | -0.910482370 | -0.1192303 |\n",
"| HCT_CL100049490_L01_24 | -0.660502514 | -0.2012571 |\n",
"| HCT_CL100049490_L01_27 | -0.677422847 | -0.2139758 |\n",
"| HCT_CL100049490_L01_28 | 0.043902569 | -0.3470660 |\n",
"| HCT_CL100049490_L01_29 | 0.022682913 | -0.3759271 |\n",
"| HCT_CL100049490_L01_3 | -0.630363953 | -0.2160441 |\n",
"| HCT_CL100049490_L01_34 | -0.631909751 | -0.1856665 |\n",
"| HCT_CL100049490_L01_35 | 0.008799254 | -0.3601215 |\n",
"| HCT_CL100049490_L01_37 | -0.144011648 | -0.3134329 |\n",
"| HCT_CL100049490_L01_38 | -0.748957989 | -0.2064419 |\n",
"| HCT_CL100049490_L01_39 | -0.518834437 | -0.2525882 |\n",
"| HCT_CL100049490_L01_4 | -0.185956750 | -0.2837601 |\n",
"| HCT_CL100049490_L01_44 | -0.354688160 | -0.2644386 |\n",
"| HCT_CL100049490_L01_45 | -0.047822974 | -0.3455467 |\n",
"| HCT_CL100049490_L01_48 | -0.853088112 | -0.1394189 |\n",
"| HCT_CL100049490_L01_7 | -0.539051277 | -0.2259481 |\n",
"| HCT_CL100049490_L02_49 | 0.677212516 | -0.3745267 |\n",
"| HCT_CL100049490_L02_52 | -0.253533343 | -0.2902493 |\n",
"| HCT_CL100049490_L02_53 | 0.396343303 | -0.3539985 |\n",
"| HCT_CL100049490_L02_54 | -0.681967479 | -0.2130827 |\n",
"| HCT_CL100049490_L02_55 | -0.451779929 | -0.2428144 |\n",
"| HCT_CL100049490_L02_59 | -0.554110697 | -0.2617444 |\n",
"| HCT_CL100049490_L02_61 | -0.540449915 | -0.2402839 |\n",
"| ⋮ | ⋮ | ⋮ |\n",
"| K562_CL100050926_L01_5 | 0.40127262 | 0.3368034 |\n",
"| K562_CL100050926_L01_6 | -0.12129390 | 0.5291087 |\n",
"| K562_CL100050926_L01_7 | 0.04607682 | 0.4939682 |\n",
"| K562_CL100050926_L01_9 | 0.15609522 | 0.4098625 |\n",
"| K562_CL100050926_L02_49 | -0.02730089 | 0.4767028 |\n",
"| K562_CL100050926_L02_51 | 0.20785087 | 0.4087220 |\n",
"| K562_CL100050926_L02_52 | -0.06744686 | 0.5397458 |\n",
"| K562_CL100050926_L02_53 | -0.11326876 | 0.6072958 |\n",
"| K562_CL100050926_L02_55 | 0.04346271 | 0.4116644 |\n",
"| K562_CL100050926_L02_57 | -0.20343559 | 0.5564069 |\n",
"| K562_CL100050926_L02_63 | -0.01162893 | 0.4774225 |\n",
"| K562_CL100050926_L02_66 | 0.06700249 | 0.5013100 |\n",
"| K562_CL100050926_L02_67 | 0.29218496 | 0.3199849 |\n",
"| K562_CL100050926_L02_70 | 0.11527284 | 0.4289749 |\n",
"| K562_CL100050926_L02_71 | 0.21414290 | 0.4133064 |\n",
"| K562_CL100050926_L02_72 | -0.01905255 | 0.5592848 |\n",
"| K562_CL100050926_L02_73 | -0.01926271 | 0.4749965 |\n",
"| K562_CL100050926_L02_74 | -0.15351760 | 0.5652242 |\n",
"| K562_CL100050926_L02_75 | -0.11927437 | 0.4847888 |\n",
"| K562_CL100050926_L02_76 | 0.17409235 | 0.4915614 |\n",
"| K562_CL100050926_L02_79 | 0.03370218 | 0.4736911 |\n",
"| K562_CL100050926_L02_81 | 0.31950782 | 0.3477363 |\n",
"| K562_CL100050926_L02_83 | 0.30898912 | 0.3371745 |\n",
"| K562_CL100050926_L02_84 | 0.50802986 | 0.2540375 |\n",
"| K562_CL100050926_L02_85 | 0.18623889 | 0.3718062 |\n",
"| K562_CL100050926_L02_86 | 0.40216492 | 0.2846719 |\n",
"| K562_CL100050926_L02_87 | 0.11154862 | 0.3684545 |\n",
"| K562_CL100050926_L02_88 | 0.07699620 | 0.4872814 |\n",
"| K562_CL100050926_L02_92 | 0.02257859 | 0.4266070 |\n",
"| K562_CL100050926_L02_93 | 0.21579373 | 0.3457352 |\n",
"\n",
"\n",
"2. 1. \n",
"A matrix: 5000 × 2 of type dbl\n",
"\n",
"| <!--/--> | 1 | 2 |\n",
"|---|---|---|\n",
"| ENSG00000000003 | -0.0194675539 | -2.310484e-02 |\n",
"| ENSG00000000457 | -0.0135462145 | 1.132626e-02 |\n",
"| ENSG00000000460 | -0.0179017651 | 1.412284e-02 |\n",
"| ENSG00000000971 | 0.0024298197 | 3.620967e-02 |\n",
"| ENSG00000001167 | -0.0118871390 | -5.055053e-04 |\n",
"| ENSG00000001460 | -0.0172038999 | 2.391407e-03 |\n",
"| ENSG00000001461 | -0.0169015945 | 8.839285e-03 |\n",
"| ENSG00000001497 | -0.0123823691 | -3.919588e-03 |\n",
"| ENSG00000001561 | -0.0165555150 | -1.749135e-02 |\n",
"| ENSG00000001629 | -0.0103663604 | 6.470224e-03 |\n",
"| ENSG00000001631 | -0.0179466500 | 1.078110e-02 |\n",
"| ENSG00000002016 | -0.0118580943 | -5.246298e-03 |\n",
"| ENSG00000002587 | -0.0181319944 | -2.517125e-02 |\n",
"| ENSG00000002745 | -0.0185095723 | -2.263403e-02 |\n",
"| ENSG00000002822 | -0.0142500834 | 1.203101e-02 |\n",
"| ENSG00000002919 | -0.0148761142 | 1.636216e-03 |\n",
"| ENSG00000003147 | -0.0201687769 | 8.425888e-03 |\n",
"| ENSG00000003249 | -0.0137883858 | -6.171106e-03 |\n",
"| ENSG00000003393 | -0.0174065454 | -2.490121e-03 |\n",
"| ENSG00000003400 | -0.0007799666 | 2.441851e-02 |\n",
"| ENSG00000003402 | -0.0192791908 | 8.600155e-03 |\n",
"| ENSG00000003509 | -0.0090055968 | 1.331874e-02 |\n",
"| ENSG00000003756 | -0.0130149280 | 1.031357e-02 |\n",
"| ENSG00000004487 | -0.0162811788 | -5.955571e-05 |\n",
"| ENSG00000004534 | -0.0190989089 | 6.246953e-03 |\n",
"| ENSG00000004700 | -0.0122761722 | 7.829217e-03 |\n",
"| ENSG00000004766 | -0.0103215377 | 7.009564e-03 |\n",
"| ENSG00000004799 | -0.0027963643 | 2.759022e-02 |\n",
"| ENSG00000004864 | -0.0097251768 | 9.486824e-03 |\n",
"| ENSG00000004866 | -0.0045770365 | 2.434532e-02 |\n",
"| ⋮ | ⋮ | ⋮ |\n",
"| ENSG00000263244 | -0.0093672791 | 0.0141358525 |\n",
"| ENSG00000263711 | 0.0042563311 | 0.0351159552 |\n",
"| ENSG00000263753 | 0.0081431651 | 0.0270350702 |\n",
"| ENSG00000264247 | -0.0164839650 | -0.0064174099 |\n",
"| ENSG00000264578 | -0.0166216792 | 0.0082167545 |\n",
"| ENSG00000266642 | -0.0062311988 | 0.0127834594 |\n",
"| ENSG00000266962 | -0.0130063997 | -0.0029643233 |\n",
"| ENSG00000266976 | 0.0007304652 | 0.0388744487 |\n",
"| ENSG00000267080 | -0.0121886814 | 0.0023167624 |\n",
"| ENSG00000267100 | -0.0135843697 | 0.0107590595 |\n",
"| ENSG00000267232 | -0.0072129681 | 0.0274794329 |\n",
"| ENSG00000267453 | -0.0184421541 | -0.0221717372 |\n",
"| ENSG00000267575 | -0.0106293390 | 0.0122894407 |\n",
"| ENSG00000267577 | -0.0142395230 | -0.0101016144 |\n",
"| ENSG00000267607 | 0.0027208328 | 0.0328242452 |\n",
"| ENSG00000267680 | -0.0011176447 | 0.0109814468 |\n",
"| ENSG00000268739 | -0.0192734880 | -0.0236676468 |\n",
"| ENSG00000269343 | -0.0124815997 | 0.0027126993 |\n",
"| ENSG00000269495 | -0.0181453581 | -0.0246676567 |\n",
"| ENSG00000269604 | -0.0157908194 | -0.0040931947 |\n",
"| ENSG00000269609 | -0.0157587969 | 0.0133707888 |\n",
"| ENSG00000270228 | -0.0151931728 | 0.0024190930 |\n",
"| ENSG00000271122 | -0.0130051944 | -0.0050009692 |\n",
"| ENSG00000272159 | -0.0177595017 | -0.0231183114 |\n",
"| ENSG00000272686 | -0.0050063613 | 0.0175759286 |\n",
"| ENSG00000272758 | -0.0180323164 | 0.0105091410 |\n",
"| ENSG00000272763 | -0.0174015808 | -0.0226860695 |\n",
"| ENSG00000273015 | -0.0059732457 | 0.0206014436 |\n",
"| ENSG00000273192 | -0.0189750659 | -0.0004423689 |\n",
"| ENSG00000273344 | -0.0072887441 | 0.0252858786 |\n",
"\n",
"\n",
"2. \n",
"A matrix: 5000 × 2 of type dbl\n",
"\n",
"| <!--/--> | 1 | 2 |\n",
"|---|---|---|\n",
"| Hela_peak_28b | 0.01725913 | 0.0040756034 |\n",
"| Hela_peak_28c | 0.01516821 | 0.0059456306 |\n",
"| Hela_peak_34a | 0.01789808 | 0.0028503418 |\n",
"| Hela_peak_41 | 0.01492479 | 0.0095819055 |\n",
"| Hela_peak_47 | 0.01379148 | 0.0127460495 |\n",
"| Hela_peak_53 | 0.01722123 | 0.0038403405 |\n",
"| Hela_peak_69a | 0.01778134 | -0.0006469863 |\n",
"| Hela_peak_73 | 0.01413129 | 0.0090056593 |\n",
"| Hela_peak_85c | 0.01528905 | 0.0038015508 |\n",
"| Hela_peak_87 | 0.01423577 | 0.0084034122 |\n",
"| Hela_peak_101b | 0.01734376 | 0.0073712181 |\n",
"| Hela_peak_104a | 0.01603512 | 0.0041737312 |\n",
"| Hela_peak_118b | 0.01876127 | 0.0049053650 |\n",
"| Hela_peak_127 | 0.01788265 | 0.0086111188 |\n",
"| Hela_peak_128 | 0.01385191 | 0.0067127552 |\n",
"| Hela_peak_140b | 0.01870552 | -0.0260796399 |\n",
"| Hela_peak_141 | 0.01347466 | 0.0106974000 |\n",
"| HCT116_peak_221 | 0.02327259 | -0.0200003599 |\n",
"| Hela_peak_146c | 0.01869036 | 0.0016566129 |\n",
"| Hela_peak_151b | 0.01287070 | 0.0048502650 |\n",
"| Hela_peak_165b | 0.01498533 | 0.0043384852 |\n",
"| Hela_peak_166a | 0.01428953 | 0.0102777979 |\n",
"| Hela_peak_182a | 0.01676060 | 0.0050847575 |\n",
"| Hela_peak_191a | 0.01444036 | 0.0084127239 |\n",
"| Hela_peak_195 | 0.01364276 | 0.0061150608 |\n",
"| Hela_peak_231b | 0.01594876 | 0.0080887840 |\n",
"| HCT116_peak_372a | 0.01902758 | 0.0032786119 |\n",
"| Hela_peak_245 | 0.01417920 | 0.0162453705 |\n",
"| Hela_peak_252b | 0.01891022 | 0.0033845595 |\n",
"| Hela_peak_254b | 0.01427603 | 0.0076013328 |\n",
"| ⋮ | ⋮ | ⋮ |\n",
"| K562_peak_57467b | -7.469237e-04 | 4.059980e-02 |\n",
"| K562_peak_57467c | -1.093510e-03 | 3.425520e-02 |\n",
"| K562_peak_57509b | 3.261322e-04 | 3.581221e-02 |\n",
"| K562_peak_57574b | -2.172015e-03 | 3.105348e-02 |\n",
"| K562_peak_57574c | -2.928692e-03 | 3.626421e-02 |\n",
"| K562_peak_57574d | -2.471893e-03 | 3.775303e-02 |\n",
"| K562_peak_57574g | -1.683027e-03 | 4.232230e-02 |\n",
"| K562_peak_57574h | 5.272538e-05 | 4.176382e-02 |\n",
"| Hela_peak_138208 | 3.269637e-03 | 4.175070e-03 |\n",
"| HCT116_peak_93629 | 1.674785e-02 | 8.764308e-03 |\n",
"| Hela_peak_138278b | 1.038759e-02 | 2.993853e-03 |\n",
"| Hela_peak_138314 | 1.222216e-02 | 7.092110e-04 |\n",
"| Hela_peak_138326c | 1.743609e-02 | 4.639038e-03 |\n",
"| Hela_peak_138400 | 1.100395e-02 | 2.948213e-03 |\n",
"| Hela_peak_138542e | 1.135172e-02 | 1.383667e-02 |\n",
"| Hela_peak_138560c | 1.344246e-02 | 9.839686e-03 |\n",
"| Hela_peak_138588a | 1.114320e-02 | 6.886063e-03 |\n",
"| Hela_peak_138597a | 1.114535e-02 | -8.043704e-04 |\n",
"| Hela_peak_138597b | 1.184878e-02 | 2.092852e-03 |\n",
"| Hela_peak_138604b | 1.162418e-02 | 1.338468e-05 |\n",
"| Hela_peak_138639a | 1.958263e-02 | -1.645798e-02 |\n",
"| Hela_peak_138662a | 7.402616e-03 | 5.148582e-03 |\n",
"| Hela_peak_138687b | 1.632005e-02 | 5.747770e-03 |\n",
"| Hela_peak_138702a | 9.331831e-03 | 6.392104e-03 |\n",
"| Hela_peak_138730b | 1.033829e-02 | 1.086851e-03 |\n",
"| HCT116_peak_94117b | 1.178224e-02 | 8.664913e-03 |\n",
"| Hela_peak_138765c | 1.216091e-02 | 2.633533e-03 |\n",
"| Hela_peak_138772b | 9.304095e-03 | 7.027795e-03 |\n",
"| Hela_peak_138792b | 1.232495e-02 | 6.474760e-03 |\n",
"| Hela_peak_138817c | 9.399972e-03 | 1.240935e-02 |\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"$method\n",
": 'RGCCA'\n",
"\n",
"\n"
],
"text/plain": [
"$factorizations\n",
"$factorizations[[1]]\n",
"$factorizations[[1]][[1]]\n",
" comp1 comp2\n",
"HCT_CL100049490_L01_10 -0.603619302 -0.23299647\n",
"HCT_CL100049490_L01_14 -0.632492584 -0.18841422\n",
"HCT_CL100049490_L01_15 -0.758610238 -0.19184096\n",
"HCT_CL100049490_L01_16 -0.369532528 -0.25537424\n",
"HCT_CL100049490_L01_19 -0.798700859 -0.16130432\n",
"HCT_CL100049490_L01_2 -0.778697261 -0.18424769\n",
"HCT_CL100049490_L01_21 -0.680400157 -0.20631098\n",
"HCT_CL100049490_L01_22 -0.910482370 -0.11923025\n",
"HCT_CL100049490_L01_24 -0.660502514 -0.20125706\n",
"HCT_CL100049490_L01_27 -0.677422847 -0.21397578\n",
"HCT_CL100049490_L01_28 0.043902569 -0.34706599\n",
"HCT_CL100049490_L01_29 0.022682913 -0.37592705\n",
"HCT_CL100049490_L01_3 -0.630363953 -0.21604406\n",
"HCT_CL100049490_L01_34 -0.631909751 -0.18566653\n",
"HCT_CL100049490_L01_35 0.008799254 -0.36012154\n",
"HCT_CL100049490_L01_37 -0.144011648 -0.31343291\n",
"HCT_CL100049490_L01_38 -0.748957989 -0.20644185\n",
"HCT_CL100049490_L01_39 -0.518834437 -0.25258825\n",
"HCT_CL100049490_L01_4 -0.185956750 -0.28376007\n",
"HCT_CL100049490_L01_44 -0.354688160 -0.26443864\n",
"HCT_CL100049490_L01_45 -0.047822974 -0.34554671\n",
"HCT_CL100049490_L01_48 -0.853088112 -0.13941892\n",
"HCT_CL100049490_L01_7 -0.539051277 -0.22594812\n",
"HCT_CL100049490_L02_49 0.677212516 -0.37452671\n",
"HCT_CL100049490_L02_52 -0.253533343 -0.29024933\n",
"HCT_CL100049490_L02_53 0.396343303 -0.35399847\n",
"HCT_CL100049490_L02_54 -0.681967479 -0.21308273\n",
"HCT_CL100049490_L02_55 -0.451779929 -0.24281442\n",
"HCT_CL100049490_L02_59 -0.554110697 -0.26174437\n",
"HCT_CL100049490_L02_61 -0.540449915 -0.24028395\n",
"HCT_CL100049490_L02_69 -0.528365611 -0.25067656\n",
"HCT_CL100049490_L02_70 -0.933989506 -0.11425298\n",
"HCT_CL100049490_L02_75 -0.114795630 -0.29214532\n",
"HCT_CL100049490_L02_78 -0.353538850 -0.27549622\n",
"HCT_CL100049490_L02_79 -0.491618313 -0.25384516\n",
"HCT_CL100049490_L02_83 0.123365683 -0.36527309\n",
"HCT_CL100049490_L02_84 -0.363698216 -0.27907964\n",
"HCT_CL100049490_L02_85 -0.587617574 -0.24180205\n",
"HCT_CL100049490_L02_86 -0.441282037 -0.26996009\n",
"HCT_CL100049490_L02_88 -0.505212912 -0.23463262\n",
"HCT_CL100049490_L02_91 -0.287905154 -0.29445835\n",
"HCT_CL100049490_L02_93 -0.744295495 -0.18670130\n",
"HCT_CL100036542_L01_10 -0.293727055 -0.26439315\n",
"HCT_CL100036542_L01_12 -0.096013345 -0.29976954\n",
"HCT_CL100036542_L01_13 -0.603391426 -0.25074031\n",
"HCT_CL100036542_L01_14 -0.453851433 -0.27068136\n",
"HCT_CL100036542_L01_15 -0.679335325 -0.20893975\n",
"HCT_CL100036542_L01_19 0.625119529 -0.39613794\n",
"HCT_CL100036542_L01_2 -0.655525604 -0.21274655\n",
"HCT_CL100036542_L01_21 -0.763036626 -0.16238498\n",
"HCT_CL100036542_L01_22 -0.602825205 -0.23404071\n",
"HCT_CL100036542_L01_23 -0.046735916 -0.32532242\n",
"HCT_CL100036542_L01_24 -0.569138020 -0.21904110\n",
"HCT_CL100036542_L01_3 -0.251909393 -0.29586972\n",
"HCT_CL100036542_L01_32 -0.838591205 -0.16112146\n",
"HCT_CL100036542_L01_36 -0.420832139 -0.25939086\n",
"HCT_CL100036542_L01_37 -0.388382352 -0.26150250\n",
"HCT_CL100036542_L01_42 -0.327029503 -0.26339438\n",
"HCT_CL100036542_L01_43 -0.820165234 -0.14797903\n",
"HCT_CL100036542_L01_44 0.802107510 -0.39149810\n",
"HCT_CL100036542_L01_45 -0.362793738 -0.30384807\n",
"HCT_CL100036542_L01_47 -0.304463132 -0.31265526\n",
"HCT_CL100036542_L01_48 -0.678657869 -0.17884508\n",
"HCT_CL100036542_L01_5 -0.481758755 -0.25125417\n",
"HCT_CL100036542_L01_7 -0.179458435 -0.31219395\n",
"HCT_CL100036542_L01_9 -0.417621997 -0.24939892\n",
"HCT_CL100036542_L02_51 -0.802718458 -0.16896733\n",
"HCT_CL100036542_L02_52 -0.191642225 -0.29034991\n",
"HCT_CL100036542_L02_53 -0.320693034 -0.24791289\n",
"HCT_CL100036542_L02_54 0.008178763 -0.35971215\n",
"HCT_CL100036542_L02_55 -0.183979901 -0.32476182\n",
"HCT_CL100036542_L02_56 -0.837174626 -0.16714775\n",
"HCT_CL100036542_L02_59 -0.241203283 -0.32457710\n",
"HCT_CL100036542_L02_62 -0.442837240 -0.24784930\n",
"HCT_CL100036542_L02_63 -0.172892304 -0.30687859\n",
"HCT_CL100036542_L02_64 -0.739486347 -0.16386409\n",
"HCT_CL100036542_L02_66 -0.480608246 -0.23421518\n",
"HCT_CL100036542_L02_67 -0.343126914 -0.28862647\n",
"HCT_CL100036542_L02_69 -0.439633925 -0.24935155\n",
"HCT_CL100036542_L02_72 -0.888897583 -0.12343120\n",
"HCT_CL100036542_L02_74 -0.222729951 -0.28392442\n",
"HCT_CL100036542_L02_75 -0.697753709 -0.22665569\n",
"HCT_CL100036542_L02_77 -0.652032194 -0.18821151\n",
"HCT_CL100036542_L02_78 -0.205906026 -0.30543008\n",
"HCT_CL100036542_L02_79 -0.375724597 -0.26124776\n",
"HCT_CL100036542_L02_80 -0.783474272 -0.15823456\n",
"HCT_CL100036542_L02_82 0.489896760 -0.36403455\n",
"HCT_CL100036542_L02_85 0.335598084 -0.33931206\n",
"HCT_CL100036542_L02_88 -0.755970059 -0.15391430\n",
"HCT_CL100036542_L02_92 0.052451880 -0.30495556\n",
"Hela_CL100047420_L01_10 0.570495336 -0.23379289\n",
"Hela_CL100047420_L01_11 0.991050197 -0.32353788\n",
"Hela_CL100047420_L01_17 0.246374189 -0.17970784\n",
"Hela_CL100047420_L01_18 1.099183266 -0.32120228\n",
"Hela_CL100047420_L01_19 0.773169084 -0.26815510\n",
"Hela_CL100047420_L01_2 0.527693917 -0.22581212\n",
"Hela_CL100047420_L01_25 0.578457506 -0.24368084\n",
"Hela_CL100047420_L01_26 0.958291080 -0.31952237\n",
"Hela_CL100047420_L01_29 0.864955680 -0.28978347\n",
"Hela_CL100047420_L01_33 0.491674402 -0.22065616\n",
"Hela_CL100047420_L01_34 1.043710498 -0.30731583\n",
"Hela_CL100047420_L01_35 0.787073779 -0.26647592\n",
"Hela_CL100047420_L01_41 0.166420660 -0.15658721\n",
"Hela_CL100047420_L01_9 0.176183879 -0.17909854\n",
"Hela_CL100047420_L02_49 1.006735214 -0.29821713\n",
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"\n",
"$factorizations[[1]][[2]]\n",
"$factorizations[[1]][[2]][[1]]\n",
" 1 2\n",
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"ENSG00000219481 -1.844049e-02 2.165168e-02\n",
"ENSG00000219545 -1.462419e-02 7.732959e-03\n",
"ENSG00000221829 -1.928452e-02 2.354198e-03\n",
"ENSG00000221909 -8.268698e-03 5.874232e-03\n",
"ENSG00000221926 -1.103215e-02 -1.793720e-02\n",
"ENSG00000221955 -1.778968e-02 -1.860377e-02\n",
"ENSG00000221968 -7.498539e-03 -1.163792e-02\n",
"ENSG00000221978 -1.569803e-02 1.504783e-02\n",
"ENSG00000222019 -1.628067e-02 3.000418e-03\n",
"ENSG00000222047 -1.901910e-02 -1.202968e-02\n",
"ENSG00000223959 -1.421062e-02 -1.586375e-03\n",
"ENSG00000224287 -1.791961e-02 -3.577990e-04\n",
"ENSG00000224531 -9.778901e-03 -6.051746e-03\n",
"ENSG00000224597 -1.381096e-02 9.340513e-04\n",
"ENSG00000224660 -3.988631e-03 -5.352930e-03\n",
"ENSG00000225339 -1.260644e-02 1.851852e-02\n",
"ENSG00000225697 -1.171691e-02 -1.047753e-03\n",
"ENSG00000225733 -1.642805e-02 8.887057e-04\n",
"ENSG00000226479 -1.198515e-02 -4.356984e-03\n",
"ENSG00000226742 -4.585459e-03 -3.610859e-02\n",
"ENSG00000226887 -1.754573e-02 -1.608273e-02\n",
"ENSG00000227036 -1.920149e-02 -2.089178e-02\n",
"ENSG00000227345 -1.576476e-02 5.601510e-03\n",
"ENSG00000227617 -1.584345e-02 -1.698405e-03\n",
"ENSG00000227706 8.416462e-04 3.918556e-02\n",
"ENSG00000228192 -3.072956e-03 2.383063e-02\n",
"ENSG00000228794 -2.028029e-02 1.410823e-03\n",
"ENSG00000229124 1.221817e-02 3.077084e-02\n",
"ENSG00000230257 -7.392550e-04 3.912513e-02\n",
"ENSG00000230583 -1.015996e-02 8.808296e-03\n",
"ENSG00000231607 -1.949865e-02 -7.584989e-03\n",
"ENSG00000231721 -9.006490e-03 1.624838e-02\n",
"ENSG00000232053 4.298393e-04 3.219753e-02\n",
"ENSG00000232445 -1.093904e-02 -1.249249e-02\n",
"ENSG00000232811 -1.532183e-02 1.696586e-02\n",
"ENSG00000232884 2.126733e-03 3.009682e-02\n",
"ENSG00000233137 -1.442873e-02 1.134387e-02\n",
"ENSG00000233276 -3.678808e-03 -3.837316e-02\n",
"ENSG00000233822 -6.423803e-03 3.016234e-02\n",
"ENSG00000234171 -1.205929e-02 -1.664865e-02\n",
"ENSG00000234377 -1.501173e-02 -3.031525e-03\n",
"ENSG00000234722 2.094659e-03 3.549457e-02\n",
"ENSG00000234912 -6.545969e-03 1.222183e-02\n",
"ENSG00000234978 1.520567e-03 3.522752e-02\n",
"ENSG00000235106 -1.036527e-02 1.337779e-02\n",
"ENSG00000235109 -1.232299e-02 1.270292e-02\n",
"ENSG00000235162 -1.869396e-02 -2.411776e-02\n",
"ENSG00000235335 -9.334781e-04 3.796613e-02\n",
"ENSG00000235437 -7.712717e-03 4.479942e-03\n",
"ENSG00000235750 -1.199289e-02 -1.468244e-02\n",
"ENSG00000235823 -1.457993e-02 9.532552e-03\n",
"ENSG00000235884 -1.769887e-02 -2.023976e-02\n",
"ENSG00000236287 -7.341279e-03 1.584152e-02\n",
"ENSG00000236338 -1.373408e-02 5.913711e-03\n",
"ENSG00000236624 -9.059058e-03 1.438549e-02\n",
"ENSG00000237187 -1.457883e-02 -2.101189e-02\n",
"ENSG00000237298 -7.145802e-03 1.307435e-02\n",
"ENSG00000237412 1.812123e-02 -1.567479e-02\n",
"ENSG00000237438 -1.145875e-02 3.696413e-03\n",
"ENSG00000237523 -1.551492e-02 -1.767011e-02\n",
"ENSG00000240024 -1.279952e-02 1.362719e-02\n",
"ENSG00000241058 -7.740810e-03 3.606509e-03\n",
"ENSG00000241945 -1.735471e-02 -4.299124e-03\n",
"ENSG00000242247 -9.583302e-03 1.383134e-02\n",
"ENSG00000242802 -9.513096e-03 1.445937e-02\n",
"ENSG00000243335 -5.948573e-03 3.426609e-02\n",
"ENSG00000243943 -1.032890e-02 1.437671e-02\n",
"ENSG00000244026 -1.616944e-02 -1.631438e-02\n",
"ENSG00000244041 -7.490431e-04 2.827870e-02\n",
"ENSG00000244405 -2.274250e-02 4.429201e-03\n",
"ENSG00000244462 -1.881013e-02 4.792865e-03\n",
"ENSG00000245060 -1.655635e-02 1.320738e-02\n",
"ENSG00000245149 -2.132905e-03 3.111164e-02\n",
"ENSG00000245532 -2.987237e-03 2.869635e-02\n",
"ENSG00000245748 -2.041888e-02 1.106973e-02\n",
"ENSG00000245958 -9.514807e-03 5.823054e-03\n",
"ENSG00000246082 -1.466671e-02 -1.586520e-03\n",
"ENSG00000246089 -1.588143e-02 -3.168348e-03\n",
"ENSG00000246273 -1.204434e-02 -1.090954e-02\n",
"ENSG00000246985 -7.386155e-03 3.427338e-02\n",
"ENSG00000247271 -9.827310e-03 9.725524e-03\n",
"ENSG00000247626 -1.898003e-02 -7.480629e-03\n",
"ENSG00000248008 -1.135702e-02 5.122100e-03\n",
"ENSG00000248049 -1.761190e-02 8.595611e-03\n",
"ENSG00000248596 -2.378241e-03 3.491360e-02\n",
"ENSG00000249115 -1.878252e-02 1.191741e-03\n",
"ENSG00000249307 -9.984658e-04 3.802406e-02\n",
"ENSG00000249550 -1.707138e-02 -7.301826e-03\n",
"ENSG00000249673 -2.023553e-02 -7.071641e-03\n",
"ENSG00000249859 -1.654556e-02 -1.813265e-02\n",
"ENSG00000250501 6.097457e-04 3.792503e-02\n",
"ENSG00000250714 -1.523698e-02 -1.472920e-03\n",
"ENSG00000250722 -1.328510e-02 1.986409e-02\n",
"ENSG00000250920 -1.893538e-02 -2.386206e-02\n",
"ENSG00000251003 -1.972367e-02 -4.118265e-03\n",
"ENSG00000251381 6.725869e-03 3.186699e-02\n",
"ENSG00000253174 -1.181787e-02 -4.457031e-03\n",
"ENSG00000253187 -1.295073e-02 -2.955152e-02\n",
"ENSG00000253200 -1.681572e-02 3.047807e-03\n",
"ENSG00000253250 -5.095593e-06 3.990306e-02\n",
"ENSG00000253293 -1.314393e-02 -2.834972e-02\n",
"ENSG00000253352 -5.193480e-03 1.267441e-02\n",
"ENSG00000253394 6.100041e-04 3.715853e-02\n",
"ENSG00000253719 -1.907259e-02 1.860625e-03\n",
"ENSG00000254087 -8.456290e-03 1.489620e-02\n",
"ENSG00000254685 -9.554322e-03 9.560206e-05\n",
"ENSG00000254829 -1.294274e-02 -2.177519e-02\n",
"ENSG00000255248 -1.929042e-02 -1.663132e-02\n",
"ENSG00000255529 -2.073967e-02 -2.445924e-03\n",
"ENSG00000256525 -1.336235e-02 2.071560e-03\n",
"ENSG00000256546 -1.448745e-02 -2.514043e-03\n",
"ENSG00000257267 -1.113629e-02 1.402618e-02\n",
"ENSG00000257365 -1.605170e-02 -6.138318e-03\n",
"ENSG00000257453 -1.830446e-02 -1.762502e-02\n",
"ENSG00000257621 -1.427497e-02 1.670141e-02\n",
"ENSG00000258377 -1.272832e-02 8.620295e-04\n",
"ENSG00000258525 -8.405299e-03 3.291988e-02\n",
"ENSG00000258727 -1.571701e-02 -5.737610e-03\n",
"ENSG00000258890 -1.607476e-02 6.933311e-04\n",
"ENSG00000259536 -5.690829e-03 5.397715e-03\n",
"ENSG00000259583 -1.605708e-02 -2.732728e-02\n",
"ENSG00000259758 -1.546378e-02 3.166858e-03\n",
"ENSG00000259943 -5.434901e-03 2.108534e-02\n",
"ENSG00000260032 -1.789123e-02 5.350592e-03\n",
"ENSG00000260257 -5.635283e-03 2.711430e-02\n",
"ENSG00000260539 -9.828189e-03 -2.065116e-03\n",
"ENSG00000261183 -1.859346e-02 -2.095506e-02\n",
"ENSG00000261188 -4.308188e-03 5.171337e-03\n",
"ENSG00000261317 -4.203937e-03 1.546574e-02\n",
"ENSG00000261373 -1.632376e-02 -1.205521e-02\n",
"ENSG00000261526 -6.868916e-03 2.705025e-02\n",
"ENSG00000261609 -1.492700e-02 3.812127e-03\n",
"ENSG00000261801 -1.983893e-02 -1.953592e-02\n",
"ENSG00000261824 -1.931633e-02 6.774121e-03\n",
"ENSG00000262001 -1.525999e-02 1.862499e-02\n",
"ENSG00000262160 -9.047138e-03 2.270889e-02\n",
"ENSG00000263244 -9.367279e-03 1.413585e-02\n",
"ENSG00000263711 4.256331e-03 3.511596e-02\n",
"ENSG00000263753 8.143165e-03 2.703507e-02\n",
"ENSG00000264247 -1.648397e-02 -6.417410e-03\n",
"ENSG00000264578 -1.662168e-02 8.216754e-03\n",
"ENSG00000266642 -6.231199e-03 1.278346e-02\n",
"ENSG00000266962 -1.300640e-02 -2.964323e-03\n",
"ENSG00000266976 7.304652e-04 3.887445e-02\n",
"ENSG00000267080 -1.218868e-02 2.316762e-03\n",
"ENSG00000267100 -1.358437e-02 1.075906e-02\n",
"ENSG00000267232 -7.212968e-03 2.747943e-02\n",
"ENSG00000267453 -1.844215e-02 -2.217174e-02\n",
"ENSG00000267575 -1.062934e-02 1.228944e-02\n",
"ENSG00000267577 -1.423952e-02 -1.010161e-02\n",
"ENSG00000267607 2.720833e-03 3.282425e-02\n",
"ENSG00000267680 -1.117645e-03 1.098145e-02\n",
"ENSG00000268739 -1.927349e-02 -2.366765e-02\n",
"ENSG00000269343 -1.248160e-02 2.712699e-03\n",
"ENSG00000269495 -1.814536e-02 -2.466766e-02\n",
"ENSG00000269604 -1.579082e-02 -4.093195e-03\n",
"ENSG00000269609 -1.575880e-02 1.337079e-02\n",
"ENSG00000270228 -1.519317e-02 2.419093e-03\n",
"ENSG00000271122 -1.300519e-02 -5.000969e-03\n",
"ENSG00000272159 -1.775950e-02 -2.311831e-02\n",
"ENSG00000272686 -5.006361e-03 1.757593e-02\n",
"ENSG00000272758 -1.803232e-02 1.050914e-02\n",
"ENSG00000272763 -1.740158e-02 -2.268607e-02\n",
"ENSG00000273015 -5.973246e-03 2.060144e-02\n",
"ENSG00000273192 -1.897507e-02 -4.423689e-04\n",
"ENSG00000273344 -7.288744e-03 2.528588e-02\n",
"\n",
"$factorizations[[1]][[2]][[2]]\n",
" 1 2\n",
"Hela_peak_28b 1.725913e-02 4.075603e-03\n",
"Hela_peak_28c 1.516821e-02 5.945631e-03\n",
"Hela_peak_34a 1.789808e-02 2.850342e-03\n",
"Hela_peak_41 1.492479e-02 9.581905e-03\n",
"Hela_peak_47 1.379148e-02 1.274605e-02\n",
"Hela_peak_53 1.722123e-02 3.840341e-03\n",
"Hela_peak_69a 1.778134e-02 -6.469863e-04\n",
"Hela_peak_73 1.413129e-02 9.005659e-03\n",
"Hela_peak_85c 1.528905e-02 3.801551e-03\n",
"Hela_peak_87 1.423577e-02 8.403412e-03\n",
"Hela_peak_101b 1.734376e-02 7.371218e-03\n",
"Hela_peak_104a 1.603512e-02 4.173731e-03\n",
"Hela_peak_118b 1.876127e-02 4.905365e-03\n",
"Hela_peak_127 1.788265e-02 8.611119e-03\n",
"Hela_peak_128 1.385191e-02 6.712755e-03\n",
"Hela_peak_140b 1.870552e-02 -2.607964e-02\n",
"Hela_peak_141 1.347466e-02 1.069740e-02\n",
"HCT116_peak_221 2.327259e-02 -2.000036e-02\n",
"Hela_peak_146c 1.869036e-02 1.656613e-03\n",
"Hela_peak_151b 1.287070e-02 4.850265e-03\n",
"Hela_peak_165b 1.498533e-02 4.338485e-03\n",
"Hela_peak_166a 1.428953e-02 1.027780e-02\n",
"Hela_peak_182a 1.676060e-02 5.084758e-03\n",
"Hela_peak_191a 1.444036e-02 8.412724e-03\n",
"Hela_peak_195 1.364276e-02 6.115061e-03\n",
"Hela_peak_231b 1.594876e-02 8.088784e-03\n",
"HCT116_peak_372a 1.902758e-02 3.278612e-03\n",
"Hela_peak_245 1.417920e-02 1.624537e-02\n",
"Hela_peak_252b 1.891022e-02 3.384560e-03\n",
"Hela_peak_254b 1.427603e-02 7.601333e-03\n",
"Hela_peak_254c 1.844565e-02 6.003484e-03\n",
"Hela_peak_254d 1.823115e-02 1.569152e-03\n",
"Hela_peak_285b 1.756726e-02 -2.794708e-03\n",
"Hela_peak_309b 1.515878e-02 4.247446e-03\n",
"Hela_peak_310a 1.950351e-02 1.800753e-03\n",
"Hela_peak_315c 1.596918e-02 4.414783e-03\n",
"Hela_peak_340b 1.568518e-02 1.470609e-02\n",
"HCT116_peak_553a 2.216904e-02 -2.061461e-02\n",
"Hela_peak_371d 1.709915e-02 3.046140e-03\n",
"Hela_peak_371e 1.663531e-02 3.781414e-03\n",
"Hela_peak_373b 1.353076e-02 4.138869e-03\n",
"Hela_peak_382a 1.279830e-02 7.677541e-03\n",
"Hela_peak_382b 1.197459e-02 5.286696e-03\n",
"Hela_peak_461 1.281917e-02 6.613897e-03\n",
"Hela_peak_470 1.729842e-02 4.693076e-03\n",
"Hela_peak_536 1.736995e-02 1.640573e-03\n",
"Hela_peak_558b 1.312591e-02 9.959367e-03\n",
"HCT116_peak_760c 1.294640e-02 6.942278e-03\n",
"Hela_peak_603 1.628934e-02 5.832574e-03\n",
"Hela_peak_644a 1.233229e-02 1.487412e-02\n",
"Hela_peak_666a 1.216149e-02 8.290548e-03\n",
"Hela_peak_673a 1.473446e-02 4.701682e-03\n",
"Hela_peak_681b 1.428715e-02 6.294536e-03\n",
"Hela_peak_686a 1.348091e-02 1.309062e-02\n",
"Hela_peak_723a 1.723198e-02 5.424916e-03\n",
"Hela_peak_729 1.334623e-02 8.918142e-03\n",
"Hela_peak_734 1.666353e-02 4.507963e-03\n",
"Hela_peak_757 1.889587e-02 7.034655e-03\n",
"Hela_peak_771b 1.883556e-02 3.853024e-03\n",
"Hela_peak_780b 1.782141e-02 3.483921e-04\n",
"Hela_peak_780c 2.174807e-02 1.967980e-03\n",
"Hela_peak_791a 1.483225e-02 4.986925e-03\n",
"Hela_peak_851a 1.443758e-02 5.217718e-03\n",
"Hela_peak_872a 1.246778e-02 1.335239e-02\n",
"Hela_peak_900c 1.813050e-02 4.378894e-03\n",
"Hela_peak_940a 1.590338e-02 2.305676e-03\n",
"Hela_peak_942 1.978979e-02 3.647704e-03\n",
"Hela_peak_946a 1.823293e-02 5.388882e-04\n",
"Hela_peak_970c 8.219898e-03 -6.454700e-03\n",
"Hela_peak_974b 1.709210e-02 9.071888e-03\n",
"Hela_peak_974d 1.910020e-02 7.516065e-03\n",
"Hela_peak_975a 1.637448e-02 3.466175e-03\n",
"Hela_peak_1023a 1.399167e-02 4.207330e-03\n",
"Hela_peak_1034a 1.869921e-02 5.637991e-03\n",
"HCT116_peak_1344 2.170489e-02 2.541884e-03\n",
"Hela_peak_1046 1.980528e-02 8.778085e-03\n",
"HCT116_peak_1357b 1.323835e-02 1.434217e-02\n",
"HCT116_peak_1367 1.977639e-02 6.351114e-03\n",
"HCT116_peak_1375a 2.419097e-02 -2.442122e-02\n",
"HCT116_peak_1375b 2.145633e-02 -2.041348e-02\n",
"Hela_peak_1053 1.931968e-02 2.681574e-03\n",
"HCT116_peak_1394 2.456145e-02 -2.389477e-02\n",
"HCT116_peak_1395 2.290090e-02 -9.864452e-03\n",
"Hela_peak_1061a 1.356902e-02 -3.702810e-03\n",
"Hela_peak_1063 1.333550e-02 -2.380892e-03\n",
"Hela_peak_1186b 1.477539e-02 6.724393e-03\n",
"Hela_peak_1193b 1.368690e-02 7.376467e-03\n",
"Hela_peak_1201 1.640796e-02 -2.103614e-02\n",
"Hela_peak_1209b 1.529335e-02 6.814806e-03\n",
"Hela_peak_1215b 1.556202e-02 1.148179e-02\n",
"Hela_peak_1225b 1.221018e-02 6.966384e-03\n",
"HCT116_peak_1713 1.752300e-02 1.012296e-02\n",
"Hela_peak_1313a 1.432837e-02 6.351632e-03\n",
"Hela_peak_1348 1.716568e-02 -1.966625e-02\n",
"Hela_peak_1385a 1.636128e-02 8.943406e-03\n",
"Hela_peak_1409a 1.924541e-02 -3.258679e-03\n",
"HCT116_peak_1863b 2.254638e-02 -1.996515e-02\n",
"Hela_peak_1453 1.459092e-02 1.423705e-02\n",
"Hela_peak_1473c 1.549985e-02 3.263907e-03\n",
"Hela_peak_1476b 1.485263e-02 6.252936e-03\n",
"Hela_peak_1476c 1.670092e-02 7.855785e-03\n",
"HCT116_peak_1993 1.814422e-02 9.821132e-03\n",
"Hela_peak_1569b 1.764361e-02 -1.974441e-03\n",
"Hela_peak_1578a 1.450560e-02 4.944883e-03\n",
"Hela_peak_1590a 1.260025e-02 6.497928e-03\n",
"Hela_peak_1611 1.980775e-02 -2.751859e-03\n",
"HCT116_peak_2044 1.616030e-02 4.042983e-03\n",
"Hela_peak_1623a 1.762239e-02 5.407710e-03\n",
"Hela_peak_1623b 1.716878e-02 9.592623e-03\n",
"Hela_peak_1674b 1.261244e-02 9.793514e-03\n",
"Hela_peak_1688b 1.560710e-02 1.067516e-02\n",
"Hela_peak_1688c 1.457034e-02 5.266507e-03\n",
"Hela_peak_1696 1.600689e-02 6.194272e-03\n",
"HCT116_peak_2125a 1.920632e-02 9.169955e-03\n",
"Hela_peak_1729b 1.514973e-02 7.320947e-03\n",
"Hela_peak_1734a 1.317190e-02 1.754118e-02\n",
"Hela_peak_1738a 1.800615e-02 2.889933e-03\n",
"HCT116_peak_2166a 1.624219e-02 1.138747e-02\n",
"Hela_peak_1747a 1.617258e-02 6.581865e-03\n",
"Hela_peak_1753c 1.443006e-02 1.010286e-03\n",
"Hela_peak_1763a 2.072702e-02 1.117015e-03\n",
"Hela_peak_1770 1.279702e-02 5.907531e-03\n",
"Hela_peak_1786a 1.090575e-02 1.495033e-02\n",
"Hela_peak_1803 1.828166e-02 4.453206e-03\n",
"Hela_peak_1811a 1.679027e-02 7.774357e-03\n",
"Hela_peak_1848 1.430916e-02 9.075391e-03\n",
"Hela_peak_1856 1.635434e-02 3.755678e-03\n",
"Hela_peak_1859b 2.224534e-02 -1.434807e-02\n",
"Hela_peak_1861 1.661278e-02 8.195412e-03\n",
"Hela_peak_1885a 1.515596e-02 5.210233e-03\n",
"Hela_peak_1912 1.186758e-02 6.753049e-03\n",
"Hela_peak_1913a 1.837904e-02 1.263794e-02\n",
"Hela_peak_1913b 1.650258e-02 3.907340e-03\n",
"Hela_peak_1920 1.897281e-02 9.474247e-03\n",
"Hela_peak_1941b 1.676244e-02 3.016302e-03\n",
"Hela_peak_1942b 1.101155e-02 1.631766e-02\n",
"Hela_peak_2001a 9.226871e-03 8.997217e-03\n",
"Hela_peak_2041 6.416267e-03 6.475514e-03\n",
"HCT116_peak_2561c 2.305087e-02 -1.676075e-02\n",
"Hela_peak_2099 1.420065e-02 9.888867e-03\n",
"Hela_peak_2147c 9.741250e-03 1.320867e-03\n",
"Hela_peak_2158a 1.535128e-02 4.281062e-03\n",
"Hela_peak_2166 1.391347e-02 4.830186e-03\n",
"Hela_peak_2176a 1.395614e-02 3.096525e-03\n",
"Hela_peak_2191a 1.088414e-02 -1.754140e-03\n",
"Hela_peak_2199 9.541723e-03 4.997205e-03\n",
"Hela_peak_2200 1.319812e-02 -4.815140e-03\n",
"Hela_peak_2217a 1.363587e-02 7.741794e-04\n",
"Hela_peak_2217b 1.264798e-02 8.842584e-04\n",
"Hela_peak_2241c 1.460353e-02 5.975430e-03\n",
"Hela_peak_2312b 1.446593e-02 6.086323e-03\n",
"Hela_peak_2319 2.276038e-02 -1.144818e-02\n",
"Hela_peak_2376b 1.055112e-02 1.007376e-02\n",
"Hela_peak_2385b 1.363023e-02 3.666786e-03\n",
"Hela_peak_2400b 1.041470e-02 1.295966e-02\n",
"Hela_peak_2416b 8.697434e-03 1.346427e-02\n",
"Hela_peak_2416c 8.714035e-03 1.528480e-02\n",
"Hela_peak_2421a 1.143400e-02 1.346455e-02\n",
"Hela_peak_2423 1.600419e-02 9.029181e-03\n",
"Hela_peak_2435 7.706597e-03 2.794574e-03\n",
"Hela_peak_2437b 1.169286e-02 1.697557e-02\n",
"Hela_peak_2446b 7.268287e-03 1.507743e-02\n",
"HCT116_peak_2925 2.282491e-02 -9.506028e-03\n",
"Hela_peak_2461d 1.500867e-02 1.006703e-02\n",
"Hela_peak_2461e 1.311983e-02 1.102881e-02\n",
"Hela_peak_2511a 1.407850e-02 9.576233e-03\n",
"Hela_peak_2535a 1.001695e-02 9.850023e-03\n",
"Hela_peak_2535c 1.064847e-02 5.323464e-03\n",
"Hela_peak_2544b 1.158684e-02 1.001126e-02\n",
"Hela_peak_2546b 1.129516e-02 7.540505e-03\n",
"Hela_peak_2553 1.201100e-02 7.449312e-03\n",
"Hela_peak_2561b 1.208026e-02 3.385242e-03\n",
"Hela_peak_2567 9.409007e-03 -8.543886e-05\n",
"Hela_peak_2600b 9.030360e-03 1.339024e-02\n",
"Hela_peak_2601 1.069898e-02 1.170345e-02\n",
"Hela_peak_2608a 1.229186e-02 1.485991e-02\n",
"Hela_peak_2642b 1.989094e-02 -1.211802e-02\n",
"Hela_peak_2656b 1.030211e-02 1.150427e-02\n",
"HCT116_peak_3135 2.237503e-02 3.033981e-03\n",
"Hela_peak_2683a 1.181882e-02 1.069884e-02\n",
"Hela_peak_2696 1.655194e-02 5.648797e-03\n",
"Hela_peak_2708a 1.197517e-02 1.360675e-02\n",
"Hela_peak_2708b 9.496462e-03 1.138371e-02\n",
"Hela_peak_2719 1.329575e-02 5.394490e-03\n",
"Hela_peak_2750 1.359759e-02 9.993673e-03\n",
"Hela_peak_2752a 1.114106e-02 7.658061e-03\n",
"Hela_peak_2752b 8.265526e-03 1.479429e-02\n",
"Hela_peak_2755a 9.801435e-03 1.120955e-02\n",
"Hela_peak_2794a 1.136894e-02 9.392581e-03\n",
"Hela_peak_2827b 8.306244e-03 1.233599e-02\n",
"Hela_peak_2922b 1.141252e-02 1.588093e-02\n",
"Hela_peak_2933b 1.404854e-02 9.599601e-03\n",
"Hela_peak_2945b 7.569139e-03 1.247546e-02\n",
"Hela_peak_2949a 6.350258e-03 1.308207e-02\n",
"Hela_peak_2953 1.665146e-02 1.380850e-02\n",
"Hela_peak_2965f 1.659980e-02 4.266579e-03\n",
"Hela_peak_3001a 1.503879e-02 -2.614139e-02\n",
"Hela_peak_3002b 1.018279e-02 7.749032e-03\n",
"Hela_peak_3007b 1.003206e-02 6.517058e-03\n",
"Hela_peak_3008 8.993866e-03 1.234335e-02\n",
"Hela_peak_3035a 1.015375e-02 -2.233813e-04\n",
"Hela_peak_3040b 6.481968e-03 1.776242e-02\n",
"Hela_peak_3061 1.269196e-02 1.134551e-02\n",
"Hela_peak_3066a 1.043413e-02 1.434820e-02\n",
"Hela_peak_3070 1.611182e-02 3.157711e-03\n",
"Hela_peak_3075a 1.195372e-02 1.128709e-02\n",
"Hela_peak_3118a 1.270971e-02 1.478894e-02\n",
"Hela_peak_3123 5.395108e-03 9.303785e-03\n",
"Hela_peak_3131c 1.752309e-02 1.293440e-03\n",
"Hela_peak_3134b 1.019495e-02 1.030382e-02\n",
"Hela_peak_3142 9.338039e-03 1.717213e-02\n",
"Hela_peak_3151a 8.878285e-03 8.902545e-03\n",
"Hela_peak_3154a 1.374825e-02 9.822612e-05\n",
"Hela_peak_3158a 1.421948e-02 3.028488e-03\n",
"Hela_peak_3167 9.060100e-03 1.995920e-02\n",
"Hela_peak_3183a 1.037042e-02 1.356402e-02\n",
"Hela_peak_3189a 7.425605e-03 1.038359e-02\n",
"HCT116_peak_3538 2.047069e-02 -5.343958e-03\n",
"Hela_peak_3199 1.217811e-02 4.882382e-03\n",
"Hela_peak_3210 1.117649e-02 1.047506e-02\n",
"Hela_peak_3215a 1.104296e-02 1.156731e-02\n",
"Hela_peak_3215b 1.279617e-02 1.030133e-02\n",
"Hela_peak_3215c 1.170696e-02 1.567192e-02\n",
"Hela_peak_3222b 1.149097e-02 1.178979e-02\n",
"Hela_peak_3222c 1.125662e-02 1.199042e-02\n",
"Hela_peak_3224b 1.014871e-02 1.191786e-02\n",
"Hela_peak_3226b 1.210149e-02 8.529722e-03\n",
"Hela_peak_3242c 4.328817e-03 2.035380e-02\n",
"Hela_peak_3257a 1.116194e-02 5.930652e-03\n",
"Hela_peak_3261d 1.156229e-02 1.296877e-02\n",
"Hela_peak_3267b 1.362203e-02 9.401420e-03\n",
"HCT116_peak_3591 1.633260e-02 1.229545e-02\n",
"Hela_peak_3294 1.335394e-02 1.005762e-02\n",
"Hela_peak_3303b 1.214788e-02 1.083298e-02\n",
"Hela_peak_3339b 1.006369e-02 8.928008e-03\n",
"Hela_peak_3342a 1.298393e-02 1.269850e-02\n",
"Hela_peak_3353c 1.158262e-02 1.368816e-02\n",
"Hela_peak_3496 1.419078e-02 6.617792e-03\n",
"Hela_peak_3498b 9.534668e-03 1.322370e-02\n",
"Hela_peak_3502c 7.203005e-04 2.794840e-02\n",
"Hela_peak_3519a 1.222774e-02 1.541096e-02\n",
"Hela_peak_3566a 6.816633e-03 2.184453e-02\n",
"Hela_peak_3571c 1.018929e-02 1.258927e-02\n",
"Hela_peak_3593b 7.079285e-03 1.361444e-02\n",
"Hela_peak_3594b 1.173213e-02 1.185533e-02\n",
"Hela_peak_3623 -1.017763e-02 3.238040e-02\n",
"Hela_peak_3644 8.554378e-03 1.031127e-02\n",
"Hela_peak_3654a 7.757509e-03 1.419563e-02\n",
"Hela_peak_3715b 8.165341e-03 8.552862e-03\n",
"Hela_peak_3723d 1.094767e-02 8.960172e-03\n",
"Hela_peak_3763a 9.699725e-03 1.548785e-02\n",
"Hela_peak_3783b 6.784360e-03 1.843918e-02\n",
"Hela_peak_3800c 1.504352e-02 2.454120e-03\n",
"Hela_peak_3855 1.342019e-02 -2.473778e-02\n",
"Hela_peak_4151e -8.582561e-04 -5.990964e-03\n",
"Hela_peak_4158c 6.034017e-03 -1.062198e-02\n",
"Hela_peak_4175 2.838265e-03 -2.176857e-02\n",
"Hela_peak_4224b 4.418057e-03 -1.769378e-02\n",
"Hela_peak_4368a 3.986165e-03 2.208174e-02\n",
"Hela_peak_4373b 6.712861e-03 1.297415e-02\n",
"Hela_peak_4465b 1.198930e-02 -2.549605e-02\n",
"Hela_peak_4556a 2.072257e-03 -1.212861e-02\n",
"Hela_peak_4621 1.045168e-03 -1.853888e-03\n",
"Hela_peak_4629 7.839718e-03 -2.077495e-03\n",
"Hela_peak_4747 3.972894e-03 -2.634051e-02\n",
"Hela_peak_4775a 9.865949e-03 7.589209e-04\n",
"Hela_peak_4816b 4.202358e-03 7.935785e-04\n",
"Hela_peak_4860a 3.932220e-03 6.366031e-03\n",
"Hela_peak_4879 -2.990783e-03 -5.125560e-03\n",
"Hela_peak_5020c 2.613903e-03 -2.954189e-03\n",
"Hela_peak_5022b 3.154739e-03 6.802355e-03\n",
"Hela_peak_5085c 6.446787e-04 -4.244748e-03\n",
"Hela_peak_5138e -1.421978e-03 -5.106537e-03\n",
"Hela_peak_5246 2.245189e-02 -2.297207e-02\n",
"HCT116_peak_4363b 2.462459e-02 -2.146319e-02\n",
"HCT116_peak_4377 7.902891e-03 -3.395149e-03\n",
"Hela_peak_5380 5.545378e-03 1.746803e-03\n",
"Hela_peak_5400a -6.595948e-03 -1.563335e-03\n",
"Hela_peak_5724d 1.251941e-03 6.368092e-03\n",
"Hela_peak_5935h 3.607741e-03 -5.102441e-03\n",
"Hela_peak_6250a 5.535995e-03 -5.964468e-03\n",
"Hela_peak_6317c -5.082927e-04 -4.663662e-03\n",
"Hela_peak_6392b 5.688562e-03 4.642605e-03\n",
"Hela_peak_6410b 1.109040e-03 -9.401577e-03\n",
"Hela_peak_6478c -2.869390e-03 -8.091558e-03\n",
"Hela_peak_6483c 1.694619e-02 -2.546151e-02\n",
"Hela_peak_6580a 6.884635e-03 3.147046e-03\n",
"Hela_peak_6607b 6.174720e-03 -1.528640e-03\n",
"Hela_peak_6746b 3.267772e-03 -3.618535e-03\n",
"Hela_peak_6767c 3.966857e-04 -8.131031e-03\n",
"K562_peak_2612b 1.886999e-02 4.804963e-03\n",
"HCT116_peak_4708b 1.878032e-02 1.097651e-02\n",
"Hela_peak_6855b -1.629202e-02 9.531373e-04\n",
"Hela_peak_6891a 5.926623e-03 -6.746993e-03\n",
"Hela_peak_7081 1.594195e-02 8.367760e-03\n",
"Hela_peak_7104d 8.086899e-03 -1.471922e-03\n",
"Hela_peak_7111 1.080702e-02 -5.193244e-04\n",
"Hela_peak_7143b 7.974425e-03 -5.484653e-03\n",
"Hela_peak_7253c 1.179442e-02 8.888680e-04\n",
"Hela_peak_7258b 8.271715e-03 1.152604e-03\n",
"Hela_peak_7303a 1.250271e-02 -2.783519e-02\n",
"Hela_peak_7311b 5.258472e-03 -5.816158e-03\n",
"Hela_peak_7402b 1.261403e-02 -9.907929e-04\n",
"Hela_peak_7409b 1.173245e-02 -1.043936e-02\n",
"Hela_peak_7662 7.269935e-03 7.140231e-04\n",
"Hela_peak_7675a 1.193848e-02 1.265007e-02\n",
"Hela_peak_7675b 5.944863e-03 -1.237163e-03\n",
"Hela_peak_7737b -1.645169e-04 -9.132159e-03\n",
"Hela_peak_8026c 4.929046e-03 1.861413e-02\n",
"Hela_peak_8045b 6.951297e-03 2.284237e-02\n",
"Hela_peak_8065b 7.114540e-03 1.062344e-02\n",
"Hela_peak_8074a 4.754885e-03 3.528548e-03\n",
"Hela_peak_8085a 9.138903e-03 1.281576e-02\n",
"Hela_peak_8092d 5.842574e-03 1.444833e-02\n",
"Hela_peak_8094d 5.295520e-03 1.215546e-02\n",
"Hela_peak_8100a 6.103481e-03 2.096062e-02\n",
"Hela_peak_8101a 5.174149e-03 1.737020e-02\n",
"Hela_peak_8144 4.537995e-03 1.233443e-02\n",
"Hela_peak_8163b 5.796133e-03 7.551235e-03\n",
"Hela_peak_8171a 9.309659e-03 1.732674e-02\n",
"Hela_peak_8175 6.617544e-03 1.454277e-02\n",
"Hela_peak_8275b 2.044313e-03 1.093797e-02\n",
"Hela_peak_8283a 4.863443e-03 1.087225e-02\n",
"Hela_peak_8343c 5.115648e-03 4.751621e-03\n",
"HCT116_peak_5160b 2.142686e-02 -2.034015e-02\n",
"Hela_peak_8406b 5.522391e-03 3.012851e-02\n",
"Hela_peak_8432a 7.309141e-03 1.543531e-02\n",
"Hela_peak_8434 6.467362e-03 2.392869e-02\n",
"Hela_peak_8459d 9.776065e-03 6.998815e-03\n",
"Hela_peak_8462b 7.886439e-03 8.709086e-03\n",
"Hela_peak_8518f 3.671461e-03 1.111573e-02\n",
"Hela_peak_8520a 7.390239e-03 1.058374e-02\n",
"Hela_peak_8549b 4.492729e-03 1.639191e-02\n",
"Hela_peak_8561a 1.162831e-02 7.335419e-03\n",
"Hela_peak_8585b 3.592864e-03 -3.352356e-03\n",
"Hela_peak_8608b 7.302744e-03 7.461330e-03\n",
"Hela_peak_8613b 7.340851e-03 5.054316e-03\n",
"Hela_peak_8618a 8.862595e-03 1.566856e-02\n",
"Hela_peak_8660b 7.494703e-03 8.421035e-03\n",
"Hela_peak_8678e 5.794629e-03 7.633228e-03\n",
"Hela_peak_8797a 8.821183e-03 1.498049e-02\n",
"Hela_peak_8832b 5.359411e-03 9.210568e-03\n",
"Hela_peak_8832c 3.366441e-03 1.755323e-02\n",
"Hela_peak_8883a 6.082193e-03 9.181113e-03\n",
"Hela_peak_8889 4.517108e-03 1.889169e-02\n",
"Hela_peak_8906b 4.542533e-03 1.237794e-02\n",
"Hela_peak_8926b -2.972159e-03 2.295807e-02\n",
"Hela_peak_8926c -2.556955e-03 2.659987e-02\n",
"Hela_peak_8960b 5.742886e-03 1.091798e-02\n",
"Hela_peak_9019b 5.810155e-03 1.139877e-02\n",
"Hela_peak_9068 5.140069e-03 9.840459e-03\n",
"HCT116_peak_5450 1.310696e-02 -1.857586e-02\n",
"Hela_peak_9116 1.508709e-02 4.362510e-03\n",
"Hela_peak_9129 -7.415577e-03 -3.538181e-04\n",
"Hela_peak_9150a 2.434212e-03 -2.852773e-02\n",
"K562_peak_3207f -2.470583e-03 3.766747e-02\n",
"K562_peak_3215 9.213412e-03 1.774736e-02\n",
"K562_peak_3219c 1.055960e-02 1.545770e-02\n",
"Hela_peak_9175b 1.535483e-02 -8.821400e-03\n",
"HCT116_peak_5538 1.140460e-02 4.530489e-03\n",
"HCT116_peak_5539 1.348789e-02 -1.735227e-02\n",
"Hela_peak_9204b 1.262094e-02 -6.586576e-03\n",
"Hela_peak_9206a 1.124377e-02 3.830634e-03\n",
"Hela_peak_9206b 5.920155e-03 4.445095e-03\n",
"Hela_peak_9207b 1.266564e-02 1.803743e-03\n",
"Hela_peak_9224a 1.002426e-02 8.379733e-04\n",
"Hela_peak_9225 1.073416e-02 2.938543e-03\n",
"Hela_peak_9254b 1.551481e-02 3.524003e-03\n",
"Hela_peak_9282a 7.383935e-03 3.541920e-03\n",
"Hela_peak_9316 2.070748e-02 -2.005399e-02\n",
"Hela_peak_9321a 1.721524e-02 -5.717410e-03\n",
"Hela_peak_9335b 8.549920e-03 8.172996e-03\n",
"Hela_peak_9350b 1.380858e-02 2.392221e-03\n",
"Hela_peak_9351a 5.714073e-03 -3.011484e-03\n",
"Hela_peak_9351c 8.113712e-03 2.263704e-03\n",
"Hela_peak_9357 1.870387e-02 -1.269991e-02\n",
"Hela_peak_9384 7.061408e-03 4.107631e-03\n",
"Hela_peak_9396a 8.110789e-03 6.408322e-03\n",
"Hela_peak_9406 5.726429e-03 8.609556e-03\n",
"Hela_peak_9417a 1.044462e-02 -2.232251e-03\n",
"Hela_peak_9426d 2.483198e-03 1.540990e-03\n",
"Hela_peak_9433b 2.293103e-03 -6.416684e-03\n",
"Hela_peak_9441 1.087114e-02 2.967618e-03\n",
"Hela_peak_9485a 6.107611e-03 -3.609900e-03\n",
"Hela_peak_9485b 1.003038e-02 1.752113e-04\n",
"Hela_peak_9494 1.029060e-02 1.363665e-03\n",
"Hela_peak_9518a 1.083510e-02 -1.601690e-02\n",
"Hela_peak_9518b 1.054431e-02 -7.692635e-03\n",
"Hela_peak_9533b 1.209459e-02 1.546543e-04\n",
"Hela_peak_9544a 1.113809e-02 1.199062e-02\n",
"Hela_peak_9584 1.296613e-02 -1.011147e-02\n",
"Hela_peak_9699 9.474030e-03 5.953436e-03\n",
"Hela_peak_9723b 8.952072e-03 5.437873e-03\n",
"Hela_peak_9728c 1.014715e-02 1.123356e-03\n",
"Hela_peak_9730a 7.053967e-03 -2.164818e-03\n",
"Hela_peak_9747c 7.544051e-03 -3.647597e-03\n",
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"Hela_peak_9845c 8.769844e-03 1.308982e-03\n",
"HCT116_peak_5991c 1.298060e-02 5.338781e-04\n",
"Hela_peak_9851a 1.506046e-02 1.111952e-02\n",
"Hela_peak_9863c 9.666356e-03 1.913674e-03\n",
"Hela_peak_9868a 1.032643e-02 7.524490e-03\n",
"Hela_peak_9871c 1.150940e-02 5.306866e-03\n",
"Hela_peak_9891a 9.376823e-03 9.295202e-03\n",
"Hela_peak_9900c -9.680329e-03 -5.629062e-03\n",
"Hela_peak_9904c 1.143473e-02 6.489545e-03\n",
"Hela_peak_9908b 7.720357e-03 8.980310e-03\n",
"Hela_peak_9912b 1.992374e-03 -1.030797e-03\n",
"Hela_peak_9914a 9.367008e-03 3.455550e-03\n",
"Hela_peak_9914b 1.203328e-02 8.490607e-03\n",
"Hela_peak_9914c 7.693797e-03 6.151086e-03\n",
"Hela_peak_9926b 9.954926e-03 5.357304e-03\n",
"Hela_peak_9937 1.138926e-02 5.129638e-03\n",
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"Hela_peak_9982c 5.252595e-03 1.836388e-03\n",
"Hela_peak_10005b 7.750677e-03 -5.498121e-04\n",
"Hela_peak_10015a 8.056278e-03 2.788154e-03\n",
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"Hela_peak_10063e 6.388321e-03 6.142816e-03\n",
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"Hela_peak_10120 8.435955e-03 -3.692929e-04\n",
"Hela_peak_10125b 8.250327e-03 1.518983e-03\n",
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"Hela_peak_10487 8.931908e-03 1.291342e-02\n",
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"Hela_peak_11529b 8.170486e-03 1.499662e-02\n",
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"Hela_peak_20093a 1.301550e-02 -2.464911e-04\n",
"Hela_peak_20121 1.229337e-02 -6.965258e-03\n",
"Hela_peak_20142b 8.696452e-03 -9.080310e-03\n",
"HCT116_peak_11979a 2.450070e-02 -6.553601e-03\n",
"Hela_peak_20174c 5.431161e-03 -9.705527e-03\n",
"Hela_peak_20221b 1.857563e-02 -2.161136e-02\n",
"Hela_peak_20280a 1.833755e-02 -1.751051e-02\n",
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"Hela_peak_20297a 1.966470e-02 -1.693572e-02\n",
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"Hela_peak_20307b 1.839943e-02 -2.353425e-02\n",
"Hela_peak_20334b 1.980212e-02 -1.180511e-02\n",
"Hela_peak_20350b 1.718233e-02 -1.133917e-02\n",
"Hela_peak_20351b 1.729137e-02 -1.367098e-02\n",
"Hela_peak_20354a 1.594605e-02 -1.731166e-02\n",
"Hela_peak_20354b 1.897490e-02 -1.449399e-02\n",
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"Hela_peak_20372b 1.714189e-02 -1.422147e-02\n",
"Hela_peak_20373a 1.787004e-02 -1.527803e-02\n",
"Hela_peak_20373b 1.608217e-02 -1.559534e-02\n",
"HCT116_peak_12272a 2.117423e-02 -2.113215e-02\n",
"Hela_peak_20406a 1.964338e-02 -1.271467e-02\n",
"Hela_peak_20408 1.845761e-02 -1.367108e-02\n",
"Hela_peak_20410b 1.806700e-02 -1.431497e-02\n",
"HCT116_peak_12337 1.786809e-02 -2.029533e-02\n",
"Hela_peak_20421a 1.872791e-02 -1.024904e-02\n",
"Hela_peak_20423 1.895415e-02 -1.504341e-02\n",
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"Hela_peak_20428c 1.425967e-02 -1.225376e-02\n",
"Hela_peak_20450a 1.808054e-02 -1.461968e-02\n",
"HCT116_peak_12425a 2.229357e-02 -2.240056e-02\n",
"Hela_peak_20462 2.180016e-02 -1.028567e-02\n",
"Hela_peak_20478b 2.047267e-02 -1.417500e-02\n",
"Hela_peak_20490b 1.893702e-02 -1.110274e-02\n",
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"Hela_peak_20503a 1.872559e-02 -1.031930e-02\n",
"Hela_peak_20504b 1.889901e-02 -1.904060e-02\n",
"HCT116_peak_12532 1.949792e-02 -9.740921e-03\n",
"Hela_peak_20511b 1.469291e-02 -2.009177e-02\n",
"Hela_peak_20516 1.695669e-02 -1.669530e-02\n",
"Hela_peak_20529a 2.097444e-02 -1.261065e-02\n",
"Hela_peak_20530 1.639874e-02 -1.147929e-02\n",
"Hela_peak_20537a 2.111201e-02 -1.401309e-02\n",
"HCT116_peak_12589c 2.099417e-02 -1.739273e-02\n",
"Hela_peak_20539b 2.117765e-02 -1.613198e-02\n",
"Hela_peak_20544a 1.701792e-02 -1.743201e-02\n",
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"Hela_peak_20545 1.679134e-02 -1.485989e-02\n",
"Hela_peak_20553a 1.717401e-02 -1.184509e-02\n",
"Hela_peak_20553f 1.740841e-02 -1.538759e-02\n",
"HCT116_peak_12649c 2.082373e-02 -1.412668e-02\n",
"Hela_peak_20558b 1.762605e-02 -1.363951e-02\n",
"Hela_peak_20561 1.786664e-02 -1.210023e-02\n",
"Hela_peak_20578 1.914048e-02 -9.018804e-03\n",
"Hela_peak_20581a 1.966659e-02 -1.541811e-02\n",
"Hela_peak_20630b 2.032542e-02 -1.144368e-02\n",
"Hela_peak_20630c 1.879442e-02 -9.190083e-03\n",
"Hela_peak_20631b 1.722994e-02 -1.417503e-02\n",
"HCT116_peak_12754b 2.553371e-02 -2.157022e-02\n",
"HCT116_peak_12842a 2.323744e-02 -7.017913e-03\n",
"HCT116_peak_12870a 1.781176e-02 -1.376151e-02\n",
"Hela_peak_20719b 2.084218e-02 -1.693461e-02\n",
"Hela_peak_20722a 1.775874e-02 -8.647548e-03\n",
"HCT116_peak_12899a 2.314659e-02 -2.435249e-02\n",
"Hela_peak_20740a 2.163426e-02 -1.546150e-02\n",
"Hela_peak_20740b 2.082906e-02 -1.636245e-02\n",
"Hela_peak_20745 2.017884e-02 -1.847441e-02\n",
"Hela_peak_20750b 1.864223e-02 -2.596097e-02\n",
"HCT116_peak_12912 2.330675e-02 -1.854863e-02\n",
"Hela_peak_20758a 2.013120e-02 -2.033564e-02\n",
"Hela_peak_20763b 2.019687e-02 -1.482051e-02\n",
"HCT116_peak_12977 2.380762e-02 -2.248311e-02\n",
"Hela_peak_20896a 2.029708e-02 -1.263629e-02\n",
"Hela_peak_20896h 2.063369e-02 -9.797461e-03\n",
"Hela_peak_20899a 2.212774e-02 -9.621239e-03\n",
"Hela_peak_20904b 1.978514e-02 -1.214613e-02\n",
"HCT116_peak_13085a 2.341157e-02 -2.397221e-02\n",
"HCT116_peak_13085b 2.347893e-02 -2.290161e-02\n",
"Hela_peak_20921a 2.170167e-02 -2.368448e-02\n",
"HCT116_peak_13093b 2.424525e-02 -2.284695e-02\n",
"Hela_peak_20936a 2.275027e-02 -1.118291e-02\n",
"Hela_peak_20942 2.138649e-02 -2.330208e-02\n",
"Hela_peak_20943b 1.916412e-02 -1.452088e-02\n",
"HCT116_peak_13158a 2.501727e-02 -1.597110e-02\n",
"Hela_peak_20974a 1.653439e-02 -1.053699e-02\n",
"Hela_peak_20987a 2.015768e-02 -1.533704e-02\n",
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"K562_peak_7436c 2.140494e-02 -9.680340e-03\n",
"Hela_peak_21006 2.120104e-02 -1.215246e-02\n",
"HCT116_peak_13262 1.965504e-02 -2.096941e-02\n",
"Hela_peak_21020c 2.127794e-02 -9.844234e-03\n",
"HCT116_peak_13282 2.081264e-02 -2.199598e-02\n",
"HCT116_peak_13284 2.299614e-02 -2.147012e-02\n",
"Hela_peak_21046 1.758187e-02 -2.505319e-02\n",
"HCT116_peak_13346 2.140559e-02 -1.477659e-02\n",
"Hela_peak_21073 2.246516e-02 -2.454572e-02\n",
"HCT116_peak_13405 2.204289e-02 -1.875345e-02\n",
"Hela_peak_21102b 2.472236e-02 -1.729716e-02\n",
"Hela_peak_21111c 2.183196e-02 -6.000634e-03\n",
"HCT116_peak_13500 2.107110e-02 -2.064059e-02\n",
"Hela_peak_21124b 2.104524e-02 -1.512829e-02\n",
"HCT116_peak_13534c 2.210685e-02 -2.266242e-02\n",
"Hela_peak_21182b 2.193734e-02 -1.124832e-02\n",
"Hela_peak_21225b 2.464718e-02 -1.372051e-02\n",
"HCT116_peak_13703c 2.444924e-02 -2.195444e-02\n",
"Hela_peak_21239b 2.351202e-02 -1.607229e-02\n",
"Hela_peak_21263c 2.276523e-02 -9.123876e-03\n",
"Hela_peak_21291b 2.195926e-02 -1.371729e-02\n",
"Hela_peak_21294 2.142061e-02 -1.032854e-02\n",
"Hela_peak_21312c 2.225985e-02 -1.268157e-02\n",
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"Hela_peak_21333b 2.403408e-02 -1.351880e-02\n",
"Hela_peak_21335a 2.186153e-02 -9.837673e-03\n",
"Hela_peak_21338 1.982108e-02 -3.676451e-03\n",
"Hela_peak_21340 1.844823e-02 -8.436924e-03\n",
"Hela_peak_21344a 2.054647e-02 -1.156962e-02\n",
"Hela_peak_21344b 1.938732e-02 -1.245917e-02\n",
"Hela_peak_21348 1.869347e-02 -2.649217e-02\n",
"HCT116_peak_13926b 2.141270e-02 -2.080158e-02\n",
"HCT116_peak_13944a 2.201740e-02 -2.222220e-02\n",
"Hela_peak_21372a 2.191382e-02 -1.240782e-02\n",
"Hela_peak_21374c 2.094890e-02 -1.288777e-02\n",
"Hela_peak_21377 2.124431e-02 -9.591456e-03\n",
"Hela_peak_21390 1.904557e-02 -1.009608e-02\n",
"Hela_peak_21401a 2.376228e-02 -1.716774e-02\n",
"Hela_peak_21401b 2.464505e-02 -1.414546e-02\n",
"HCT116_peak_14039 2.287609e-02 -1.596390e-02\n",
"HCT116_peak_14159 2.518120e-02 -2.277823e-02\n",
"Hela_peak_21483b 2.096555e-02 -1.071449e-02\n",
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"Hela_peak_21496a 1.769356e-02 -2.678137e-02\n",
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"Hela_peak_21513b 2.102034e-02 -1.300295e-02\n",
"Hela_peak_21513c 2.307786e-02 -1.270269e-02\n",
"Hela_peak_21533 1.979554e-02 -4.741261e-03\n",
"Hela_peak_21542a 1.980487e-02 -1.226694e-02\n",
"Hela_peak_21542b 1.512653e-02 -1.076867e-02\n",
"Hela_peak_21585b 2.103970e-02 -2.227424e-02\n",
"HCT116_peak_14484b 2.483878e-02 -9.795257e-03\n",
"Hela_peak_21605 2.180175e-02 -1.014847e-02\n",
"Hela_peak_21619 2.309588e-02 -1.145721e-02\n",
"Hela_peak_21624a 2.112892e-02 -1.331695e-02\n",
"Hela_peak_21643f 1.911612e-02 -2.388592e-03\n",
"Hela_peak_21665b 2.136017e-02 -2.503287e-02\n",
"Hela_peak_21668a 2.134524e-02 -1.224275e-02\n",
"HCT116_peak_14731b 2.256292e-02 -1.121524e-02\n",
"Hela_peak_21711a 2.140985e-02 -2.470483e-02\n",
"HCT116_peak_14881 2.245650e-02 -2.058534e-02\n",
"HCT116_peak_14913a 2.271811e-02 -1.143237e-02\n",
"HCT116_peak_14920a 2.148759e-02 -1.789640e-02\n",
"Hela_peak_21939b 1.569054e-02 8.657259e-03\n",
"Hela_peak_21941b 1.246048e-02 1.046895e-02\n",
"HCT116_peak_15137 2.165323e-02 -6.663517e-03\n",
"Hela_peak_21943b 1.440468e-02 7.847114e-03\n",
"Hela_peak_21945b 1.126583e-02 1.331498e-02\n",
"Hela_peak_21946e 1.394101e-02 3.418388e-03\n",
"Hela_peak_21956a 1.583580e-02 3.695404e-03\n",
"HCT116_peak_15165b 1.537727e-02 6.815464e-03\n",
"Hela_peak_21973c 1.620211e-02 7.242132e-03\n",
"Hela_peak_22004 1.385935e-02 1.232789e-02\n",
"Hela_peak_22038 1.851882e-02 7.967748e-03\n",
"Hela_peak_22119b 1.489111e-02 -1.367473e-04\n",
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"Hela_peak_22171d 1.066190e-02 3.991892e-03\n",
"Hela_peak_22243b 4.041709e-03 -5.379108e-04\n",
"Hela_peak_22254b 5.388661e-03 1.333019e-02\n",
"Hela_peak_22258a 1.196436e-02 1.088026e-02\n",
"Hela_peak_22368b 8.263058e-03 -4.240730e-03\n",
"Hela_peak_22394 1.300004e-02 4.939820e-03\n",
"Hela_peak_22430b 1.254667e-02 1.283206e-03\n",
"Hela_peak_22610b 8.783244e-03 -9.236936e-03\n",
"Hela_peak_22694b 1.011064e-02 2.198270e-03\n",
"Hela_peak_22701b 7.980722e-03 -5.549785e-03\n",
"Hela_peak_22712b 3.694314e-03 2.789072e-03\n",
"Hela_peak_22776e 1.007902e-02 -1.400605e-02\n",
"Hela_peak_22837c 1.131417e-02 7.020387e-04\n",
"HCT116_peak_15767 1.088443e-02 -6.110723e-04\n",
"Hela_peak_22854b 1.211450e-02 1.839527e-03\n",
"Hela_peak_22996b 9.722183e-03 -2.410001e-02\n",
"Hela_peak_23031a 1.744617e-03 8.443965e-03\n",
"HCT116_peak_15915b 2.585003e-02 -2.281152e-02\n",
"Hela_peak_23131 7.200023e-03 -2.962536e-02\n",
"Hela_peak_23158a 4.889249e-03 2.997373e-03\n",
"Hela_peak_23241a 1.908777e-02 6.302743e-03\n",
"Hela_peak_23364 2.105440e-02 3.433980e-03\n",
"Hela_peak_23397 1.555583e-02 9.885187e-03\n",
"Hela_peak_23405a 1.657721e-02 4.151903e-03\n",
"Hela_peak_23411b 1.629873e-02 6.766461e-03\n",
"Hela_peak_23422 1.774444e-02 4.410764e-03\n",
"Hela_peak_23449 1.326309e-02 7.929309e-03\n",
"Hela_peak_23563a 1.489491e-02 4.842016e-03\n",
"Hela_peak_23591 1.432678e-02 1.266265e-03\n",
"HCT116_peak_16270a 2.222989e-02 -2.421277e-02\n",
"Hela_peak_23768c 1.136133e-02 4.371686e-03\n",
"Hela_peak_24020a 1.248064e-02 3.646695e-03\n",
"Hela_peak_24023a 8.180137e-03 9.198136e-03\n",
"HCT116_peak_16451b 2.142295e-02 -2.038836e-02\n",
"Hela_peak_24121b 1.103415e-02 2.171294e-03\n",
"Hela_peak_24232 1.748145e-02 -6.627677e-03\n",
"HCT116_peak_16572 2.155641e-02 -1.382476e-02\n",
"Hela_peak_24508c 1.161879e-02 -2.629330e-02\n",
"Hela_peak_24579a 9.555456e-03 5.279616e-03\n",
"Hela_peak_24590a 1.333585e-02 8.268866e-03\n",
"Hela_peak_24600 6.832716e-03 6.257805e-03\n",
"Hela_peak_24701b 1.131591e-02 4.881109e-04\n",
"Hela_peak_24718b 1.142156e-02 1.638210e-02\n",
"Hela_peak_24736a 9.314704e-03 3.682611e-03\n",
"Hela_peak_24759c 1.222585e-02 4.227369e-03\n",
"Hela_peak_24772 4.647100e-03 1.880571e-02\n",
"Hela_peak_24781c 7.949084e-03 8.178793e-03\n",
"Hela_peak_24967b 1.275495e-02 5.740336e-03\n",
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"Hela_peak_24984 1.392396e-02 -2.416766e-03\n",
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"Hela_peak_25080a 8.435379e-03 9.421152e-03\n",
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"Hela_peak_25317d 6.900391e-03 8.441518e-03\n",
"Hela_peak_25326c 5.231091e-03 1.336241e-02\n",
"Hela_peak_25342b 8.100145e-03 1.128870e-02\n",
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"Hela_peak_25422c 7.090475e-03 1.926669e-02\n",
"Hela_peak_25424b 1.063581e-02 1.939304e-02\n",
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"Hela_peak_25430b 1.374349e-02 1.660688e-02\n",
"Hela_peak_25437 4.014637e-03 2.935217e-02\n",
"Hela_peak_25441b 5.443452e-03 1.001731e-02\n",
"Hela_peak_25441c 6.866218e-03 1.510029e-02\n",
"Hela_peak_25444 1.237607e-02 1.249919e-02\n",
"Hela_peak_25447 1.041302e-02 1.856308e-02\n",
"HCT116_peak_17199 1.389004e-02 1.397077e-02\n",
"Hela_peak_25451c 1.300981e-02 1.566171e-02\n",
"Hela_peak_25452b 1.299378e-02 1.692761e-02\n",
"Hela_peak_25454a 1.691914e-02 7.779184e-03\n",
"Hela_peak_25454b 1.411165e-02 7.031740e-03\n",
"HCT116_peak_17206d 1.270228e-02 1.779795e-02\n",
"Hela_peak_25458 1.271221e-02 1.781807e-02\n",
"Hela_peak_25497b 1.146669e-02 9.521270e-03\n",
"Hela_peak_25498 1.603990e-02 7.127217e-03\n",
"Hela_peak_25508 1.585503e-02 2.792898e-03\n",
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"Hela_peak_25517 1.507677e-02 4.441315e-03\n",
"Hela_peak_25534b 1.646053e-02 1.034135e-02\n",
"Hela_peak_25536b 1.577374e-02 5.574109e-03\n",
"Hela_peak_25542c 1.413928e-02 1.405536e-02\n",
"Hela_peak_25547b 1.427439e-02 5.514093e-03\n",
"Hela_peak_25549c 1.613666e-02 1.143062e-02\n",
"Hela_peak_25552b 1.177341e-02 1.267850e-03\n",
"Hela_peak_25555c 1.910306e-02 3.279561e-03\n",
"Hela_peak_25595b 1.213380e-02 1.798057e-03\n",
"Hela_peak_25597a 1.499784e-02 5.337659e-03\n",
"HCT116_peak_17392e 2.115901e-02 2.555963e-03\n",
"Hela_peak_25610b 1.483201e-02 1.391148e-02\n",
"Hela_peak_25621b 1.386302e-02 1.175469e-02\n",
"Hela_peak_25622 1.236177e-02 1.183814e-02\n",
"Hela_peak_25636 1.885367e-02 1.819601e-03\n",
"Hela_peak_25643c 1.806377e-02 1.368763e-02\n",
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"Hela_peak_50943b 8.761202e-03 8.813668e-03\n",
"Hela_peak_50989a 9.354344e-03 6.112336e-03\n",
"Hela_peak_51027a 1.109683e-02 1.551476e-02\n",
"Hela_peak_51053a 7.571665e-03 8.894546e-03\n",
"Hela_peak_51208a 1.346543e-02 1.690521e-02\n",
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"Hela_peak_51213 1.663867e-02 1.599269e-02\n",
"Hela_peak_51216b 7.457641e-03 1.896998e-02\n",
"HCT116_peak_34758a 2.175852e-02 -2.272638e-02\n",
"Hela_peak_51240a 1.159234e-02 1.530535e-02\n",
"Hela_peak_51246 1.869293e-02 -2.121365e-02\n",
"Hela_peak_51254a 1.379773e-02 2.191280e-02\n",
"Hela_peak_51277b 1.938399e-02 -2.286778e-02\n",
"HCT116_peak_34862a 2.253453e-02 -1.567656e-02\n",
"Hela_peak_51304 1.905948e-02 6.839826e-03\n",
"Hela_peak_51332a 1.176433e-02 1.673840e-02\n",
"Hela_peak_51387a 1.297030e-02 1.784738e-02\n",
"Hela_peak_51395a 1.405696e-02 1.656534e-02\n",
"Hela_peak_51396 1.839043e-02 -6.953709e-03\n",
"Hela_peak_51403a 1.665467e-02 1.267802e-02\n",
"Hela_peak_51416a 1.414753e-02 1.084844e-02\n",
"Hela_peak_51431a 1.382450e-02 1.783695e-02\n",
"Hela_peak_51434b 1.308142e-02 2.109280e-02\n",
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"Hela_peak_51475b 1.477075e-02 9.217611e-03\n",
"Hela_peak_51494b 1.320584e-02 1.161903e-02\n",
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"Hela_peak_51500a 1.458498e-02 1.844291e-02\n",
"Hela_peak_51505a 1.735024e-02 1.734221e-02\n",
"HCT116_peak_35207 1.226878e-02 1.025001e-02\n",
"Hela_peak_51516a 8.970189e-03 1.761165e-02\n",
"K562_peak_21466b -7.304533e-05 3.674923e-02\n",
"Hela_peak_51545c 1.467251e-02 1.292832e-02\n",
"Hela_peak_51547 1.707550e-02 1.585994e-02\n",
"HCT116_peak_35284 1.573171e-02 8.128262e-03\n",
"K562_peak_21660a -1.142511e-03 3.764764e-02\n",
"K562_peak_21684b -1.979210e-03 3.527472e-02\n",
"K562_peak_21695b -3.825716e-04 3.598869e-02\n",
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"K562_peak_21720b -4.845475e-04 3.850918e-02\n",
"Hela_peak_51629a 9.620887e-03 1.711201e-02\n",
"Hela_peak_51629b 9.248008e-03 1.769625e-02\n",
"Hela_peak_51629c 9.193813e-03 2.095128e-02\n",
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"K562_peak_21743b -1.047964e-03 3.352415e-02\n",
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"Hela_peak_51636a 1.545327e-02 1.540680e-02\n",
"Hela_peak_51636d 1.228808e-02 4.434068e-03\n",
"Hela_peak_51636e 1.483997e-02 1.260953e-02\n",
"HCT116_peak_35466c 2.228247e-02 -1.145032e-02\n",
"Hela_peak_51647 1.495165e-02 1.474782e-02\n",
"HCT116_peak_35515a 2.273125e-02 -2.175436e-02\n",
"Hela_peak_51663b 1.630493e-02 1.061549e-02\n",
"HCT116_peak_35597c 2.185588e-02 -2.082989e-02\n",
"Hela_peak_51728 1.147948e-02 5.070729e-03\n",
"Hela_peak_51733a 1.409569e-02 7.072824e-03\n",
"HCT116_peak_35644a 2.089094e-02 -1.590134e-02\n",
"Hela_peak_51797a 1.214833e-02 1.505909e-02\n",
"Hela_peak_51800c 1.637769e-02 1.078418e-02\n",
"Hela_peak_51801 1.169512e-02 1.284628e-02\n",
"Hela_peak_51802a 1.000655e-02 1.668699e-02\n",
"HCT116_peak_35733 2.223521e-02 -1.984555e-02\n",
"Hela_peak_51811b 1.359555e-02 1.000331e-02\n",
"Hela_peak_51822 -8.852073e-04 1.556089e-02\n",
"Hela_peak_51879b 5.870340e-03 9.958124e-03\n",
"Hela_peak_51885b 2.426577e-03 4.458392e-03\n",
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"Hela_peak_51905bm -7.213230e-03 5.422702e-03\n",
"Hela_peak_51905bn -5.703266e-03 1.700770e-02\n",
"Hela_peak_51928b 1.429651e-02 5.093309e-03\n",
"Hela_peak_51963b 1.377225e-02 1.128094e-02\n",
"Hela_peak_51963f 2.081843e-02 1.125035e-04\n",
"Hela_peak_51964 1.276294e-02 1.481679e-02\n",
"Hela_peak_51965 -4.259679e-04 3.668551e-02\n",
"K562_peak_22052 -1.065012e-03 3.260447e-02\n",
"K562_peak_22054c 3.470519e-04 3.198715e-02\n",
"Hela_peak_51992b 1.421390e-02 1.321558e-02\n",
"Hela_peak_52002 1.684833e-02 2.011505e-02\n",
"HCT116_peak_36031b 1.764945e-02 1.159195e-02\n",
"Hela_peak_52003b 1.409998e-02 7.308403e-03\n",
"Hela_peak_52011 1.587900e-02 1.576200e-02\n",
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"Hela_peak_52048b 1.468173e-02 1.388262e-02\n",
"Hela_peak_52052d 1.700318e-02 -4.343441e-04\n",
"Hela_peak_52052e 1.764191e-02 6.997561e-03\n",
"Hela_peak_52059a 1.770595e-02 1.148291e-02\n",
"K562_peak_22267 2.533533e-04 3.777744e-02\n",
"K562_peak_22274a -1.351796e-03 3.655839e-02\n",
"K562_peak_22274b 1.636170e-04 3.781149e-02\n",
"Hela_peak_52090d 1.598878e-02 -2.251930e-02\n",
"Hela_peak_52100b 1.416884e-02 8.865518e-03\n",
"HCT116_peak_36286 2.108928e-02 8.130908e-03\n",
"Hela_peak_52111 1.340295e-02 1.189862e-02\n",
"Hela_peak_52113 1.135707e-02 1.509414e-02\n",
"Hela_peak_52120a 1.572662e-02 8.387530e-03\n",
"Hela_peak_52140b 1.170756e-02 7.885536e-03\n",
"Hela_peak_52143b 9.804855e-03 1.944001e-02\n",
"Hela_peak_52147b 1.759043e-02 5.061894e-03\n",
"Hela_peak_52150e 1.840248e-02 -6.676574e-03\n",
"HCT116_peak_36419a 1.517341e-02 1.624323e-02\n",
"HCT116_peak_36420 1.573640e-02 1.177780e-02\n",
"Hela_peak_52176a 1.317171e-02 8.604488e-03\n",
"Hela_peak_52178a 1.063758e-02 1.292586e-02\n",
"Hela_peak_52180 1.752467e-02 6.730691e-03\n",
"HCT116_peak_36470b 1.178696e-02 1.599569e-02\n",
"Hela_peak_52181a 1.585441e-02 1.250726e-02\n",
"Hela_peak_52187b 1.588277e-02 1.232070e-02\n",
"Hela_peak_52187c 1.201736e-02 2.300055e-03\n",
"Hela_peak_52193b 1.753885e-02 -5.201132e-04\n",
"Hela_peak_52195c 9.108879e-03 4.746565e-03\n",
"Hela_peak_52200a 1.747902e-02 -3.215533e-03\n",
"Hela_peak_52200b 9.283004e-03 -1.215885e-03\n",
"Hela_peak_52207a 2.135323e-02 -1.664829e-02\n",
"Hela_peak_52209b 1.557124e-02 -4.783920e-03\n",
"Hela_peak_52209c 2.383288e-02 -7.804565e-03\n",
"Hela_peak_52215 1.798286e-02 9.902969e-04\n",
"Hela_peak_52228 1.405338e-02 -8.024106e-04\n",
"Hela_peak_52229 1.886191e-02 3.627372e-03\n",
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"Hela_peak_52340a 1.793066e-02 -8.454975e-03\n",
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"Hela_peak_52372 2.121787e-02 -2.842077e-03\n",
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"Hela_peak_52407 1.996964e-02 -2.235955e-02\n",
"Hela_peak_52412a 1.417925e-02 -4.363739e-03\n",
"Hela_peak_52414b 1.292652e-02 -1.155902e-02\n",
"Hela_peak_52414e 1.396027e-02 -5.424034e-03\n",
"Hela_peak_52427a 1.609822e-02 -9.677765e-03\n",
"Hela_peak_52427b 1.593889e-02 -7.429925e-03\n",
"Hela_peak_52479b 1.589715e-02 -6.367101e-03\n",
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"Hela_peak_52583 1.015313e-02 -5.856529e-03\n",
"Hela_peak_52610b 1.569797e-02 -1.054742e-02\n",
"Hela_peak_52615b 1.443939e-02 -1.568218e-02\n",
"Hela_peak_52637b 1.418240e-02 -8.167854e-03\n",
"Hela_peak_52644 1.078354e-02 -8.361039e-03\n",
"Hela_peak_52721 1.210823e-02 -7.936456e-03\n",
"Hela_peak_52726 1.461383e-02 -8.284804e-03\n",
"Hela_peak_52737a 1.492527e-02 -3.095946e-03\n",
"Hela_peak_52748d 1.525491e-02 -8.590177e-03\n",
"Hela_peak_52750a 1.576625e-02 -8.870425e-03\n",
"Hela_peak_52761a 1.765642e-02 -9.277174e-03\n",
"Hela_peak_52769b 8.738145e-03 -1.206057e-02\n",
"Hela_peak_52776b 1.304107e-02 -2.775638e-03\n",
"Hela_peak_52779 1.227936e-02 -1.322191e-02\n",
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"Hela_peak_52807a 1.535122e-02 -3.364473e-03\n",
"Hela_peak_52812b 8.271863e-03 -9.048551e-03\n",
"HCT116_peak_37091a 2.124267e-02 -9.297344e-03\n",
"Hela_peak_52832c 1.099556e-02 -1.153217e-02\n",
"Hela_peak_52832e 1.563993e-02 -8.393128e-03\n",
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"Hela_peak_52868d 1.312686e-02 2.039485e-03\n",
"Hela_peak_52868e 1.664541e-02 -2.360440e-03\n",
"Hela_peak_52870a 1.440969e-02 -9.196368e-03\n",
"Hela_peak_52875 8.533011e-03 -1.039402e-02\n",
"Hela_peak_52878 2.020085e-02 -1.286007e-02\n",
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"Hela_peak_52882c 8.397176e-03 -6.382942e-03\n",
"Hela_peak_52891c 1.490070e-02 -6.343623e-03\n",
"Hela_peak_52891d 1.337949e-02 -6.966905e-03\n",
"HCT116_peak_37184b 2.111071e-02 -2.391330e-03\n",
"Hela_peak_52912a 1.512496e-02 -1.028718e-02\n",
"Hela_peak_52927 1.058969e-02 -8.386030e-03\n",
"Hela_peak_53104b 1.505761e-02 -2.162874e-03\n",
"Hela_peak_53227 1.068637e-02 -7.803912e-03\n",
"Hela_peak_53242a 1.042007e-02 -6.368557e-03\n",
"Hela_peak_53259a 1.541566e-02 -1.175349e-02\n",
"HCT116_peak_37408 1.965359e-02 -2.493925e-02\n",
"Hela_peak_53341b 1.507645e-02 -8.568542e-03\n",
"Hela_peak_53368a 1.346513e-02 -4.197436e-03\n",
"Hela_peak_53407 1.696746e-02 -9.107638e-03\n",
"Hela_peak_53414 1.368346e-02 -5.403135e-03\n",
"Hela_peak_53427a 1.712511e-02 -1.210987e-02\n",
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"Hela_peak_53476 1.432291e-02 -6.046423e-03\n",
"Hela_peak_53481b 1.206235e-02 -9.860091e-03\n",
"Hela_peak_53495b 1.302354e-02 -2.059223e-02\n",
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"Hela_peak_53583b 1.599865e-02 -5.977474e-03\n",
"Hela_peak_53598b 1.561113e-02 -8.652579e-03\n",
"Hela_peak_53668b 1.347073e-02 4.460119e-03\n",
"Hela_peak_53739b 8.200240e-03 1.464388e-03\n",
"Hela_peak_53750b 1.135142e-02 6.638675e-03\n",
"Hela_peak_53751a 1.068175e-02 3.941167e-03\n",
"Hela_peak_53798d 1.231974e-02 7.168192e-03\n",
"Hela_peak_53798e 1.327184e-02 4.656290e-03\n",
"Hela_peak_53818 9.171902e-03 8.373225e-03\n",
"Hela_peak_53820d 9.399767e-03 8.063885e-03\n",
"Hela_peak_53872a 1.200715e-02 5.948151e-03\n",
"Hela_peak_53932a 1.391594e-02 -3.058656e-02\n",
"Hela_peak_53961 1.876538e-02 3.923343e-05\n",
"Hela_peak_54047a 1.638310e-02 3.271927e-03\n",
"Hela_peak_54051a 1.702595e-02 8.559622e-03\n",
"HCT116_peak_37967 2.084704e-02 1.313323e-02\n",
"Hela_peak_54134a 1.675503e-02 8.172521e-03\n",
"Hela_peak_54184b 1.017952e-02 4.625981e-03\n",
"Hela_peak_54201b 1.428068e-02 5.761211e-03\n",
"Hela_peak_54202b 9.006418e-03 7.726296e-03\n",
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"Hela_peak_54233b 1.024114e-02 8.131315e-03\n",
"Hela_peak_54240d 7.274972e-03 1.863302e-02\n",
"Hela_peak_54242c 1.626428e-02 8.077340e-03\n",
"Hela_peak_54316c 9.321868e-03 6.573096e-03\n",
"Hela_peak_54328c 1.407386e-02 8.222798e-03\n",
"Hela_peak_54343a 1.634905e-02 4.742748e-03\n",
"Hela_peak_54343b 1.290806e-02 9.831345e-03\n",
"Hela_peak_54370c 1.238481e-02 1.460349e-02\n",
"Hela_peak_54396a 4.608772e-03 1.786402e-02\n",
"Hela_peak_54432 1.603587e-02 2.951377e-03\n",
"Hela_peak_54436 1.091361e-02 1.704362e-02\n",
"Hela_peak_54443 1.148011e-02 5.444232e-03\n",
"Hela_peak_54445 1.458260e-02 5.651090e-03\n",
"Hela_peak_54463 1.124128e-02 -5.048684e-03\n",
"Hela_peak_54509b 1.382590e-02 1.808979e-03\n",
"HCT116_peak_38388b 2.337867e-02 -1.478929e-02\n",
"Hela_peak_54615b 1.163234e-02 7.728811e-03\n",
"Hela_peak_54615c 9.524615e-03 1.117942e-02\n",
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"Hela_peak_54722 1.515303e-02 6.041370e-04\n",
"Hela_peak_54730b 1.185781e-02 8.865574e-03\n",
"Hela_peak_54730c 9.812571e-03 2.132637e-03\n",
"HCT116_peak_38555 1.441123e-02 2.067276e-02\n",
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"Hela_peak_54771c 1.204265e-02 1.188083e-02\n",
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"Hela_peak_54804b 1.684670e-02 7.464121e-03\n",
"Hela_peak_54839a 1.290940e-02 2.766757e-03\n",
"Hela_peak_54839b 1.469330e-02 7.120468e-03\n",
"Hela_peak_54862 1.649880e-02 5.750833e-03\n",
"Hela_peak_54867b 1.565322e-02 1.938611e-03\n",
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"Hela_peak_54896b 1.234408e-02 6.044472e-03\n",
"Hela_peak_54914a 6.911911e-03 5.782437e-03\n",
"Hela_peak_54987 8.693832e-03 4.459020e-04\n",
"Hela_peak_55000b 1.359968e-02 5.847219e-03\n",
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"Hela_peak_55020 1.251834e-02 8.223596e-03\n",
"Hela_peak_55043 2.035354e-02 -1.833490e-04\n",
"Hela_peak_55055 1.886077e-02 -6.301291e-03\n",
"Hela_peak_55071 1.762571e-02 -2.395577e-03\n",
"HCT116_peak_38840 2.177156e-02 -1.855966e-02\n",
"HCT116_peak_38841 1.573440e-02 -2.695908e-03\n",
"Hela_peak_55076a 1.608609e-02 -7.592583e-03\n",
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"Hela_peak_55114 1.745847e-02 -1.272065e-03\n",
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"Hela_peak_60194b 1.241749e-02 6.313223e-03\n",
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"Hela_peak_60220 1.193666e-02 6.879165e-03\n",
"Hela_peak_60222b 9.779680e-03 6.408338e-03\n",
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"Hela_peak_60292 1.165413e-02 9.766945e-03\n",
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"Hela_peak_60718 1.070628e-02 5.679058e-03\n",
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"HCT116_peak_47154b 2.090030e-02 6.642659e-03\n",
"Hela_peak_62332a 1.416685e-02 1.324232e-02\n",
"Hela_peak_62332b 1.484670e-02 1.108507e-02\n",
"Hela_peak_62337a 1.487301e-02 1.145536e-02\n",
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"Hela_peak_62441 1.232516e-02 9.456311e-03\n",
"Hela_peak_62442b 1.261900e-02 1.549565e-02\n",
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"Hela_peak_62494 1.633245e-02 1.018790e-02\n",
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"Hela_peak_71669a 1.462164e-02 1.004423e-02\n",
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"Hela_peak_72183b 1.762393e-02 -8.795386e-03\n",
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"Hela_peak_72575a 1.571694e-02 -9.054901e-03\n",
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"Hela_peak_72628b 1.717864e-02 -5.707717e-03\n",
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"Hela_peak_73112 1.871905e-02 -3.117936e-03\n",
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"Hela_peak_73166 1.756054e-02 1.561254e-03\n",
"Hela_peak_73177c 1.399522e-02 -6.750657e-03\n",
"Hela_peak_73259b 1.722789e-02 -6.782930e-03\n",
"Hela_peak_73385d 1.512819e-02 -2.666616e-03\n",
"Hela_peak_73547 2.128030e-02 -2.193718e-02\n",
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"Hela_peak_73682b 2.099314e-02 -1.585579e-02\n",
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"Hela_peak_73711a 1.781191e-02 -8.804961e-03\n",
"Hela_peak_73779 2.024728e-02 -6.922057e-03\n",
"Hela_peak_73788a 1.724111e-02 -7.029221e-03\n",
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"Hela_peak_74111b 1.783384e-02 6.847713e-04\n",
"Hela_peak_74118 1.965335e-02 -2.984922e-03\n",
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"Hela_peak_74175 1.175512e-02 -3.925548e-03\n",
"Hela_peak_74225b 1.711977e-02 -4.380042e-03\n",
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"Hela_peak_74296c 1.870212e-02 1.150462e-03\n",
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"Hela_peak_74995b 1.738896e-02 -1.278571e-02\n",
"HCT116_peak_56100 2.173528e-02 -1.832480e-02\n",
"Hela_peak_75092f 1.437734e-02 -5.736024e-03\n",
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"Hela_peak_75137a 1.832966e-02 -6.377073e-03\n",
"Hela_peak_75144 1.976674e-02 -7.595860e-03\n",
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"Hela_peak_75403b 1.253792e-02 1.557914e-03\n",
"Hela_peak_75423b 1.892639e-02 3.482690e-03\n",
"HCT116_peak_56477b 1.007443e-02 1.187918e-02\n",
"Hela_peak_75504 5.154070e-03 1.862631e-02\n",
"Hela_peak_75552a 7.919258e-03 1.296909e-02\n",
"Hela_peak_75556 1.173068e-02 9.572362e-03\n",
"Hela_peak_75571b 1.223041e-02 1.344583e-02\n",
"Hela_peak_75579 1.204846e-02 6.085153e-03\n",
"Hela_peak_75597b 1.029401e-02 -7.910235e-05\n",
"Hela_peak_75615 1.132878e-02 5.452432e-03\n",
"Hela_peak_75637b 9.257787e-03 1.204201e-02\n",
"Hela_peak_75649 8.998363e-03 4.064256e-03\n",
"Hela_peak_75696 1.040300e-02 1.184272e-02\n",
"Hela_peak_75707a 7.057721e-03 7.011405e-03\n",
"Hela_peak_75712b 1.364281e-02 8.105403e-03\n",
"Hela_peak_75716b 9.201947e-03 6.557145e-04\n",
"Hela_peak_75719 1.203085e-02 7.368064e-03\n",
"Hela_peak_75743b 1.397001e-02 5.393949e-03\n",
"Hela_peak_75810 1.274577e-02 5.420062e-03\n",
"Hela_peak_75819a 9.969608e-03 1.413332e-02\n",
"HCT116_peak_56768 1.383266e-02 1.076019e-02\n",
"Hela_peak_75846a 1.411992e-02 1.301096e-03\n",
"Hela_peak_75959a 1.213601e-02 4.753092e-03\n",
"Hela_peak_75978 1.260640e-02 5.888259e-03\n",
"Hela_peak_75990a 1.191282e-02 1.567089e-03\n",
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"Hela_peak_76099b 9.136669e-03 3.901553e-03\n",
"Hela_peak_76113b 8.400277e-03 6.192217e-03\n",
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"Hela_peak_76172 1.178762e-02 1.813539e-03\n",
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"Hela_peak_76444c 1.125136e-02 7.535897e-03\n",
"Hela_peak_76452b 1.571814e-02 4.359658e-03\n",
"Hela_peak_76468c 1.086499e-02 3.770574e-03\n",
"Hela_peak_76499b 1.054422e-02 -2.218909e-04\n",
"Hela_peak_76515a 4.267394e-03 6.004034e-03\n",
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"Hela_peak_76720e 3.839781e-03 3.677472e-03\n",
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"Hela_peak_76870d 7.078795e-03 1.698165e-03\n",
"Hela_peak_76904 -2.110465e-02 -1.371589e-02\n",
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"Hela_peak_77005a 2.092222e-02 -2.113463e-02\n",
"Hela_peak_77022e 1.718548e-02 5.300141e-03\n",
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"Hela_peak_77152b 1.189557e-02 3.227303e-03\n",
"Hela_peak_77246b 7.749173e-03 6.992490e-03\n",
"Hela_peak_77401b 3.313053e-03 3.739981e-04\n",
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"Hela_peak_77795a 7.405407e-03 3.533210e-03\n",
"Hela_peak_77916a 9.930907e-03 1.990450e-03\n",
"Hela_peak_77927a 7.702291e-03 7.397587e-03\n",
"Hela_peak_77944a 5.346318e-03 -2.128437e-03\n",
"Hela_peak_77948b 4.440942e-03 2.814982e-03\n",
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"Hela_peak_78087 1.109897e-02 -5.806391e-04\n",
"Hela_peak_78111c 8.028290e-03 1.013839e-02\n",
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"Hela_peak_78273 9.928283e-03 6.012133e-03\n",
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"Hela_peak_78427 8.088731e-03 9.193740e-03\n",
"Hela_peak_78492a 8.918902e-03 1.479276e-02\n",
"Hela_peak_78607c 1.708559e-02 1.459702e-02\n",
"Hela_peak_78893 6.007020e-03 2.251223e-02\n",
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"Hela_peak_78898a 1.052031e-02 1.728673e-02\n",
"Hela_peak_78906b 8.023771e-03 1.971165e-02\n",
"Hela_peak_78908b 5.674648e-03 2.035938e-02\n",
"Hela_peak_78939a 7.055269e-03 2.883912e-02\n",
"K562_peak_34938b 6.686343e-03 2.347428e-02\n",
"HCT116_peak_58210b 2.389157e-02 -2.034916e-02\n",
"Hela_peak_79008a 8.895818e-03 2.184683e-02\n",
"Hela_peak_79015b 8.265385e-03 1.113406e-02\n",
"Hela_peak_79061b 1.247546e-02 6.485374e-03\n",
"Hela_peak_79082b 8.966774e-03 1.801045e-02\n",
"Hela_peak_79083b 6.800525e-03 1.124151e-02\n",
"Hela_peak_79121b 1.055985e-02 1.441169e-02\n",
"Hela_peak_79135b 9.633084e-03 1.519729e-02\n",
"Hela_peak_79136b 1.357051e-02 1.998990e-02\n",
"Hela_peak_79140b 8.580836e-03 1.145546e-02\n",
"Hela_peak_79143b 1.150250e-02 1.587232e-02\n",
"Hela_peak_79145a 8.723522e-03 1.725781e-02\n",
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"HCT116_peak_61950a 1.314511e-02 -3.928669e-03\n",
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"Hela_peak_84528 1.426070e-02 1.915770e-05\n",
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"Hela_peak_85343b 7.679003e-03 -2.920421e-03\n",
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"Hela_peak_85577c 1.331601e-02 -2.989042e-03\n",
"Hela_peak_85580b 1.691527e-02 -6.996207e-03\n",
"HCT116_peak_62895a 2.183742e-02 -2.332971e-02\n",
"Hela_peak_85616c 1.359813e-02 -4.486232e-03\n",
"Hela_peak_85911b 1.210700e-02 -7.267377e-03\n",
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"Hela_peak_86993c 1.148180e-02 -8.610093e-03\n",
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"HCT116_peak_63500b 2.202946e-02 -1.846604e-02\n",
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"HCT116_peak_63658 1.928097e-02 -2.096386e-02\n",
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"HCT116_peak_63798 2.296872e-02 -2.272761e-02\n",
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"Hela_peak_88324c 7.995477e-03 -6.121120e-03\n",
"Hela_peak_88434 1.495871e-02 -6.698976e-03\n",
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"Hela_peak_103239b -3.394399e-03 9.608872e-03\n",
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"Hela_peak_111295b 8.894848e-03 9.002709e-03\n",
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"Hela_peak_111786d 1.306150e-02 7.270815e-03\n",
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"Hela_peak_111891 1.103284e-02 1.199372e-02\n",
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"Hela_peak_112213b 1.366481e-02 -6.044630e-03\n",
"Hela_peak_112245b 6.847136e-03 2.589519e-02\n",
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"Hela_peak_112248b 1.363232e-02 1.127761e-02\n",
"Hela_peak_112248c 1.060341e-02 1.317453e-02\n",
"Hela_peak_112251c 1.460432e-02 1.473313e-02\n",
"Hela_peak_112251f 1.403706e-02 1.340988e-02\n",
"Hela_peak_112258c 1.597279e-02 3.189685e-03\n",
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"Hela_peak_112270b 1.024117e-02 1.407353e-02\n",
"Hela_peak_112270c 1.206549e-02 1.071405e-02\n",
"Hela_peak_112277c 8.865817e-03 1.336640e-02\n",
"HCT116_peak_75910 1.636355e-02 1.725806e-02\n",
"Hela_peak_112284c 7.424634e-03 1.755421e-02\n",
"Hela_peak_112286a 1.046867e-02 1.430317e-02\n",
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"Hela_peak_112335b 1.003764e-02 1.272105e-02\n",
"Hela_peak_112337c 9.001957e-03 1.661839e-02\n",
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"Hela_peak_112371 9.796153e-03 1.273344e-02\n",
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"Hela_peak_112397a 1.312839e-02 1.642624e-02\n",
"Hela_peak_112427a 9.945007e-03 1.286657e-02\n",
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"Hela_peak_112437c 3.473845e-03 2.363468e-02\n",
"Hela_peak_112448d 9.800420e-03 6.968945e-03\n",
"HCT116_peak_76073c 1.372426e-02 1.386553e-02\n",
"Hela_peak_112452b 1.570435e-02 7.368978e-03\n",
"Hela_peak_112454b 1.419061e-02 1.309404e-02\n",
"Hela_peak_112477a 8.628262e-03 2.022615e-02\n",
"Hela_peak_112503c 1.354208e-02 9.303675e-03\n",
"Hela_peak_112513 1.256600e-02 1.257264e-02\n",
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"Hela_peak_112561c 7.652373e-03 1.329755e-02\n",
"Hela_peak_112565c 1.021979e-02 1.917629e-02\n",
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"Hela_peak_112610a 1.206799e-02 1.372139e-02\n",
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"Hela_peak_112692 1.312195e-02 2.114710e-02\n",
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"Hela_peak_112710a 1.083233e-02 1.461322e-02\n",
"Hela_peak_112737 7.312803e-03 1.833546e-02\n",
"Hela_peak_112756c 7.166390e-03 9.898186e-03\n",
"Hela_peak_112774 1.218719e-02 1.101801e-02\n",
"Hela_peak_112806a 4.450354e-03 1.961099e-02\n",
"Hela_peak_112819a 2.267166e-03 1.160922e-02\n",
"Hela_peak_112824 1.112910e-02 1.173500e-02\n",
"Hela_peak_112839 1.512850e-02 7.161782e-03\n",
"Hela_peak_112874c 1.201562e-02 1.295406e-02\n",
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"Hela_peak_113001c 1.008474e-02 1.371086e-02\n",
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"Hela_peak_121921b 1.159038e-02 4.381934e-03\n",
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"Hela_peak_122312a 9.785920e-03 2.023029e-02\n",
"Hela_peak_122314b 9.117315e-03 1.880855e-02\n",
"Hela_peak_122347b 1.037633e-02 1.685228e-02\n",
"Hela_peak_122354b 6.869430e-03 1.276804e-02\n",
"Hela_peak_122357c 1.131532e-02 1.528181e-02\n",
"Hela_peak_122360 1.248502e-02 1.726247e-02\n",
"Hela_peak_122369 9.727043e-03 8.726623e-03\n",
"Hela_peak_122391a 7.913603e-03 1.369348e-02\n",
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"Hela_peak_122426b 9.309520e-03 1.623606e-02\n",
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"Hela_peak_122461a 1.267118e-02 1.624445e-02\n",
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"Hela_peak_122508b 1.061113e-02 2.116588e-02\n",
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"Hela_peak_122572 7.924320e-03 2.199953e-02\n",
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"Hela_peak_122616b 7.401592e-03 2.275898e-02\n",
"K562_peak_51223 4.684047e-03 3.087531e-02\n",
"K562_peak_51232 7.962970e-03 2.452151e-02\n",
"Hela_peak_122638 1.188163e-02 9.389018e-03\n",
"Hela_peak_122649a 1.167249e-02 2.155623e-02\n",
"Hela_peak_122652 7.674608e-03 2.494575e-02\n",
"Hela_peak_122654 8.590030e-03 2.108043e-02\n",
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"Hela_peak_122699 1.130632e-02 1.359632e-02\n",
"Hela_peak_122705b 1.035170e-02 2.085812e-02\n",
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"Hela_peak_122739 8.358515e-03 2.184492e-02\n",
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"Hela_peak_122769 6.164084e-03 1.989874e-02\n",
"Hela_peak_122794b 1.124166e-02 1.601032e-02\n",
"Hela_peak_122811c 9.849241e-03 1.537136e-02\n",
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"Hela_peak_123801 1.001386e-02 2.161705e-02\n",
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"Hela_peak_128878a 9.501201e-03 4.518345e-03\n",
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"HCT116_peak_86443 2.384897e-02 -1.683601e-02\n",
"HCT116_peak_86458b 2.402281e-02 -2.067835e-02\n",
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"Hela_peak_131910 1.193060e-02 -2.322145e-02\n",
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"Hela_peak_132075 2.069215e-02 2.762088e-03\n",
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"Hela_peak_132462d 1.465127e-02 -1.994138e-03\n",
"Hela_peak_132473a 1.524046e-02 -3.499425e-03\n",
"HCT116_peak_90113b 1.967024e-02 1.075204e-03\n",
"Hela_peak_132479b 1.811653e-02 -1.053589e-02\n",
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"Hela_peak_132494d 1.549513e-02 3.067439e-03\n",
"Hela_peak_132494e 1.868547e-02 -3.044089e-03\n",
"HCT116_peak_90152 2.189212e-02 -2.100186e-02\n",
"Hela_peak_132500a 1.416244e-02 -3.028365e-03\n",
"Hela_peak_132510b 1.735640e-02 -4.049447e-03\n",
"Hela_peak_132510c 1.619408e-02 -4.925986e-03\n",
"Hela_peak_132510e 1.478197e-02 8.574874e-04\n",
"Hela_peak_132513c 1.600799e-02 -6.791905e-03\n",
"Hela_peak_132517a 1.955908e-02 -3.275121e-03\n",
"Hela_peak_132522b 1.356529e-02 -2.571436e-03\n",
"Hela_peak_132532c 1.750543e-02 -3.465989e-03\n",
"Hela_peak_132535 1.071804e-02 -1.439745e-03\n",
"Hela_peak_132539c 1.229213e-02 1.338038e-04\n",
"Hela_peak_132539d 1.672769e-02 -1.214904e-02\n",
"Hela_peak_132542c 1.695458e-02 -9.451028e-03\n",
"Hela_peak_132546 1.238149e-02 7.789248e-03\n",
"Hela_peak_132549b 1.216310e-02 4.594128e-04\n",
"Hela_peak_132549c 1.400019e-02 -3.301976e-03\n",
"Hela_peak_132554c 9.057446e-03 -4.460856e-03\n",
"Hela_peak_132554d 1.106025e-02 4.493044e-03\n",
"Hela_peak_132560 1.197800e-02 -7.504410e-03\n",
"Hela_peak_132734b 1.411472e-02 -5.665308e-03\n",
"Hela_peak_132835a 7.247224e-03 4.068123e-03\n",
"Hela_peak_132847c 1.098213e-02 -6.226203e-03\n",
"Hela_peak_132874a 9.162863e-03 -9.430393e-03\n",
"Hela_peak_132938a 1.599013e-02 -8.466103e-03\n",
"Hela_peak_132941c 1.312948e-02 -8.590490e-03\n",
"Hela_peak_133195b 1.566765e-02 -7.426409e-03\n",
"Hela_peak_133394c 1.428735e-02 -1.030537e-03\n",
"Hela_peak_133487b 1.072068e-02 9.345760e-04\n",
"Hela_peak_133536b 1.872839e-02 -2.354477e-02\n",
"HCT116_peak_90690b 2.310903e-02 -2.181442e-02\n",
"Hela_peak_133563b 1.639323e-02 -2.595002e-02\n",
"Hela_peak_133583b 1.023243e-02 -2.954784e-02\n",
"Hela_peak_133741 1.544349e-02 -7.113255e-03\n",
"Hela_peak_133821c 1.306624e-02 -4.119111e-03\n",
"Hela_peak_133923b 1.364781e-02 6.790105e-03\n",
"Hela_peak_133973c 1.254725e-02 7.523432e-03\n",
"Hela_peak_134042c 1.294318e-02 9.574621e-03\n",
"Hela_peak_134064 1.337076e-02 7.233613e-03\n",
"Hela_peak_134067b 1.042913e-02 8.129303e-03\n",
"Hela_peak_134105b 1.535376e-02 -1.084558e-02\n",
"Hela_peak_134112a 1.024763e-02 1.571979e-02\n",
"Hela_peak_134145b 1.269255e-02 -4.336179e-04\n",
"Hela_peak_134150e 5.818994e-03 1.039611e-02\n",
"Hela_peak_134154a 1.010675e-02 8.144013e-03\n",
"Hela_peak_134162a 1.144328e-02 1.017339e-02\n",
"Hela_peak_134174a 6.286954e-03 -8.038086e-06\n",
"Hela_peak_134174c 1.383533e-02 8.884650e-03\n",
"Hela_peak_134179a 1.116615e-02 5.142636e-03\n",
"Hela_peak_134193b 1.410992e-02 1.913561e-04\n",
"Hela_peak_134196b 1.452586e-02 8.222410e-03\n",
"Hela_peak_134205c 1.003147e-02 -2.982227e-04\n",
"Hela_peak_134260c 8.642731e-03 6.405896e-03\n",
"Hela_peak_134264c 1.049171e-02 1.321574e-02\n",
"Hela_peak_134308b 7.417698e-03 2.674594e-02\n",
"Hela_peak_134325b 9.502859e-03 2.424928e-02\n",
"Hela_peak_134331b 9.072190e-03 1.991132e-02\n",
"Hela_peak_134346 1.164249e-02 1.797398e-02\n",
"Hela_peak_134349 1.619856e-02 1.423973e-02\n",
"Hela_peak_134355b 1.101750e-02 2.039431e-02\n",
"K562_peak_55886a 2.054793e-03 3.544123e-02\n",
"Hela_peak_134375c 1.015959e-02 2.010343e-02\n",
"Hela_peak_134388 1.056764e-02 2.199759e-02\n",
"Hela_peak_134516a 1.636136e-02 -6.324655e-03\n",
"Hela_peak_134526g 1.006958e-02 -4.205763e-03\n",
"Hela_peak_134527a 1.477419e-02 -8.534093e-03\n",
"Hela_peak_134527b 1.356808e-02 4.176148e-04\n",
"HCT116_peak_91224c 1.414764e-02 -4.402436e-03\n",
"Hela_peak_134527c 1.222821e-02 -4.352680e-03\n",
"Hela_peak_134621c 1.476215e-02 -1.311974e-02\n",
"Hela_peak_134678c 1.369113e-02 -4.471631e-03\n",
"Hela_peak_134779c 1.946782e-02 -2.939754e-03\n",
"Hela_peak_134785d 1.530687e-02 -4.424899e-03\n",
"Hela_peak_134788d 1.757152e-02 -9.387382e-03\n",
"Hela_peak_134866a 1.388910e-02 -5.228219e-03\n",
"HCT116_peak_91444 1.683399e-02 1.418103e-03\n",
"Hela_peak_135049 1.787490e-02 -1.984537e-02\n",
"Hela_peak_135113b 1.936732e-02 -9.019347e-03\n",
"Hela_peak_135129c 1.406141e-02 -7.706019e-03\n",
"Hela_peak_135215b 1.338394e-02 -7.776505e-03\n",
"Hela_peak_135220 1.236058e-02 -1.074524e-02\n",
"Hela_peak_135232b 1.389380e-02 -5.874535e-03\n",
"Hela_peak_135236b 1.287647e-02 -9.693140e-03\n",
"Hela_peak_135385d 1.366885e-02 -5.747520e-03\n",
"Hela_peak_135445 8.956660e-03 -8.021770e-03\n",
"Hela_peak_135448 1.101283e-02 -8.845627e-03\n",
"Hela_peak_135577c 1.419774e-02 8.321195e-03\n",
"Hela_peak_135599b 1.244948e-02 7.097159e-03\n",
"Hela_peak_135625d 1.204075e-02 5.880322e-03\n",
"Hela_peak_135633b 7.323058e-03 -9.325273e-04\n",
"Hela_peak_135724a 1.199454e-02 6.701180e-03\n",
"Hela_peak_135724b 1.211371e-02 3.054506e-03\n",
"Hela_peak_135815 1.024715e-02 9.481051e-04\n",
"Hela_peak_135875b 7.945935e-03 1.219680e-03\n",
"Hela_peak_135880 1.042613e-02 5.170926e-03\n",
"Hela_peak_135891b 1.269095e-02 4.956776e-03\n",
"Hela_peak_135932e 1.037848e-02 6.232649e-03\n",
"Hela_peak_135957a 1.108459e-02 4.642309e-03\n",
"Hela_peak_135960a 1.236652e-02 8.033682e-03\n",
"Hela_peak_135969 1.089475e-02 3.195164e-04\n",
"Hela_peak_135980a 1.126805e-02 1.274935e-02\n",
"Hela_peak_135991b 1.090204e-02 4.890415e-03\n",
"Hela_peak_136086 1.079231e-02 7.261701e-03\n",
"Hela_peak_136109b 1.166544e-02 1.814710e-03\n",
"Hela_peak_136125b 1.163414e-02 1.908329e-03\n",
"Hela_peak_136171 1.796978e-02 -7.163712e-03\n",
"Hela_peak_136242b 9.944482e-03 1.257374e-02\n",
"Hela_peak_136252a 1.340485e-02 9.656472e-03\n",
"Hela_peak_136404a 1.387949e-02 4.765654e-03\n",
"HCT116_peak_92286b 1.988465e-02 8.973073e-05\n",
"Hela_peak_136630b 1.106951e-02 6.774513e-03\n",
"Hela_peak_136688 9.273929e-03 -3.734096e-03\n",
"Hela_peak_136754b 1.516364e-02 3.171620e-03\n",
"Hela_peak_136760b 9.568247e-03 1.834642e-03\n",
"Hela_peak_136771b 1.603026e-02 4.400601e-03\n",
"Hela_peak_136795b 1.134290e-02 1.747842e-03\n",
"Hela_peak_136810 1.083302e-02 -5.469286e-04\n",
"Hela_peak_136813a 1.231423e-02 4.875552e-03\n",
"Hela_peak_136935a 6.318970e-03 -2.265378e-04\n",
"Hela_peak_137258 8.074006e-03 1.895274e-02\n",
"Hela_peak_137259b 7.214361e-03 2.667823e-02\n",
"Hela_peak_137260 6.018829e-03 2.899307e-02\n",
"Hela_peak_137262a 4.221976e-03 3.355797e-02\n",
"K562_peak_56615f 4.389887e-03 3.705028e-02\n",
"K562_peak_56626 -1.053207e-03 3.965624e-02\n",
"Hela_peak_137296b 1.288718e-02 3.909329e-03\n",
"Hela_peak_137307c 1.987879e-02 -9.422581e-03\n",
"Hela_peak_137433b 1.494170e-02 6.090269e-03\n",
"Hela_peak_137435a 1.343646e-02 7.667262e-04\n",
"Hela_peak_137436a 1.414613e-02 3.561192e-03\n",
"Hela_peak_137456 1.803401e-02 2.291671e-03\n",
"Hela_peak_137561c 1.244032e-02 1.120702e-02\n",
"Hela_peak_137585 1.485328e-02 5.454716e-03\n",
"Hela_peak_137587c 9.357056e-03 5.648827e-03\n",
"Hela_peak_137590 1.307489e-02 7.185836e-03\n",
"Hela_peak_137601 1.204564e-02 9.521958e-03\n",
"Hela_peak_137604 1.580109e-02 3.292455e-06\n",
"Hela_peak_137641b 1.120013e-02 5.507820e-03\n",
"Hela_peak_137708a 1.352146e-02 9.533336e-03\n",
"Hela_peak_137729b 1.205227e-02 -1.472371e-03\n",
"Hela_peak_137737a 5.824568e-03 4.935710e-03\n",
"Hela_peak_137772b 1.005292e-02 4.838824e-03\n",
"Hela_peak_137782b 1.152158e-02 4.707914e-03\n",
"Hela_peak_137785b 1.369294e-02 7.547963e-03\n",
"Hela_peak_137791 6.260026e-03 1.446076e-02\n",
"Hela_peak_137793b 1.259088e-02 9.613969e-03\n",
"Hela_peak_137793d 1.549287e-02 1.681602e-03\n",
"Hela_peak_137832b 1.132251e-02 7.917002e-03\n",
"Hela_peak_137847b 1.308199e-02 5.411512e-03\n",
"Hela_peak_137847c 1.392702e-02 5.107136e-04\n",
"Hela_peak_137860c 6.306923e-03 3.219274e-02\n",
"Hela_peak_137864a 4.606552e-03 3.401257e-02\n",
"Hela_peak_137868 6.289108e-03 2.886730e-02\n",
"Hela_peak_137870 7.293934e-03 3.161502e-02\n",
"K562_peak_57003c 6.098354e-03 2.833499e-02\n",
"Hela_peak_137877 4.643087e-03 3.187427e-02\n",
"Hela_peak_137879b 4.816089e-03 2.727271e-02\n",
"Hela_peak_137891a 9.021093e-03 5.144370e-03\n",
"Hela_peak_137900a 9.424312e-03 -2.460512e-02\n",
"Hela_peak_137904 1.036724e-02 2.164981e-02\n",
"Hela_peak_137908b 5.010932e-03 2.841740e-02\n",
"Hela_peak_137914a 9.871017e-03 4.504371e-03\n",
"Hela_peak_137949c 1.539183e-02 1.798210e-03\n",
"Hela_peak_137950 1.320374e-02 -1.600642e-03\n",
"Hela_peak_137954 1.108623e-02 9.802125e-03\n",
"Hela_peak_137956c 1.262171e-02 5.244532e-03\n",
"Hela_peak_137967c 1.502961e-02 -2.171062e-05\n",
"Hela_peak_137983b 6.134812e-03 -2.029952e-02\n",
"Hela_peak_137987b 7.217027e-03 1.803704e-02\n",
"Hela_peak_138002b 1.292846e-02 1.783081e-03\n",
"Hela_peak_138050a 8.737294e-03 1.928120e-02\n",
"K562_peak_57190 -1.747873e-03 3.753729e-02\n",
"Hela_peak_138052 8.390628e-03 2.480109e-02\n",
"Hela_peak_138135 1.206476e-02 1.287421e-03\n",
"K562_peak_57343c -1.136802e-03 3.154074e-02\n",
"K562_peak_57357c 1.582905e-03 3.401433e-02\n",
"Hela_peak_138163 -1.424858e-03 3.039454e-02\n",
"Hela_peak_138164 1.256640e-03 2.426499e-02\n",
"K562_peak_57380y 3.578561e-03 3.243658e-02\n",
"HCT116_peak_93543 1.044236e-03 3.801846e-02\n",
"K562_peak_57380bc -2.316560e-03 3.094646e-02\n",
"K562_peak_57408p -2.508714e-03 3.443519e-02\n",
"Hela_peak_138172 -1.866541e-03 3.386802e-02\n",
"K562_peak_57408x -1.161217e-03 3.796931e-02\n",
"K562_peak_57433d 1.263773e-03 3.887011e-02\n",
"K562_peak_57435d -1.015275e-03 3.904447e-02\n",
"K562_peak_57436d -9.376163e-04 3.737353e-02\n",
"K562_peak_57454b 2.103349e-03 3.695106e-02\n",
"K562_peak_57454d 2.659546e-03 3.350316e-02\n",
"K562_peak_57457b -3.154637e-03 3.523880e-02\n",
"K562_peak_57467b -7.469237e-04 4.059980e-02\n",
"K562_peak_57467c -1.093510e-03 3.425520e-02\n",
"K562_peak_57509b 3.261322e-04 3.581221e-02\n",
"K562_peak_57574b -2.172015e-03 3.105348e-02\n",
"K562_peak_57574c -2.928692e-03 3.626421e-02\n",
"K562_peak_57574d -2.471893e-03 3.775303e-02\n",
"K562_peak_57574g -1.683027e-03 4.232230e-02\n",
"K562_peak_57574h 5.272538e-05 4.176382e-02\n",
"Hela_peak_138208 3.269637e-03 4.175070e-03\n",
"HCT116_peak_93629 1.674785e-02 8.764308e-03\n",
"Hela_peak_138278b 1.038759e-02 2.993853e-03\n",
"Hela_peak_138314 1.222216e-02 7.092110e-04\n",
"Hela_peak_138326c 1.743609e-02 4.639038e-03\n",
"Hela_peak_138400 1.100395e-02 2.948213e-03\n",
"Hela_peak_138542e 1.135172e-02 1.383667e-02\n",
"Hela_peak_138560c 1.344246e-02 9.839686e-03\n",
"Hela_peak_138588a 1.114320e-02 6.886063e-03\n",
"Hela_peak_138597a 1.114535e-02 -8.043704e-04\n",
"Hela_peak_138597b 1.184878e-02 2.092852e-03\n",
"Hela_peak_138604b 1.162418e-02 1.338468e-05\n",
"Hela_peak_138639a 1.958263e-02 -1.645798e-02\n",
"Hela_peak_138662a 7.402616e-03 5.148582e-03\n",
"Hela_peak_138687b 1.632005e-02 5.747770e-03\n",
"Hela_peak_138702a 9.331831e-03 6.392104e-03\n",
"Hela_peak_138730b 1.033829e-02 1.086851e-03\n",
"HCT116_peak_94117b 1.178224e-02 8.664913e-03\n",
"Hela_peak_138765c 1.216091e-02 2.633533e-03\n",
"Hela_peak_138772b 9.304095e-03 7.027795e-03\n",
"Hela_peak_138792b 1.232495e-02 6.474760e-03\n",
"Hela_peak_138817c 9.399972e-03 1.240935e-02\n",
"\n",
"\n",
"\n",
"\n",
"$method\n",
"[1] \"RGCCA\"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"out"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Load metadata\n",
"metadata<-readRDS(\"../data/single-cell/CellLines_metadata.RDS\", refhook = NULL)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \n",
" [11] \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \n",
" [21] \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \n",
" [31] \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \n",
" [41] \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \n",
" [51] \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \n",
" [61] \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \n",
" [71] \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \n",
" [81] \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \"HCT\" \n",
" [91] \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\"\n",
"[101] \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\"\n",
"[111] \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\"\n",
"[121] \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\" \"Hela\"\n",
"[131] \"Hela\" \"Hela\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\"\n",
"[141] \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\"\n",
"[151] \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\"\n",
"[161] \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\"\n",
"[171] \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\"\n",
"[181] \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\"\n",
"[191] \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\"\n",
"[201] \"K562\" \"K562\" \"K562\" \"K562\" \"K562\" \"K562\"\n"
]
}
],
"source": [
"print(metadata[, c(\"celltype\")])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"write.table(metadata[, c(\"celltype\")], \"celltype.txt\", sep=\"\\t\", row.names=FALSE, col.names=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "ERROR",
"evalue": "Error in eval(expr, envir, enclos): 找不到对象'metadata'\n",
"output_type": "error",
"traceback": [
"Error in eval(expr, envir, enclos): 找不到对象'metadata'\nTraceback:\n"
]
}
],
"source": [
"factorization=read.table(\"../data/single-cell/AE_FAETC_EM.txt\", sep=\" \", header=FALSE)\n",
"\n",
"library(\"ggplot2\")\n",
"library(\"clusterCrit\")\n",
"#source(\"runfactorization.R\")\n",
"\n",
"# Parameters for the plots\n",
"dot_size <- 1.5\n",
"dot_alpha <- 1.0\n",
"xlabel <- \"Factor 1\"\n",
"ylabel <- \"Factor 2\"\n",
"\n",
"# Load annotations from the metadata\n",
"sample_annot <- metadata[, c(\"sample.rna\", \"celltype\")]\n",
"\n",
"# Folder for results\n",
"results_folder <- \"../results_single_cell_AE_FAETC_EM/\"\n",
"# Create output folder\n",
"dir.create(results_folder, showWarnings = FALSE)\n",
"\n",
"c_index <- numeric(0)\n",
"\n",
"# Get factorization result\n",
"factors <- factorization\n",
"\n",
"# Delete NAs\n",
"factors <- factors[!is.na(factors[,1]) & !is.na(factors[,2]), ]\n",
"sample_annot <- sample_annot[!is.na(sample_annot[,1]) & !is.na(sample_annot[,2]), ]\n",
"\n",
"# Data to be plotted\n",
"df <- data.frame(x = factors[,1], y = factors[,2], color_by = sample_annot[,2])\n",
"# Plot results\n",
"p <- ggplot(df, aes_string(x = \"x\", y = \"y\")) + \n",
" geom_point(aes_string(color = \"color_by\"), size=dot_size, alpha=dot_alpha) + \n",
" xlab(xlabel) + ylab(ylabel) +\n",
" # scale_shape_manual(values=c(19,1,2:18)[seq_along(unique(shape_by))]) +\n",
" theme(plot.margin = margin(20, 20, 10, 10), \n",
" axis.text = element_text(size = rel(1), color = \"black\"), \n",
" axis.title = element_text(size = 16), \n",
" axis.title.y = element_text(size = rel(1.1), margin = margin(0, 10, 0, 0)), \n",
" axis.title.x = element_text(size = rel(1.1), margin = margin(10, 0, 0, 0)), \n",
" axis.line = element_line(color = \"black\", size = 0.5), \n",
" axis.ticks = element_line(color = \"black\", size = 0.5),\n",
" panel.border = element_blank(), \n",
" panel.grid.major = element_blank(),\n",
" panel.grid.minor = element_blank(), \n",
" panel.background = element_blank(),\n",
" legend.key = element_rect(fill = \"white\"),\n",
" legend.text = element_text(size = 16),\n",
" legend.title = element_text(size =16)\n",
" )\n",
"p + scale_color_manual(values=c(\"#0072B2\", \"#D55E00\", \"#CC79A7\"))\n",
"# Export plot as JPEG image\n",
"ggsave(paste0(results_folder, \"plot_\",\"AE_FAETC_EM\",\".jpg\"))\n",
"\n",
"# Encode cell type annotations by numeric codes\n",
"ann <- factor(sample_annot[,2], levels=c(\"HCT\", \"Hela\", \"K562\"))\n",
"ann <- as.integer(ann)\n",
"# print(ann)\n",
"# print(factors)\n",
"# Compare factors and annotations\n",
"c_index <- c(c_index, intCriteria(as.matrix(factors), as.integer(ann), crit=c(\"C_index\"))$c_index)\n",
"print(c_index)\n",
"# Build output table\n",
"report_cindex <- data.frame(method=\"AE_FAETC_EM\", cindex=c_index)\n",
"print(report_cindex)\n",
"# Export results as one tab-separated table\n",
"write.table(report_cindex, file = paste0(results_folder, \"singlecell_cindex_AE_FAETC_EM.txt\"), \n",
" sep=\"\\t\", col.names=FALSE, row.names=FALSE, quote=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" V1 V2 V3 V4 V5\n",
"1 4.61019325 5.16947794 -3.76821876 5.0370860 -9.9737577\n",
"2 -0.56126964 5.01752853 -2.33453012 -0.8252841 -0.4528220\n",
"3 0.88768262 3.87528944 0.96259248 -4.0607824 -3.9274344\n",
"4 5.92709112 1.79780912 -0.30577454 2.2509692 0.8909727\n",
"5 -1.59644318 6.16670990 -2.62944984 -0.8999423 -0.7932591\n",
"6 0.27649751 4.16627407 -1.68425679 -0.8543711 -0.7703041\n",
"7 0.68484432 4.02045870 1.12220144 -3.4713376 -3.9866064\n",
"8 0.99768144 3.69249868 1.16467333 -3.6697772 -3.9355514\n",
"9 5.85494089 1.22425675 -0.01612791 1.8666883 0.8808652\n",
"10 -0.14798826 3.76603556 -1.59501779 -1.0284584 -0.5151181\n",
"11 3.14144683 2.99328661 -1.17072058 4.5182977 -10.6349373\n",
"12 3.30559206 3.42356443 -1.64794075 5.1160250 -10.1963148\n",
"13 0.38587579 4.17554712 1.56078017 -3.4791327 -4.1053753\n",
"14 -0.92032236 4.71401119 -2.13506413 -0.9182727 -0.4636051\n",
"15 9.40060616 0.69357455 -0.17047414 1.8089135 0.9755358\n",
"16 -0.35624462 3.82682371 -1.66454113 -0.8294598 -0.6588628\n",
"17 -0.55448914 5.65170765 2.32820582 -3.2280169 -4.0773320\n",
"18 -0.93411207 4.50150967 -2.24100089 -0.9577712 -0.7784918\n",
"19 -0.23996632 3.90031385 -1.64314854 -0.8386132 -0.5130967\n",
"20 -1.58019722 4.30489111 -1.82027400 -1.0528810 -0.7484588\n",
"21 4.64695549 3.96978474 -2.29723001 4.7468719 -9.7389145\n",
"22 4.91350460 4.22474003 -3.63120866 4.7909088 -10.2846832\n",
"23 3.07361126 1.74038041 4.21311331 0.4057915 4.2063227\n",
"24 0.76685750 4.34563923 1.31918180 -3.4634247 -4.0954208\n",
"25 -1.49787414 4.61735439 -1.86207139 -0.7020981 -0.5041559\n",
"26 -3.07672000 5.72545004 -2.61962366 -0.8941500 -0.9547660\n",
"27 4.02696371 1.35963082 7.03316736 0.3536110 4.4382577\n",
"28 0.11135007 4.24966049 1.45237553 -3.6965048 -4.1085749\n",
"29 3.27869153 1.20380080 4.08153915 0.6160923 4.2363038\n",
"30 1.62331307 3.35657597 0.53396118 -3.5073805 -4.0560699\n",
"31 4.25569487 3.80481553 -1.62448108 5.0908995 -10.2618437\n",
"32 1.03016067 4.75595665 0.86410445 -3.7029278 -3.8656242\n",
"33 7.69167852 0.98434734 -0.63080961 2.0265944 0.8714269\n",
"34 6.43369436 0.79520291 -0.31615123 1.6022036 0.8230618\n",
"35 -1.82835257 5.21869755 -2.21961498 -0.7716627 -0.4900222\n",
"36 0.18795393 3.88317657 -1.66783941 -0.8342014 -0.4321339\n",
"37 3.97559929 4.07403803 -2.80033755 4.7228904 -10.3013926\n",
"38 0.49628589 5.08377171 1.69511247 -3.6714287 -3.8065395\n",
"39 -2.90156269 6.34198141 4.63835144 -3.1540008 -3.9567049\n",
"40 4.59686852 4.87682247 -3.52492857 4.7380457 -9.8736906\n",
"41 3.87523103 1.94860506 3.83276677 0.3835658 4.4791279\n",
"42 -0.14183180 4.91201401 -2.31560302 -1.0468341 -0.7586006\n",
"43 5.85339975 1.30641150 -0.14643577 2.2300785 0.8431688\n",
"44 4.75233603 1.83684552 -0.11228939 1.9723953 0.8771994\n",
"45 0.05996285 4.90521049 2.12214398 -3.4978185 -3.9116206\n",
"46 -2.30177450 5.80871582 -2.79990792 -0.7369043 -0.5436332\n",
"47 -0.06256990 4.45982265 -2.08125401 -0.7728009 -0.3607846\n",
"48 3.92012143 1.57586741 -0.11040732 1.7644562 0.7941970\n",
"49 3.69362259 3.82690454 -2.00322247 4.7290263 -10.1929922\n",
"50 9.95546150 1.45688558 -0.46983275 1.8836297 0.8249109\n",
"51 3.37534046 1.75160050 3.80170941 0.4569615 4.2907000\n",
"52 7.07446909 0.16435160 -0.42945620 1.7143475 0.8335933\n",
"53 3.09268522 1.69184291 3.21807265 0.3926568 4.0736237\n",
"54 8.93121147 -0.22839485 -0.78502703 2.1640770 0.9047865\n",
"55 2.78923798 1.66545939 2.93406296 0.4561912 4.4002657\n",
"56 4.44902182 3.91932082 -2.71486926 4.8933010 -10.1321783\n",
"57 -1.78252912 5.83993244 -2.55696058 -0.7308637 -0.6412294\n",
"58 4.01103258 4.70005083 -3.84118080 5.0498524 -10.4521914\n",
"59 3.50038838 1.22727168 -0.14593866 2.2660887 0.9586842\n",
"60 4.23297358 1.19393909 -0.01906171 1.9420310 1.0479312\n",
"61 4.80231237 5.00058317 -4.23484993 4.6340508 -10.4469280\n",
"62 5.49884558 4.83007050 -5.17076206 4.6497240 -10.3008432\n",
"63 0.69426090 4.62446022 1.31760585 -3.7844229 -3.9326704\n",
"64 2.36064768 1.36744308 4.74539804 0.5032264 4.5627704\n",
"65 5.52830648 1.61351907 -0.24780735 1.8562003 0.9256568\n",
"66 6.52302170 1.88503575 -0.05929699 1.5550133 0.8247068\n",
"67 0.20583586 4.99303293 1.68146074 -3.7723432 -4.0395679\n",
"68 -1.14772773 4.31767750 -1.82336748 -1.0127770 -0.5225742\n",
"69 3.82372260 1.87826943 4.40156364 0.2270881 4.3271856\n",
"70 3.37214184 1.89221096 3.20979190 0.4836840 4.2411957\n",
"71 -0.36502618 4.18340874 1.87735796 -3.7819529 -3.9059153\n",
"72 0.41091290 4.56604767 1.65844190 -3.6757288 -3.7940066\n",
"73 -0.33542007 4.84611177 -2.25845838 -0.9825857 -0.6960381\n",
"74 6.68160486 -0.04243729 -0.55758882 2.0515919 0.9847024\n",
"75 7.70558167 7.52751255 -7.46963787 4.9946632 -10.2783003\n",
"76 5.81390572 1.12594771 -0.49229753 1.8688875 1.0778660\n",
"77 0.47507727 4.53661728 1.18816793 -3.3350735 -3.8425920\n",
"78 8.19357300 0.37911156 -0.39462814 1.8106306 0.9016273\n",
"79 -1.65432167 8.12695980 4.49244213 -3.5704615 -4.0206242\n",
"80 5.12669611 1.38793659 -0.19704261 2.1227090 1.0978236\n",
"81 7.99937582 0.26207450 -0.51111758 1.6273253 1.0514908\n",
"82 3.98788166 4.29119110 -1.89645946 4.9635062 -9.9362259\n",
"83 -0.95230734 4.50490284 -2.15876579 -1.0727422 -0.7411513\n",
"84 -1.65143812 5.89050150 -2.65810919 -0.6817381 -0.6793380\n",
"85 4.56057882 4.85424280 -3.04470539 4.6409106 -10.7571163\n",
"86 3.31384087 1.81458998 4.73145294 0.3704108 4.2867470\n",
"87 -0.57873505 4.98048115 -2.45023322 -0.9973536 -0.4359966\n",
"88 -1.63461185 5.14105225 -2.12324238 -0.7231722 -0.5326511\n",
"89 3.35787344 3.61247063 -2.10336804 4.6703186 -10.1869402\n",
"90 -2.95196176 5.44401407 -2.24249363 -0.6051586 -0.5590327\n",
"91 6.56419706 6.36254978 -6.30168581 4.7761660 -10.2292757\n",
"92 4.07990551 3.93304801 -2.24391246 4.6954031 -10.1661415\n",
"93 -0.84112942 6.08504295 2.87457871 -4.0420899 -3.9406135\n",
"94 0.23189218 3.45792103 1.33238626 -3.3981438 -3.9597728\n",
"95 4.88539934 5.05270338 -3.84134531 4.8135595 -10.2329426\n",
"96 2.86414099 2.03157616 2.21716237 0.4389700 4.4542098\n",
"97 3.48676229 1.98423648 3.81995940 0.3689129 4.2556958\n",
"98 3.10526657 1.63525569 4.05113316 0.3168297 4.2726917\n",
"99 -3.24087572 6.78270817 -3.31386399 -0.9501026 -0.5089903\n",
"100 4.05248213 0.89221728 6.15222168 0.5082505 4.4578934\n",
" V1 \n",
"<NA> \n",
"Levels: 0 1 2 3 4\n"
]
}
],
"source": [
"library(\"clusterCrit\")\n",
"factorization=read.table(\"../data/simulations/equal/5/AE_FAETC_EM_5.txt\", sep=\" \", header=FALSE)\n",
"factors=factorization\n",
"#print(factorization)\n",
"ann=read.table(\"../data/simulations/equal/5/c.txt\", sep=\"\", header=FALSE)\n",
"ann <- factor(ann, levels=c(0,1,2,3,4))\n",
"\n",
"print(ann)\n",
"c_index <- numeric(0)\n",
"c_index <- c(c_index, intCriteria(as.matrix(factors),as.integer(ann), crit=c(\"C_index\"))$c_index)\n",
"print(c_index)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving 6.67 x 6.67 in image\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
"[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[186] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n"
]
},
{
"data": {
"image/png": 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XWgssnSrXPoLJZuraff3qjQLobBuIQQ2uX/Ye/6aSlrS5LOdBvsLndZSnzImmXY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2GBSsGKGaGLl+EaaTZErPrVWKiKtLKm7u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\nAACAJP4fs2jiid6OR8EAAAAASUVORK5CYII=",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 420,
"width": 420
},
"text/plain": {
"height": 420,
"width": 420
}
},
"output_type": "display_data"
}
],
"source": [
"factorization=read.table(\"../data/single-cell/AE_FCTAE_EM.txt\", sep=\" \", header=FALSE)\n",
"\n",
"library(\"ggplot2\")\n",
"library(\"clusterCrit\")\n",
"#source(\"runfactorization.R\")\n",
"\n",
"# Parameters for the plots\n",
"dot_size <- 1.5\n",
"dot_alpha <- 1.0\n",
"xlabel <- \"Factor 1\"\n",
"ylabel <- \"Factor 2\"\n",
"\n",
"# Load annotations from the metadata\n",
"sample_annot <- metadata[, c(\"sample.rna\", \"celltype\")]\n",
"\n",
"# Folder for results\n",
"results_folder <- \"../results_single_cell_AE_FCTAE_EM/\"\n",
"# Create output folder\n",
"dir.create(results_folder, showWarnings = FALSE)\n",
"\n",
"c_index <- numeric(0)\n",
"\n",
"# Get factorization result\n",
"factors <- factorization\n",
"\n",
"# Delete NAs\n",
"factors <- factors[!is.na(factors[,1]) & !is.na(factors[,2]), ]\n",
"sample_annot <- sample_annot[!is.na(sample_annot[,1]) & !is.na(sample_annot[,2]), ]\n",
"\n",
"# Data to be plotted\n",
"df <- data.frame(x = factors[,1], y = factors[,2], color_by = sample_annot[,2])\n",
"# Plot results\n",
"p <- ggplot(df, aes_string(x = \"x\", y = \"y\")) + \n",
" geom_point(aes_string(color = \"color_by\"), size=dot_size, alpha=dot_alpha) + \n",
" xlab(xlabel) + ylab(ylabel) +\n",
" # scale_shape_manual(values=c(19,1,2:18)[seq_along(unique(shape_by))]) +\n",
" theme(plot.margin = margin(20, 20, 10, 10), \n",
" axis.text = element_text(size = rel(1), color = \"black\"), \n",
" axis.title = element_text(size = 16), \n",
" axis.title.y = element_text(size = rel(1.1), margin = margin(0, 10, 0, 0)), \n",
" axis.title.x = element_text(size = rel(1.1), margin = margin(10, 0, 0, 0)), \n",
" axis.line = element_line(color = \"black\", size = 0.5), \n",
" axis.ticks = element_line(color = \"black\", size = 0.5),\n",
" panel.border = element_blank(), \n",
" panel.grid.major = element_blank(),\n",
" panel.grid.minor = element_blank(), \n",
" panel.background = element_blank(),\n",
" legend.key = element_rect(fill = \"white\"),\n",
" legend.text = element_text(size = 16),\n",
" legend.title = element_text(size =16)\n",
" )\n",
"p + scale_color_manual(values=c(\"#0072B2\", \"#D55E00\", \"#CC79A7\"))\n",
"# Export plot as JPEG image\n",
"ggsave(paste0(results_folder, \"plot_\",\"AE_FCTAE_EM\",\".jpg\"))\n",
"\n",
"# Encode cell type annotations by numeric codes\n",
"ann <- factor(sample_annot[,2], levels=c(\"HCT\", \"Hela\", \"K562\"))\n",
"ann <- as.integer(ann)\n",
"print(ann)\n",
"# Compare factors and annotations\n",
"c_index <- c(c_index, intCriteria(as.matrix(factors), as.integer(ann), crit=c(\"C_index\"))$c_index)\n",
"\n",
"# Build output table\n",
"report_cindex <- data.frame(method=\"AE_FCTAE_EM\", cindex=c_index)\n",
"\n",
"# Export results as one tab-separated table\n",
"write.table(report_cindex, file = paste0(results_folder, \"singlecell_cindex_AE_FCTAE_EM.txt\"), \n",
" sep=\"\\t\", col.names=FALSE, row.names=FALSE, quote=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving 6.67 x 6.67 in image\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
"[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[186] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n"
]
},
{
"data": {
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Ap4B5cnjg52oTcrALBAKOw6gsIOgCFaZMr3j+fsSSutapEO8LR/a1LY1Eh3upO6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80xsqLa6rNobWqviBXQUBXRTFZqXpCCGE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",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 420,
"width": 420
},
"text/plain": {
"height": 420,
"width": 420
}
},
"output_type": "display_data"
}
],
"source": [
"factorization=read.table(\"../data/single-cell/DAE_FCTAE_EM.txt\", sep=\" \", header=FALSE)\n",
"\n",
"library(\"ggplot2\")\n",
"library(\"clusterCrit\")\n",
"#source(\"runfactorization.R\")\n",
"\n",
"# Parameters for the plots\n",
"dot_size <- 1.5\n",
"dot_alpha <- 1.0\n",
"xlabel <- \"Factor 1\"\n",
"ylabel <- \"Factor 2\"\n",
"\n",
"# Load annotations from the metadata\n",
"sample_annot <- metadata[, c(\"sample.rna\", \"celltype\")]\n",
"\n",
"# Folder for results\n",
"results_folder <- \"../results_single_cell_DAE_FCTAE_EM/\"\n",
"# Create output folder\n",
"dir.create(results_folder, showWarnings = FALSE)\n",
"\n",
"c_index <- numeric(0)\n",
"\n",
"# Get factorization result\n",
"factors <- factorization\n",
"\n",
"# Delete NAs\n",
"factors <- factors[!is.na(factors[,1]) & !is.na(factors[,2]), ]\n",
"sample_annot <- sample_annot[!is.na(sample_annot[,1]) & !is.na(sample_annot[,2]), ]\n",
"\n",
"# Data to be plotted\n",
"df <- data.frame(x = factors[,1], y = factors[,2], color_by = sample_annot[,2])\n",
"# Plot results\n",
"p <- ggplot(df, aes_string(x = \"x\", y = \"y\")) + \n",
" geom_point(aes_string(color = \"color_by\"), size=dot_size, alpha=dot_alpha) + \n",
" xlab(xlabel) + ylab(ylabel) +\n",
" # scale_shape_manual(values=c(19,1,2:18)[seq_along(unique(shape_by))]) +\n",
" theme(plot.margin = margin(20, 20, 10, 10), \n",
" axis.text = element_text(size = rel(1), color = \"black\"), \n",
" axis.title = element_text(size = 16), \n",
" axis.title.y = element_text(size = rel(1.1), margin = margin(0, 10, 0, 0)), \n",
" axis.title.x = element_text(size = rel(1.1), margin = margin(10, 0, 0, 0)), \n",
" axis.line = element_line(color = \"black\", size = 0.5), \n",
" axis.ticks = element_line(color = \"black\", size = 0.5),\n",
" panel.border = element_blank(), \n",
" panel.grid.major = element_blank(),\n",
" panel.grid.minor = element_blank(), \n",
" panel.background = element_blank(),\n",
" legend.key = element_rect(fill = \"white\"),\n",
" legend.text = element_text(size = 16),\n",
" legend.title = element_text(size =16)\n",
" )\n",
"p + scale_color_manual(values=c(\"#0072B2\", \"#D55E00\", \"#CC79A7\"))\n",
"# Export plot as JPEG image\n",
"ggsave(paste0(results_folder, \"plot_\",\"DAE_FCTAE_EM\",\".jpg\"))\n",
"\n",
"# Encode cell type annotations by numeric codes\n",
"ann <- factor(sample_annot[,2], levels=c(\"HCT\", \"Hela\", \"K562\"))\n",
"ann <- as.integer(ann)\n",
"print(ann)\n",
"# Compare factors and annotations\n",
"c_index <- c(c_index, intCriteria(as.matrix(factors), as.integer(ann), crit=c(\"C_index\"))$c_index)\n",
"\n",
"# Build output table\n",
"report_cindex <- data.frame(method=\"DAE_FCTAE_EM\", cindex=c_index)\n",
"\n",
"# Export results as one tab-separated table\n",
"write.table(report_cindex, file = paste0(results_folder, \"singlecell_cindex_DAE_FCTAE_EM.txt\"), \n",
" sep=\"\\t\", col.names=FALSE, row.names=FALSE, quote=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving 6.67 x 6.67 in image\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
"[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[186] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n"
]
},
{
"data": {
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58vaHs5Mtj16DOD+VSb+T41OOCx/r5p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NPgF3B5fe2BVbhCihDiBPb1mfsjN8T0d+yWCRMm1+x/\nD5fXGwdFCVMMJ6cJE5+pObDKpIEw4Rm2d0+frt4TxPuILhTUPxoj+/iKbNRdUFBQc+9VzbfC\ndXNz039RWFgYERFho5S6Csrmus6bN6+kpGTdunVarTY1NTU1NdW0JwZj+fLlCxYsoKpHAABw\nLPGf6w1VHdKvD93zX05wAi9ymP2TcX38NZfk/2iq82gcIdPV34onMJy8/V75tfyHF3GZWB/h\nhg/2mfODWYOX8H+m9PEihvrO/anjyYPO793HI1LSK8RyjSHCYzHWPhHtwJQMDGN7LcwH6zmo\nXMT00UcfTZ06deHChfpFEsb69++/devWfv36UdgdAAA4ljRtu4XghZ8dUtghhDA6k+3bCyGE\nSEJTW4jLJWyfaBpH0PYnCOLGha/Pkd8/qZNWsv3j+L2STSbPCeLGhX+WI79/SietZAfE8aMe\ng9l1PYSviHPu1SFvHrl/Pr8OJ9HAQOfPJ8XEeLW+KMcOEhISuFyuUqm8ePHif/7zH0en42AU\nr06Pj4+/evVqTk7O5cuXq6qqSJL08vIaPHhwr169qO3Iamq1esWKFceOHZPL5aNHj964caNh\nCBcAANrF4lpUvMUFqnagLLxZuW2eqjQDIYSxuO4TV3g8/UHbyy8630U0YFpLDXjOoqRnKUgU\ndDW9vYWpCwfhBEmQZGfY5cSAxWKNHz/+4MGDe/fuXbNmTUhIiKMzciSbbDsUGRnZOXcIJAhi\n8uTJZ8+eXbJkiUgk+vrrr+/cuZOWlubk5OTo1AAAXQ/LK0JV+Dd6tGRiOXTCGS6rLd30lE76\ncAEjqVHWHlxN57u4jnndgVmB7oROw+io0w3TvvHGGwcPHtTpdKtXr965c6fFNlVVVd7e3nZO\nzP6oqbjz8/Pz8/P12xS3oKKiYuvWrX/88QclnVrh5MmTqampX3/99eeff75q1aoDBw5kZGTs\n2rXLUfkAALo0j8kfmvwDZ4sSSn7/VPH60Tmv+xR80L/u+AYSt7A40UCa9rOhqjMQ/7GW2pTs\nSV2RKU3b3nBlj1Zc7OhcQOc1fPjwmTNnIoR27do1e/Zs86MQfv7559DQ0B9/bNPWP10aNYVd\neHh4eHh4q2eFHThwYMGCBfpjeh1i+/btAoFg9uyHh2EPHz48Li5u+3YLs2QAAKBVwn6TfOdv\npwvd9Zds/7jAZX8y3ahc+N9480DxZ2PkD87oGqpUxber975V8dOcFtqbHxSLENI1VBOqJgqz\nshOSrPrl9fx3Yiq2zSvf8mLeyui6Y587OifQeX333XeJiYkIoV27dg0aNGjLli1Xr169devW\nvn37nnrqqblz5yqVyp5wWr1dT4DR7293//59e3Zq7Pr168OHD2ex/t3SadSoUZs3b1ar1Ww2\n21FZAQC6Lufhc5yHztLUFtBYPIaLH7UPJ3Fd1c5XTYINV/Y4j5zPjx5p8RaGk5d5kMYV0dh8\nanOzA8m5H+tPfm24JLWq6pTl7MA+gtixDswKdFoikejs2bMLFy787bff0tPTX3nlFeNPMQxb\nuHChA4eW7Maukx/FYjFCSH+gm0OYv1/XX1ZX/3s8tkqlkvwDx3F7pwgA6HJodJZXBOVVHUJI\nKy7UNdaYx5V5zZ7f6DTkJfMazmXkwq64dlV6fmsbgwDoCQSCX3/99erVq6+88kpUVJRQKOTz\n+VFRUYsWLbp+/fqWLVuMR3a6K/uN2BUWFn7wwQcIoZiYGLt1akwqlapUKldXV+OgfklsZWWl\nYdvkjRs3vvvuu4YGDAYcaw0AcAyMbvkfIYzZ7BsGlle478KdldsXGrYRFiU96/ns/9kkPxtT\nV+WYB3UN1eZB0G08//zzzz//fMttTp482XKDgQMHDhw4sO2djh49Wn+GQvdgZdWyY8eODRs2\nmAQnT57M4XDMG5Mk2dDQUFFRoT/r4/HHH7eu0w7CLP3Cqv9vafxRSEiIIcPr168rFAr7pAcA\nACaY7kFsv97qctPpKy2/ixQlTuX3GqXIOo8rJJyg/pzAPrbM0UqkTiPPPKMVF7M8w3jRIzG6\n6T9GysKbhLLR/EaWV7hdEgSgq7KysKurq7t3755JMC/P8vnQxgYNGvThhx9a12kHOTk5cblc\nw1HBevX19QghHx8fQ2T69OnTp0/Xf52UlJSenm7PJAEAwJjfwp1F60YaL33wfG4d2693y3fR\n+S7ChMlt70V267Ai5wJCGC9qhLDfJCtzbY2q7J7s1iEag8XyjqxOWaH5Z0CO7R8XsPQgy/Pf\n86OkF36u3rPM4kOch7W0dgQAYGVhN2rUqLVr19b/49ChQwihkJCQ5pYguLm5+fv7P/PMM1On\nTqXT6dbn2zHe3t6VlY9sBKC/9PKyMN0YAAAcjhOcEP5pVv2pb9Rl9xjOvk6DX+BFP0ZlBwRe\nunmyLP2o/qru+BfChMkBSw4gjMoZ2KROU/z5GEV2GrL0vktddrf8u+khH17Tdyq9uLNi69zm\nHsX0CKYwMQC6H4yS98pCobCpqenevXu9e7fye6RjzZw588iRI2Kx2FCAxsbGcjicmzdvWmyv\nH7HTalvaNQoAALquutQvq3970yTo/eJmanfjK/tuRuO1vS23CVl9gxuSiEgiZ6lvcxPpmG6B\nERuKuuJCEADshprfyZydnSl5jq3NnTu3qalp9+7d+suLFy/ev39/7txmfzUEAIDuTXbzgHmw\n8cZ+CrsgVLLGG/tabaZrqEII6WTiFpZH+Ly8Bao6AFpGzZLPo0ePqlSq0NBQSp5mO6NGjZo0\nadLrr79eXFwsEok2bNjQt2/fl156ydF5AQCAY2jrSs2D7drNWFV0S3L2B21dCdMj2GXUK5yA\neNMu6ssQQbT6HJLQIYRoHAGi0RFhutUUyyvcd952XtTwticGQM9ETWHXp0+bVl0RBEGjOfLY\nYAzD9u/fv2LFipSUFLlcPnbs2E2bNgmFQgemBAAAjtJ4fZ+23kJhxwns28YnNFz+pfyHf383\nlqZt91u0R5T0rHEbhsgTIQxZnF5npObXNwUxo2kcoaj/0403fzf+CGNyApYeZvs5Zqss0P0c\nOXLk7Nmzrq6uq1atcnQu1KOszDp06NC+fS0Ntufl5Y0cOfLXX3+lqkfrsFisjRs35uTklJeX\n79q1y2RbOwAA6DmqU5abB2ksnsfkNu1dgMvrKx89GIPUaSq3LzDZpoQucBP0Gd/q0zS1BbLb\nRxBC3rO/N67hMAbb+8XNFqo6klTmXZFe3Cm/d5LUqtqSMAB6Fy5c2LRpU3c9UJSaETuxWLxw\n4cLa2lqSJJvbWvDYsWMXLlzIzs4eP348lFMAAOBYhKpJKy42jwv6Pcl0D2rLExQ5lwiV6UlC\nuEKqyL9qstOe339+Kfp0lLrkzsNrjIZICy9ntfVlCCGGyDP04/TGG/vVJXfoQndh/8nme9fh\nstrSr59VZKfpL1keoX6L93JDktqSNujMUlJS9DuO5eXlhYWFWWwzZsyYU6dOjR8//vjx4/bN\nrmugZsRu/fr1tbW1fn5+48aNa67Na6+91q9fv5qami+++IKSTgEAAFiNxuJiDAsbVLE8LP9r\nagGhsxzHTeN0vmvYx+nB7130fG6tz+zvwj/PRTQL+16x3B4WlBid6TRohue0T90mvGVxR+KK\nbfMMVR1CSFNbUPbNc+ZVJgA9EDWF3dGjRxFC8+fPb2F5LI1GmzJzZkoOAAAgAElEQVRlCkLo\n2LFjlHQKAADAejS6aOA0kxjG5IgGPNfGB3BCkjA60/QJDBYnJNFie17kUPcn33EZ9QrLI9R5\n2GyTT1nekYK27Y2sk5TLbv9hEtSKi5vS/2xb4gB0Z9QUdsXFxQihxETLP8wGCQkJCKGCggJK\nOgUAANAR3jM3c4L6GS4xBtvr+c+MIy1juvqbz8bzfHYtQ+TZlq5Fg6YbLjlB/QJe/53G5rel\nX6200nK8wXIcgB6Fmjl2LBZLqVRWVVW13Ex/nBfRhnXvAAAAbI3OdwldfaPx74Oq4tt0vouw\n7ySWT1S7nuA+6V2WZ1j9me+1tQUsz3CXMa+JEqe20J7EtZrKLEKrZvv28n/lN+1z69QVmQxn\nX05AXNvPumC5B1mcpWd8KBkAPRY1hV1YWNitW7cuXbo0f/78Fppdu3YNIeTv709JpwAAADqK\nRhclPWuyQUk7YJho0HTjsbcWNGUcr9yxSFtXghCicZ28nlvrMvpVpntwe/ukCz1cHpsvOfej\ncZAT1E8Q1+wkb9BD6HS6zZs3//rrr9nZ2e7u7v3793/66adnzzZ979+y1NTUffv2Xbp0qays\nzMPDo1+/fosWLWphCUFnQ01h98QTT9y6dWv37t1z5swZMWKExTZ37tzZtm0bQig5OZmSTgEA\nAHQV6orMsm+eI9Ry/SWhbKjctZjh7CNMmGLF07xmbiQJnfTCDv24HT96pO/87RbXggAKkVpV\n3YnNigdnSELHixjqNuEtGlfk6KT+VVhYOHPmzCtXriCEaDRacXFxcXHxwYMHjx8/vm3bNj6/\n9Rf9Z86ceffdd/WDUHr6hxw5cmT9+vVvvfWWDbOnDjVz7N58800fHx8cx6dNm3bo0CHzBufO\nnZsyZYpKpeJyucuWLaOkUwAAAF1F/cmvDFWdgfiPddY9jcbi+c7bFvV1VfB7FyI2FAa9c5bp\nEdLhHEFLSJ268P+G1exb2XTvhPzBmdrDHxes6ocrpI7O66GmpqbHHnvsypUr0dHRqampjY2N\nEokkJSXFw8MjJSVl/fr1bXnIuXPnrl27Fhsbu2nTpkuXLtXU1Jw+fbpv374EQbz77rulpRZ2\n8+6EqBmxc3Z2/vbbb5955pnq6uopU6YMHTp05MiR0dHRdDo9Ozv78uXLJ0+e1Lf85JNPIiI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wAwAA4GDckCS//7S+i4Sq6O+KrXNVpRkI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\nCHYAAAAqQbADAABQCYIdAACASvx/hHou1VfM/7gAAAAASUVORK5CYII=",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 420,
"width": 420
},
"text/plain": {
"height": 420,
"width": 420
}
},
"output_type": "display_data"
}
],
"source": [
"factorization=read.table(\"../data/single-cell/VAE_FCTAE_EM.txt\", sep=\" \", header=FALSE)\n",
"\n",
"library(\"ggplot2\")\n",
"library(\"clusterCrit\")\n",
"#source(\"runfactorization.R\")\n",
"\n",
"# Parameters for the plots\n",
"dot_size <- 1.5\n",
"dot_alpha <- 1.0\n",
"xlabel <- \"Factor 1\"\n",
"ylabel <- \"Factor 2\"\n",
"\n",
"# Load annotations from the metadata\n",
"sample_annot <- metadata[, c(\"sample.rna\", \"celltype\")]\n",
"\n",
"# Folder for results\n",
"results_folder <- \"../results_single_cell_VAE_FCTAE_EM/\"\n",
"# Create output folder\n",
"dir.create(results_folder, showWarnings = FALSE)\n",
"\n",
"c_index <- numeric(0)\n",
"\n",
"# Get factorization result\n",
"factors <- factorization\n",
"\n",
"# Delete NAs\n",
"factors <- factors[!is.na(factors[,1]) & !is.na(factors[,2]), ]\n",
"sample_annot <- sample_annot[!is.na(sample_annot[,1]) & !is.na(sample_annot[,2]), ]\n",
"\n",
"# Data to be plotted\n",
"df <- data.frame(x = factors[,1], y = factors[,2], color_by = sample_annot[,2])\n",
"# Plot results\n",
"p <- ggplot(df, aes_string(x = \"x\", y = \"y\")) + \n",
" geom_point(aes_string(color = \"color_by\"), size=dot_size, alpha=dot_alpha) + \n",
" xlab(xlabel) + ylab(ylabel) +\n",
" # scale_shape_manual(values=c(19,1,2:18)[seq_along(unique(shape_by))]) +\n",
" theme(plot.margin = margin(20, 20, 10, 10), \n",
" axis.text = element_text(size = rel(1), color = \"black\"), \n",
" axis.title = element_text(size = 16), \n",
" axis.title.y = element_text(size = rel(1.1), margin = margin(0, 10, 0, 0)), \n",
" axis.title.x = element_text(size = rel(1.1), margin = margin(10, 0, 0, 0)), \n",
" axis.line = element_line(color = \"black\", size = 0.5), \n",
" axis.ticks = element_line(color = \"black\", size = 0.5),\n",
" panel.border = element_blank(), \n",
" panel.grid.major = element_blank(),\n",
" panel.grid.minor = element_blank(), \n",
" panel.background = element_blank(),\n",
" legend.key = element_rect(fill = \"white\"),\n",
" legend.text = element_text(size = 16),\n",
" legend.title = element_text(size =16)\n",
" )\n",
"p + scale_color_manual(values=c(\"#0072B2\", \"#D55E00\", \"#CC79A7\"))\n",
"# Export plot as JPEG image\n",
"ggsave(paste0(results_folder, \"plot_\",\"VAE_FCTAE_EM\",\".jpg\"))\n",
"\n",
"# Encode cell type annotations by numeric codes\n",
"ann <- factor(sample_annot[,2], levels=c(\"HCT\", \"Hela\", \"K562\"))\n",
"ann <- as.integer(ann)\n",
"print(ann)\n",
"# Compare factors and annotations\n",
"c_index <- c(c_index, intCriteria(as.matrix(factors), as.integer(ann), crit=c(\"C_index\"))$c_index)\n",
"\n",
"# Build output table\n",
"report_cindex <- data.frame(method=\"VAE_FCTAE_EM\", cindex=c_index)\n",
"\n",
"# Export results as one tab-separated table\n",
"write.table(report_cindex, file = paste0(results_folder, \"singlecell_cindex_VAE_FCTAE_EM.txt\"), \n",
" sep=\"\\t\", col.names=FALSE, row.names=FALSE, quote=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving 6.67 x 6.67 in image\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
"[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[186] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n"
]
},
{
"data": {
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qauAgAA2ZLtUqwOtVo9ffr0AQMGPP/8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/2RUX7VTgMGgfXVtAeRLuiGgY9tkN0+Yn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\nAADgCAQ7AAAAAI5AsAMAAADgiP8H9mP908fGFocAAAAASUVORK5CYII=",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 420,
"width": 420
},
"text/plain": {
"height": 420,
"width": 420
}
},
"output_type": "display_data"
}
],
"source": [
"factorization=read.table(\"../data/single-cell/DAE_FAETC_EM.txt\", sep=\" \", header=FALSE)\n",
"\n",
"library(\"ggplot2\")\n",
"library(\"clusterCrit\")\n",
"#source(\"runfactorization.R\")\n",
"\n",
"# Parameters for the plots\n",
"dot_size <- 1.5\n",
"dot_alpha <- 1.0\n",
"xlabel <- \"Factor 1\"\n",
"ylabel <- \"Factor 2\"\n",
"\n",
"# Load annotations from the metadata\n",
"sample_annot <- metadata[, c(\"sample.rna\", \"celltype\")]\n",
"\n",
"# Folder for results\n",
"results_folder <- \"../results_single_cell_DAE_FAETC_EM/\"\n",
"# Create output folder\n",
"dir.create(results_folder, showWarnings = FALSE)\n",
"\n",
"c_index <- numeric(0)\n",
"\n",
"# Get factorization result\n",
"factors <- factorization\n",
"\n",
"# Delete NAs\n",
"factors <- factors[!is.na(factors[,1]) & !is.na(factors[,2]), ]\n",
"sample_annot <- sample_annot[!is.na(sample_annot[,1]) & !is.na(sample_annot[,2]), ]\n",
"\n",
"# Data to be plotted\n",
"df <- data.frame(x = factors[,1], y = factors[,2], color_by = sample_annot[,2])\n",
"# Plot results\n",
"p <- ggplot(df, aes_string(x = \"x\", y = \"y\")) + \n",
" geom_point(aes_string(color = \"color_by\"), size=dot_size, alpha=dot_alpha) + \n",
" xlab(xlabel) + ylab(ylabel) +\n",
" # scale_shape_manual(values=c(19,1,2:18)[seq_along(unique(shape_by))]) +\n",
" theme(plot.margin = margin(20, 20, 10, 10), \n",
" axis.text = element_text(size = rel(1), color = \"black\"), \n",
" axis.title = element_text(size = 16), \n",
" axis.title.y = element_text(size = rel(1.1), margin = margin(0, 10, 0, 0)), \n",
" axis.title.x = element_text(size = rel(1.1), margin = margin(10, 0, 0, 0)), \n",
" axis.line = element_line(color = \"black\", size = 0.5), \n",
" axis.ticks = element_line(color = \"black\", size = 0.5),\n",
" panel.border = element_blank(), \n",
" panel.grid.major = element_blank(),\n",
" panel.grid.minor = element_blank(), \n",
" panel.background = element_blank(),\n",
" legend.key = element_rect(fill = \"white\"),\n",
" legend.text = element_text(size = 16),\n",
" legend.title = element_text(size =16)\n",
" )\n",
"p + scale_color_manual(values=c(\"#0072B2\", \"#D55E00\", \"#CC79A7\"))\n",
"# Export plot as JPEG image\n",
"ggsave(paste0(results_folder, \"plot_\",\"DAE_FAETC_EM\",\".jpg\"))\n",
"\n",
"# Encode cell type annotations by numeric codes\n",
"ann <- factor(sample_annot[,2], levels=c(\"HCT\", \"Hela\", \"K562\"))\n",
"ann <- as.integer(ann)\n",
"print(ann)\n",
"# Compare factors and annotations\n",
"c_index <- c(c_index, intCriteria(as.matrix(factors), as.integer(ann), crit=c(\"C_index\"))$c_index)\n",
"\n",
"# Build output table\n",
"report_cindex <- data.frame(method=\"DAE_FAETC_EM\", cindex=c_index)\n",
"\n",
"# Export results as one tab-separated table\n",
"write.table(report_cindex, file = paste0(results_folder, \"singlecell_cindex_DAE_FAETC_EM.txt\"), \n",
" sep=\"\\t\", col.names=FALSE, row.names=FALSE, quote=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving 6.67 x 6.67 in image\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
"[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[186] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n"
]
},
{
"data": {
"image/png": 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bADAACQCYIdgKpHU6jOS87UFKpNXQgA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\nAAAAAElFTkSuQmCC",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 420,
"width": 420
},
"text/plain": {
"height": 420,
"width": 420
}
},
"output_type": "display_data"
}
],
"source": [
"factorization=read.table(\"../data/single-cell/LSTMVAE_FCTAE_EM.txt\", sep=\" \", header=FALSE)\n",
"\n",
"library(\"ggplot2\")\n",
"library(\"clusterCrit\")\n",
"#source(\"runfactorization.R\")\n",
"\n",
"# Parameters for the plots\n",
"dot_size <- 1.5\n",
"dot_alpha <- 1.0\n",
"xlabel <- \"Factor 1\"\n",
"ylabel <- \"Factor 2\"\n",
"\n",
"# Load annotations from the metadata\n",
"sample_annot <- metadata[, c(\"sample.rna\", \"celltype\")]\n",
"\n",
"# Folder for results\n",
"results_folder <- \"../results_single_cell_LSTMVAE_FCTAE_EM/\"\n",
"# Create output folder\n",
"dir.create(results_folder, showWarnings = FALSE)\n",
"\n",
"c_index <- numeric(0)\n",
"\n",
"# Get factorization result\n",
"factors <- factorization\n",
"\n",
"# Delete NAs\n",
"factors <- factors[!is.na(factors[,1]) & !is.na(factors[,2]), ]\n",
"sample_annot <- sample_annot[!is.na(sample_annot[,1]) & !is.na(sample_annot[,2]), ]\n",
"\n",
"# Data to be plotted\n",
"df <- data.frame(x = factors[,1], y = factors[,2], color_by = sample_annot[,2])\n",
"# Plot results\n",
"p <- ggplot(df, aes_string(x = \"x\", y = \"y\")) + \n",
" geom_point(aes_string(color = \"color_by\"), size=dot_size, alpha=dot_alpha) + \n",
" xlab(xlabel) + ylab(ylabel) +\n",
" # scale_shape_manual(values=c(19,1,2:18)[seq_along(unique(shape_by))]) +\n",
" theme(plot.margin = margin(20, 20, 10, 10), \n",
" axis.text = element_text(size = rel(1), color = \"black\"), \n",
" axis.title = element_text(size = 16), \n",
" axis.title.y = element_text(size = rel(1.1), margin = margin(0, 10, 0, 0)), \n",
" axis.title.x = element_text(size = rel(1.1), margin = margin(10, 0, 0, 0)), \n",
" axis.line = element_line(color = \"black\", size = 0.5), \n",
" axis.ticks = element_line(color = \"black\", size = 0.5),\n",
" panel.border = element_blank(), \n",
" panel.grid.major = element_blank(),\n",
" panel.grid.minor = element_blank(), \n",
" panel.background = element_blank(),\n",
" legend.key = element_rect(fill = \"white\"),\n",
" legend.text = element_text(size = 16),\n",
" legend.title = element_text(size =16)\n",
" )\n",
"p + scale_color_manual(values=c(\"#0072B2\", \"#D55E00\", \"#CC79A7\"))\n",
"# Export plot as JPEG image\n",
"ggsave(paste0(results_folder, \"plot_\",\"LSTMVAE_FCTAE_EM\",\".jpg\"))\n",
"\n",
"# Encode cell type annotations by numeric codes\n",
"ann <- factor(sample_annot[,2], levels=c(\"HCT\", \"Hela\", \"K562\"))\n",
"ann <- as.integer(ann)\n",
"print(ann)\n",
"# Compare factors and annotations\n",
"c_index <- c(c_index, intCriteria(as.matrix(factors), as.integer(ann), crit=c(\"C_index\"))$c_index)\n",
"\n",
"# Build output table\n",
"report_cindex <- data.frame(method=\"LSTMVAE_FCTAE_EM\", cindex=c_index)\n",
"\n",
"# Export results as one tab-separated table\n",
"write.table(report_cindex, file = paste0(results_folder, \"singlecell_cindex_LSTMVAE_FCTAE_EM.txt\"), \n",
" sep=\"\\t\", col.names=FALSE, row.names=FALSE, quote=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving 6.67 x 6.67 in image\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
"[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[186] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n"
]
},
{
"data": {
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xQAmIc5dgAQMJuLW7yLm4qaCSF2g8VQ1WLTmYPeVIDJcxI4PM9sJ0uJ5ss8Jw4C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MRo8eDQBISkpq5nVdC56bbdeuXRsfHx8S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\nAAAASUVORK5CYII=",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 420,
"width": 420
},
"text/plain": {
"height": 420,
"width": 420
}
},
"output_type": "display_data"
}
],
"source": [
"factorization=read.table(\"../data/single-cell/SVAE_FCTAE_EM.txt\", sep=\" \", header=FALSE)\n",
"\n",
"library(\"ggplot2\")\n",
"library(\"clusterCrit\")\n",
"#source(\"runfactorization.R\")\n",
"\n",
"# Parameters for the plots\n",
"dot_size <- 1.5\n",
"dot_alpha <- 1.0\n",
"xlabel <- \"Factor 1\"\n",
"ylabel <- \"Factor 2\"\n",
"\n",
"# Load annotations from the metadata\n",
"sample_annot <- metadata[, c(\"sample.rna\", \"celltype\")]\n",
"\n",
"# Folder for results\n",
"results_folder <- \"../results_single_cell_SVAE_FCTAE_EM/\"\n",
"# Create output folder\n",
"dir.create(results_folder, showWarnings = FALSE)\n",
"\n",
"c_index <- numeric(0)\n",
"\n",
"# Get factorization result\n",
"factors <- factorization\n",
"\n",
"# Delete NAs\n",
"factors <- factors[!is.na(factors[,1]) & !is.na(factors[,2]), ]\n",
"sample_annot <- sample_annot[!is.na(sample_annot[,1]) & !is.na(sample_annot[,2]), ]\n",
"\n",
"# Data to be plotted\n",
"df <- data.frame(x = factors[,1], y = factors[,2], color_by = sample_annot[,2])\n",
"# Plot results\n",
"p <- ggplot(df, aes_string(x = \"x\", y = \"y\")) + \n",
" geom_point(aes_string(color = \"color_by\"), size=dot_size, alpha=dot_alpha) + \n",
" xlab(xlabel) + ylab(ylabel) +\n",
" # scale_shape_manual(values=c(19,1,2:18)[seq_along(unique(shape_by))]) +\n",
" theme(plot.margin = margin(20, 20, 10, 10), \n",
" axis.text = element_text(size = rel(1), color = \"black\"), \n",
" axis.title = element_text(size = 16), \n",
" axis.title.y = element_text(size = rel(1.1), margin = margin(0, 10, 0, 0)), \n",
" axis.title.x = element_text(size = rel(1.1), margin = margin(10, 0, 0, 0)), \n",
" axis.line = element_line(color = \"black\", size = 0.5), \n",
" axis.ticks = element_line(color = \"black\", size = 0.5),\n",
" panel.border = element_blank(), \n",
" panel.grid.major = element_blank(),\n",
" panel.grid.minor = element_blank(), \n",
" panel.background = element_blank(),\n",
" legend.key = element_rect(fill = \"white\"),\n",
" legend.text = element_text(size = 16),\n",
" legend.title = element_text(size =16)\n",
" )\n",
"p + scale_color_manual(values=c(\"#0072B2\", \"#D55E00\", \"#CC79A7\"))\n",
"# Export plot as JPEG image\n",
"ggsave(paste0(results_folder, \"plot_\",\"SVAE_FCTAE_EM\",\".jpg\"))\n",
"\n",
"# Encode cell type annotations by numeric codes\n",
"ann <- factor(sample_annot[,2], levels=c(\"HCT\", \"Hela\", \"K562\"))\n",
"ann <- as.integer(ann)\n",
"print(ann)\n",
"# Compare factors and annotations\n",
"c_index <- c(c_index, intCriteria(as.matrix(factors), as.integer(ann), crit=c(\"C_index\"))$c_index)\n",
"\n",
"# Build output table\n",
"report_cindex <- data.frame(method=\"SVAE_FCTAE_EM\", cindex=c_index)\n",
"\n",
"# Export results as one tab-separated table\n",
"write.table(report_cindex, file = paste0(results_folder, \"singlecell_cindex_SVAE_FCTAE_EM.txt\"), \n",
" sep=\"\\t\", col.names=FALSE, row.names=FALSE, quote=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving 6.67 x 6.67 in image\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
"[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
"[186] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n"
]
},
{
"data": {
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jkrzr0CQFxhKhZgGrD3GIOpLsAzau/6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\nIbW1tW1tbbrG4uLitLQ0QsiwYcO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"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 420,
"width": 420
},
"text/plain": {
"height": 420,
"width": 420
}
},
"output_type": "display_data"
}
],
"source": [
"factorization=read.table(\"../data/single-cell/MMDVAE_EM.txt\", sep=\" \", header=FALSE)\n",
"\n",
"library(\"ggplot2\")\n",
"library(\"clusterCrit\")\n",
"#source(\"runfactorization.R\")\n",
"\n",
"# Parameters for the plots\n",
"dot_size <- 1.5\n",
"dot_alpha <- 1.0\n",
"xlabel <- \"Factor 1\"\n",
"ylabel <- \"Factor 2\"\n",
"\n",
"# Load annotations from the metadata\n",
"sample_annot <- metadata[, c(\"sample.rna\", \"celltype\")]\n",
"\n",
"# Folder for results\n",
"results_folder <- \"../results_single_cell_MMDVAE_EM/\"\n",
"# Create output folder\n",
"dir.create(results_folder, showWarnings = FALSE)\n",
"\n",
"c_index <- numeric(0)\n",
"\n",
"# Get factorization result\n",
"factors <- factorization\n",
"\n",
"# Delete NAs\n",
"factors <- factors[!is.na(factors[,1]) & !is.na(factors[,2]), ]\n",
"sample_annot <- sample_annot[!is.na(sample_annot[,1]) & !is.na(sample_annot[,2]), ]\n",
"\n",
"# Data to be plotted\n",
"df <- data.frame(x = factors[,1], y = factors[,2], color_by = sample_annot[,2])\n",
"# Plot results\n",
"p <- ggplot(df, aes_string(x = \"x\", y = \"y\")) + \n",
" geom_point(aes_string(color = \"color_by\"), size=dot_size, alpha=dot_alpha) + \n",
" xlab(xlabel) + ylab(ylabel) +\n",
" # scale_shape_manual(values=c(19,1,2:18)[seq_along(unique(shape_by))]) +\n",
" theme(plot.margin = margin(20, 20, 10, 10), \n",
" axis.text = element_text(size = rel(1), color = \"black\"), \n",
" axis.title = element_text(size = 16), \n",
" axis.title.y = element_text(size = rel(1.1), margin = margin(0, 10, 0, 0)), \n",
" axis.title.x = element_text(size = rel(1.1), margin = margin(10, 0, 0, 0)), \n",
" axis.line = element_line(color = \"black\", size = 0.5), \n",
" axis.ticks = element_line(color = \"black\", size = 0.5),\n",
" panel.border = element_blank(), \n",
" panel.grid.major = element_blank(),\n",
" panel.grid.minor = element_blank(), \n",
" panel.background = element_blank(),\n",
" legend.key = element_rect(fill = \"white\"),\n",
" legend.text = element_text(size = 16),\n",
" legend.title = element_text(size =16)\n",
" )\n",
"p + scale_color_manual(values=c(\"#0072B2\", \"#D55E00\", \"#CC79A7\"))\n",
"# Export plot as JPEG image\n",
"ggsave(paste0(results_folder, \"plot_\",\"MMDVAE_EM\",\".jpg\"))\n",
"\n",
"# Encode cell type annotations by numeric codes\n",
"ann <- factor(sample_annot[,2], levels=c(\"HCT\", \"Hela\", \"K562\"))\n",
"ann <- as.integer(ann)\n",
"print(ann)\n",
"# Compare factors and annotations\n",
"c_index <- c(c_index, intCriteria(as.matrix(factors), as.integer(ann), crit=c(\"C_index\"))$c_index)\n",
"\n",
"# Build output table\n",
"report_cindex <- data.frame(method=\"MMDVAE_EM\", cindex=c_index)\n",
"\n",
"# Export results as one tab-separated table\n",
"write.table(report_cindex, file = paste0(results_folder, \"singlecell_cindex_MMDVAE_EM.txt\"), \n",
" sep=\"\\t\", col.names=FALSE, row.names=FALSE, quote=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Datasets
Models
