changeset 0:75faf9a89f5b draft

planemo upload commit a0e968c7bd2b6f7b963baeecb08f3a39e50f52d6
author galaxyp
date Fri, 14 Sep 2018 12:22:31 -0400
parents
children bcc7a4c4cc29
files quantp.r quantp.xml test-data/exp_design_file.tabular test-data/output.html test-data/protein_data.tabular test-data/transcript_data.tabular
diffstat 6 files changed, 6996 insertions(+), 0 deletions(-) [+]
line wrap: on
line diff
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/quantp.r	Fri Sep 14 12:22:31 2018 -0400
@@ -0,0 +1,1004 @@
+#***************************************************************************************************************************************
+# Functions: Start
+#***************************************************************************************************************************************
+
+#===============================================================================
+# PCA
+#===============================================================================
+multisample_PCA = function(df, sampleinfo_df, outfile)
+{
+  tempdf = df[,-1];
+  tempcol = colnames(tempdf);
+  tempgrp = sampleinfo_df[tempcol,2];
+  tempdf = t(tempdf) %>% as.data.frame();
+  tempdf[is.na(tempdf)] = 0;
+  tempdf$Group = tempgrp;
+  png(outfile, width = 6, height = 6, units = 'in', res=300);
+  # bitmap(outfile, "png16m");
+  g = autoplot(prcomp(select(tempdf, -Group)), data = tempdf, colour = 'Group', size=3);
+  plot(g);
+  dev.off();
+}
+
+#===============================================================================
+# Regression and Cook's distance
+#===============================================================================
+singlesample_regression = function(PE_TE_data,htmloutfile, append=TRUE)
+{
+  rownames(PE_TE_data) = PE_TE_data$PE_ID;
+  regmodel = lm(PE_abundance~TE_abundance, data=PE_TE_data);
+  regmodel_summary = summary(regmodel);
+  
+  cat("<font><h3>Linear Regression model fit between Proteome and Transcriptome data</h3></font>\n",
+    "<p>Assuming a linear relationship between Proteome and Transcriptome data, we here fit a linear regression model.</p>\n",
+    '<table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  cat("<tr><td>Formula</td><td>","PE_abundance~TE_abundance","</td></tr>\n",
+    "<tr><td colspan='2' align='center'> <b>Coefficients</b></td>","</tr>\n",
+    "<tr><td>",names(regmodel$coefficients[1]),"</td><td>",regmodel$coefficients[1]," (Pvalue:", regmodel_summary$coefficients[1,4],")","</td></tr>\n",
+    "<tr><td>",names(regmodel$coefficients[2]),"</td><td>",regmodel$coefficients[2]," (Pvalue:", regmodel_summary$coefficients[2,4],")","</td></tr>\n",
+    "<tr><td colspan='2' align='center'> <b>Model parameters</b></td>","</tr>\n",
+    "<tr><td>Residual standard error</td><td>",regmodel_summary$sigma," (",regmodel_summary$df[2]," degree of freedom)</td></tr>\n",
+    "<tr><td>F-statistic</td><td>",regmodel_summary$fstatistic[1]," ( on ",regmodel_summary$fstatistic[2]," and  ",regmodel_summary$fstatistic[3]," degree of freedom)</td></tr>\n",
+    "<tr><td>R-squared</td><td>",regmodel_summary$r.squared,"</td></tr>\n",
+    "<tr><td>Adjusted R-squared</td><td>",regmodel_summary$adj.r.squared,"</td></tr>\n",
+    file = htmloutfile, append = TRUE);
+  
+  cat("</table>\n", file = htmloutfile, append = TRUE);
+  
+  cat(
+    "<font color='#ff0000'><h3>Regression and diagnostics plots</h3></font>\n",
+    file = htmloutfile, append = TRUE);
+  
+  outplot = paste(outdir,"/PE_TE_lm_1.png",sep="",collapse="");
+  png(outplot, width = 10, height = 10, units = 'in',res=300);
+  # bitmap(outplot, "png16m");
+  par(mfrow=c(1,1));
+  plot(regmodel, 1, cex.lab=1.5);
+  dev.off();
+  
+  outplot = paste(outdir,"/PE_TE_lm_2.png",sep="",collapse="");
+  png(outplot,width = 10, height = 10, units = 'in', res=300);
+  # bitmap(outplot, "png16m");
+  par(mfrow=c(1,1));
+  plot(regmodel, 2, cex.lab=1.5);
+  dev.off();
+  
+  outplot = paste(outdir,"/PE_TE_lm_5.png",sep="",collapse="");
+  png(outplot, width = 10, height = 10, units = 'in',res=300);
+  # bitmap(outplot, "png16m");
+  par(mfrow=c(1,1));
+  plot(regmodel, 5, cex.lab=1.5);
+  dev.off();
+  
+  cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">', file = htmloutfile, append = TRUE);
+  
+    cat(
+    '<tr bgcolor="#7a0019"><th>', "<font color='#ffcc33'><h4>1) <u>Residuals vs Fitted plot</h4></font></u></th>\n",
+    '<th><font color=#ffcc33><h4>2) <u>Normal Q-Q plot of residuals</h4></font></u></th></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  cat(
+    '<tr><td align=center><img src="PE_TE_lm_1.png" width=600 height=600></td><td align=center><img src="PE_TE_lm_2.png" width=600 height=600></td></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  cat(
+    '<tr><td align=center>This plot checks for linear relationship assumptions.<br>If a horizontal line is observed without any distinct patterns, it indicates a linear relationship.</td>\n',
+    '<td align=center>This plot checks whether residuals are normally distributed or not.<br>It is good if the residuals points follow the straight dashed line i.e., do not deviate much from dashed line.</td></tr></table>\n',
+    file = htmloutfile, append = TRUE);
+    
+    
+    #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    # Residuals data
+    #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    res_all = regmodel$residuals;
+    res_mean = mean(res_all);
+    res_sd = sd(res_all);
+    res_diff = (res_all-res_mean);
+    res_zscore = res_diff/res_sd;
+    # res_outliers = res_all[which((res_zscore > 2)|(res_zscore < -2))]
+    
+    
+    tempind = which((res_zscore > 2)|(res_zscore < -2));
+    res_PE_TE_data_no_outlier = PE_TE_data[-tempind,];
+    res_PE_TE_data_no_outlier$residuals = res_all[-tempind];
+    res_PE_TE_data_outlier = PE_TE_data[tempind,];
+    res_PE_TE_data_outlier$residuals = res_all[tempind];
+  
+  # Save the complete table for download (influential_observations)
+    temp_outlier_data = data.frame(res_PE_TE_data_outlier$PE_ID, res_PE_TE_data_outlier$TE_abundance, res_PE_TE_data_outlier$PE_abundance, res_PE_TE_data_outlier$residuals)
+    colnames(temp_outlier_data) = c("Gene", "Transcript abundance", "Protein abundance", "Residual value")
+    outdatafile = paste(outdir,"/PE_TE_outliers_residuals.txt", sep="", collapse="");
+    write.table(temp_outlier_data, file=outdatafile, row.names=F, sep="\t", quote=F);
+  
+  
+  # Save the complete table for download (non influential_observations)
+    temp_all_data = data.frame(PE_TE_data$PE_ID, PE_TE_data$TE_abundance, PE_TE_data$PE_abundance, res_all)
+    colnames(temp_all_data) = c("Gene", "Transcript abundance", "Protein abundance", "Residual value")
+    outdatafile = paste(outdir,"/PE_TE_abundance_residuals.txt", sep="", collapse="");
+    write.table(temp_all_data, file=outdatafile, row.names=F, sep="\t", quote=F);
+  
+  
+  cat('<br><h2 id="inf_obs"><font color=#ff0000>Outliers based on the residuals from regression analysis</font></h2>\n',
+    file = htmloutfile, append = TRUE);
+  cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">\n',
+  '<tr bgcolor="#7a0019"><th colspan=2><font color=#ffcc33>Residuals from Regression</font></th></tr>\n',
+   '<tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr>\n',
+    file = htmloutfile, append = TRUE);
+    
+  cat("<tr><td>Mean Residual value</td><td>",res_mean,"</td></tr>\n",
+    "<tr><td>Standard deviation (Residuals)</td><td>",res_sd,"</td></tr>\n",
+    '<tr><td>Total outliers (Residual value > 2 standard deviation from the mean)</td><td>',length(tempind),' <font size=4>(<b><a href=PE_TE_outliers_residuals.txt target="_blank">Download these ',length(tempind),' data points with high residual values here</a></b>)</font></td>\n',
+    '<tr><td colspan=2 align=center><font size=4>(<b><a href=PE_TE_abundance_residuals.txt target="_blank">Download the complete residuals data here</a></b>)</font></td></td>\n',
+    "</table><br><br>\n",
+    file = htmloutfile, append = TRUE);
+    
+  #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    
+    
+  cat('<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">', file = htmloutfile, append = TRUE);
+
+  cat(
+    '<tr bgcolor="#7a0019"><th><font color=#ffcc33><h4>3) <u>Residuals vs Leverage plot</h4></font></u></th></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  cat(
+    '<tr><td align=center><img src="PE_TE_lm_5.png" width=600 height=600></td></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  cat(
+    '<tr><td align=center>This plot is useful to identify any influential cases, that is outliers or extreme values.<br>They might influence the regression results upon inclusion or exclusion from the analysis.</td></tr></table><br>\n',
+    file = htmloutfile, append = TRUE);
+  
+  
+  
+  #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  # Cook's Distance
+  #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  cat('<hr/><h2 id="inf_obs"><font color=#ff0000>INFLUENTIAL OBSERVATIONS</font></h2>\n',
+    file = htmloutfile, append = TRUE);
+  cat(
+    '<p><b>Cook\'s distance</b> computes the influence of each data point/observation on the predicted outcome. i.e. this measures how much the observation is influencing the fitted values.<br>In general use, those observations that have a <b>Cook\'s distance > than ', cookdist_upper_cutoff,' times the mean</b> may be classified as <b>influential.</b></p>\n',
+    file = htmloutfile, append = TRUE);
+  
+  cooksd <- cooks.distance(regmodel);
+  
+  outplot = paste(outdir,"/PE_TE_lm_cooksd.png",sep="",collapse="");
+  png(outplot, width = 10, height = 10, units = 'in', res=300);
+  # bitmap(outplot, "png16m");
+  par(mfrow=c(1,1));
+  plot(cooksd, main="Influential Obs. by Cook\'s distance", ylab="Cook\'s distance", xlab="Observations", type="n")  # plot cooks distance
+  sel_outlier=which(cooksd>=as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T))
+  sel_nonoutlier=which(cooksd<as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T))
+  points(sel_outlier, cooksd[sel_outlier],pch="*", cex=2, cex.lab=1.5, col="red")
+  points(sel_nonoutlier, cooksd[sel_nonoutlier],pch="*", cex=2, cex.lab=1.5, col="black")
+  abline(h = as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T), col="red")  # add cutoff line
+  #text(x=1:length(cooksd)+1, y=cooksd, labels=ifelse(cooksd>as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T),names(cooksd),""), col="red", pos=2)  # add labels
+  dev.off();
+  
+  cat(
+    '<img src="PE_TE_lm_cooksd.png" width=800 height=800>',
+    '<br>In the above plot, observations above red line (',cookdist_upper_cutoff,' * mean Cook\'s distance) are influential. Genes that are outliers could be important. These observations influences the correlation values and regression coefficients<br><br>',
+    file = htmloutfile, append = TRUE);    
+
+  tempind = which(cooksd>as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T));
+  PE_TE_data_no_outlier = PE_TE_data[-tempind,];
+  PE_TE_data_no_outlier$cooksd = cooksd[-tempind];
+  PE_TE_data_outlier = PE_TE_data[tempind,];
+  PE_TE_data_outlier$cooksd = cooksd[tempind];
+  a = sort(PE_TE_data_outlier$cooksd, decreasing=T, index.return=T);
+  PE_TE_data_outlier_sorted = PE_TE_data_outlier[a$ix,];
+  
+  cat(
+    '<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  # Save the complete table for download (influential_observations)
+    temp_outlier_data = data.frame(PE_TE_data_outlier$PE_ID, PE_TE_data_outlier$TE_abundance, PE_TE_data_outlier$PE_abundance, PE_TE_data_outlier$cooksd)
+    colnames(temp_outlier_data) = c("Gene", "Transcript abundance", "Protein abundance", "Cook's distance")
+    outdatafile = paste(outdir,"/PE_TE_influential_observation.txt", sep="", collapse="");
+    write.table(temp_outlier_data, file=outdatafile, row.names=F, sep="\t", quote=F);
+  
+  
+  # Save the complete table for download (non influential_observations)
+    temp_no_outlier_data = data.frame(PE_TE_data_no_outlier$PE_ID, PE_TE_data_no_outlier$TE_abundance, PE_TE_data_no_outlier$PE_abundance, PE_TE_data_no_outlier$cooksd)
+    colnames(temp_no_outlier_data) = c("Gene", "Transcript abundance", "Protein abundance", "Cook's distance")
+    outdatafile = paste(outdir,"/PE_TE_non_influential_observation.txt", sep="", collapse="");
+    write.table(temp_no_outlier_data, file=outdatafile, row.names=F, sep="\t", quote=F);
+  
+  
+  cat("<tr><td>Mean Cook\'s distance</td><td>",mean(cooksd, na.rm=T),"</td></tr>\n",
+    "<tr><td>Total influential observations (Cook\'s distance > ",cookdist_upper_cutoff," * mean Cook\'s distance)</td><td>",length(tempind),"</td>\n",
+    
+    "<tr><td>Observations with Cook\'s distance < ",cookdist_upper_cutoff," * mean Cook\'s distance</td><td>",length(which(cooksd<as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T))),"</td>\n",
+    "</table><br><br>\n",
+    file = htmloutfile, append = TRUE);
+    
+    
+    #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    # Scatter plot after removal of influential points
+    #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    outplot = paste(outdir,"/AbundancePlot_scatter_without_outliers.png",sep="",collapse="");
+    min_lim = min(c(PE_TE_data$PE_abundance,PE_TE_data$TE_abundance));
+    max_lim = max(c(PE_TE_data$PE_abundance,PE_TE_data$TE_abundance));
+    png(outplot, width = 10, height = 10, units = 'in', res=300);
+    # bitmap(outplot,"png16m");
+    g = ggplot(PE_TE_data_no_outlier, aes(x=TE_abundance, y=PE_abundance))+geom_point() + geom_smooth() + xlab("Transcript abundance log fold-change") + ylab("Protein abundance log fold-change") + xlim(min_lim,max_lim) + ylim(min_lim,max_lim);
+    suppressMessages(plot(g));
+    dev.off();
+    
+    cat('<table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatterplot: Before removal</font></th><th><font color=#ffcc33>Scatterplot: After removal</font></th></tr>\n', file = htmloutfile, append = TRUE);
+    # Before
+    cat("<tr><td align=center><!--<font color='#ff0000'><h3>Scatter plot between Proteome and Transcriptome Abundance</h3></font>\n-->", '<img src="TE_PE_scatter.png" width=600 height=600></td>\n', file = htmloutfile, append = TRUE);
+    
+    # After
+    cat("<td align=center>\n",
+    '<img src="AbundancePlot_scatter_without_outliers.png" width=600 height=600></td></tr>\n',
+    file = htmloutfile, append = TRUE);
+    #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    
+  
+  cor_result_pearson = cor.test(PE_TE_data_no_outlier[,"TE_abundance"], PE_TE_data_no_outlier[,"PE_abundance"], method = "pearson");
+  cor_result_spearman = cor.test(PE_TE_data_no_outlier[,"TE_abundance"], PE_TE_data_no_outlier[,"PE_abundance"], method = "spearman");
+  cor_result_kendall = cor.test(PE_TE_data_no_outlier[,"TE_abundance"], PE_TE_data_no_outlier[,"PE_abundance"], method = "kendall");
+  
+  cat('<tr><td>\n', file = htmloutfile, append=TRUE);
+  singlesample_cor(PE_TE_data, htmloutfile, append=TRUE);
+  cat('</td>\n', file = htmloutfile, append=TRUE);
+  
+  
+  cat('<td><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Method 1</font></th><th><font color=#ffcc33>Method 2</font></th><th><font color=#ffcc33>Method 3</font></th></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  cat(
+    "<tr><td>Correlation method</td><td>",cor_result_pearson$method,"</td><td>",cor_result_spearman$method,"</td><td>",cor_result_kendall$method,"</td></tr>\n",
+    "<tr><td>Correlation coefficient</td><td>",cor_result_pearson$estimate,"</td><td>",cor_result_spearman$estimate,"</td><td>",cor_result_kendall$estimate,"</td></tr>\n",
+    file = htmloutfile, append = TRUE)
+  cat("</table></td></tr></table>\n", file = htmloutfile, append = TRUE)
+  
+  
+  
+  if(dim(PE_TE_data_outlier)[1]<10)
+  {
+    tab_n_row = dim(PE_TE_data_outlier)[1];
+  }else{
+    tab_n_row = 10;
+  }
+  
+  cat("<br><br><font size=5><b><a href='PE_TE_influential_observation.txt' target='_blank'>Download the complete list of influential observations</a></b></font>&nbsp;&nbsp;&nbsp;&nbsp;",
+  "<font size=5><b><a href='PE_TE_non_influential_observation.txt' target='_blank'>Download the complete list (After removing influential points)</a></b></font><br>\n",
+  '<br><font color="brown"><h4>Top ',as.character(tab_n_row),' Influential observations (Cook\'s distance > ',cookdist_upper_cutoff,' * mean Cook\'s distance)</h4></font>\n',
+    file = htmloutfile, append = TRUE);
+  
+  cat('<table border=1 cellspacing=0 cellpadding=5> <tr bgcolor="#7a0019">\n', sep = "",file = htmloutfile, append = TRUE);
+  cat("<th><font color=#ffcc33>Gene</font></th><th><font color=#ffcc33>Protein Log Fold-Change</font></th><th><font color=#ffcc33>Transcript Log Fold-Change</font></th><th><font color=#ffcc33>Cook's Distance</font></th></tr>\n",
+    file = htmloutfile, append = TRUE);
+  
+  
+  for(i in 1:tab_n_row)
+  {
+    cat(
+      '<tr>','<td>',as.character(PE_TE_data_outlier_sorted[i,1]),'</td>\n',
+      '<td>',format(PE_TE_data_outlier_sorted[i,2], scientific=F),'</td>\n',
+      '<td>',PE_TE_data_outlier_sorted[i,4],'</td>\n',
+      '<td>',format(PE_TE_data_outlier_sorted[i,5], scientific=F),'</td></tr>\n',
+      file = htmloutfile, append = TRUE);
+  }
+    cat('</table><br><br>\n',file = htmloutfile, append = TRUE);
+    
+    
+}
+
+
+
+#===============================================================================
+# Heatmap
+#===============================================================================
+singlesample_heatmap=function(PE_TE_data, htmloutfile, hm_nclust){
+  cat('<br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Heatmap of PE and TE abundance values (Hierarchical clustering)</font></th><th><font color=#ffcc33>Number of clusters to extract: ',hm_nclust,'</font></th></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  hc=hclust(dist(as.matrix(PE_TE_data[,c("PE_abundance","TE_abundance")])))
+  hm_cluster = cutree(hc,k=hm_nclust);
+  
+  outplot = paste(outdir,"/PE_TE_heatmap.png",sep="",collapse="");
+  png(outplot, width = 10, height = 10, units = 'in', res=300);
+  # bitmap(outplot, "png16m");
+  par(mfrow=c(1,1));
+  hmap = heatmap.2(as.matrix(PE_TE_data[,c("PE_abundance","TE_abundance")]), trace="none", cexCol=1, col=greenred(100),Colv=F, labCol=c("Proteins","Transcripts"), scale="col", hclustfun = hclust, distfun = dist);
+  dev.off();
+  
+  cat('<tr><td align=center colspan="2"><img src="PE_TE_heatmap.png" width=800 height=800></td></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  
+  temp_PE_TE_data = data.frame(PE_TE_data$PE_ID, PE_TE_data$TE_abundance, PE_TE_data$PE_abundance, hm_cluster);
+  colnames(temp_PE_TE_data) = c("Gene", "Transcript abundance", "Protein abundance", "Cluster (Hierarchical clustering)")
+  tempoutfile = paste(outdir,"/PE_TE_hc_clusterpoints.txt",sep="",collapse="");
+  write.table(temp_PE_TE_data, file=tempoutfile, row.names=F, quote=F, sep="\t", eol="\n")
+  
+  
+  cat('<tr><td colspan="2" align=center><font size=5><a href="PE_TE_hc_clusterpoints.txt" target="_blank"><b>Download the hierarchical cluster list</b></a></font></td></tr></table>\n',
+    file = htmloutfile, append = TRUE);
+}
+
+
+#===============================================================================
+# K-means clustering
+#===============================================================================
+singlesample_kmeans=function(PE_TE_data, htmloutfile, nclust){
+  PE_TE_data_kdata = PE_TE_data;
+  k1 = kmeans(PE_TE_data_kdata[,c("PE_abundance","TE_abundance")], nclust);
+  outplot = paste(outdir,"/PE_TE_kmeans.png",sep="",collapse="");
+  png(outplot, width = 10, height = 10, units = 'in', res=300);
+  # bitmap(outplot, "png16m");
+  par(mfrow=c(1,1));
+  scatter.smooth(PE_TE_data_kdata[,"TE_abundance"], PE_TE_data_kdata[,"PE_abundance"], xlab="Transcript Abundance", ylab="Protein Abundance", cex.lab=1.5);
+  legend(1, 95, legend=c("Cluster 1", "Line 2"), col="red", lty=1:1, cex=0.8)
+  legend(1, 95, legend="Cluster 2", col="green", lty=1:1, cex=0.8)
+  
+  
+  ind=which(k1$cluster==1);
+  points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="red", pch=16);
+  ind=which(k1$cluster==2);
+  points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="green", pch=16);
+  ind=which(k1$cluster==3);
+  points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="blue", pch=16);
+  ind=which(k1$cluster==4);
+  points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="cyan", pch=16);
+  ind=which(k1$cluster==5);
+  points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="black", pch=16);
+  ind=which(k1$cluster==6);
+  points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="brown", pch=16);
+  ind=which(k1$cluster==7);
+  points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="gold", pch=16);
+  ind=which(k1$cluster==8);
+  points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="thistle", pch=16);
+  ind=which(k1$cluster==9);
+  points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="yellow", pch=16);
+  ind=which(k1$cluster==10);
+  points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="orange", pch=16);
+  dev.off();
+  
+  cat('<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>K-mean clustering</font></th><th><font color=#ffcc33>Number of clusters: ',nclust,'</font></th></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  tempind = order(k1$cluster);
+  tempoutfile = paste(outdir,"/PE_TE_kmeans_clusterpoints.txt",sep="",collapse="");
+  write.table(data.frame(PE_TE_data_kdata[tempind, ], Cluster=k1$cluster[tempind]), file=tempoutfile, row.names=F, quote=F, sep="\t", eol="\n")
+  
+  
+  cat('<tr><td colspan="2" align=center><img src="PE_TE_kmeans.png" width=800 height=800></td></tr>\n',
+    file = htmloutfile, append = TRUE);
+  cat('<tr><td colspan="2" align=center><font size=5><a href="PE_TE_kmeans_clusterpoints.txt" target="_blank"><b>Download the cluster list</b></a></font></td></tr></table><br><hr/>\n',
+    file = htmloutfile, append = TRUE);
+  
+}
+
+#===============================================================================
+# scatter plot
+#===============================================================================
+singlesample_scatter = function(PE_TE_data, outfile)
+{
+  min_lim = min(c(PE_TE_data$PE_abundance,PE_TE_data$TE_abundance));
+  max_lim = max(c(PE_TE_data$PE_abundance,PE_TE_data$TE_abundance));
+  png(outfile, width = 10, height = 10, units = 'in', res=300);
+  # bitmap(outfile, "png16m");
+  g = ggplot(PE_TE_data, aes(x=TE_abundance, y=PE_abundance))+geom_point() + geom_smooth() + xlab("Transcript abundance log fold-change") + ylab("Protein abundance log fold-change") + xlim(min_lim,max_lim) + ylim(min_lim,max_lim);
+  suppressMessages(plot(g));
+  # plot(g);
+  dev.off();
+}
+
+#===============================================================================
+# Correlation table
+#===============================================================================
+singlesample_cor = function(PE_TE_data, htmloutfile, append=TRUE)
+{
+  cor_result_pearson = cor.test(PE_TE_data$TE_abundance, PE_TE_data$PE_abundance, method = "pearson");
+  cor_result_spearman = cor.test(PE_TE_data$TE_abundance, PE_TE_data$PE_abundance, method = "spearman");
+  cor_result_kendall = cor.test(PE_TE_data$TE_abundance, PE_TE_data$PE_abundance, method = "kendall");
+  
+  cat(
+    '<table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Method 1</font></th><th><font color=#ffcc33>Method 2</font></th><th><font color=#ffcc33>Method 3</font></th></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  cat(
+    "<tr><td>Correlation method</td><td>",cor_result_pearson$method,"</td><td>",cor_result_spearman$method,"</td><td>",cor_result_kendall$method,"</td></tr>\n",
+    "<tr><td>Correlation coefficient</td><td>",cor_result_pearson$estimate,"</td><td>",cor_result_spearman$estimate,"</td><td>",cor_result_kendall$estimate,"</td></tr>\n",
+    file = htmloutfile, append = TRUE)
+  cat("</table>\n", file = htmloutfile, append = TRUE);
+  
+}
+
+#===============================================================================
+# Boxplot
+#===============================================================================
+multisample_boxplot = function(df, sampleinfo_df, outfile, fill_leg, user_xlab, user_ylab)
+{
+  tempdf = df[,-1, drop=FALSE];
+  tempdf = t(tempdf) %>% as.data.frame();
+  tempdf[is.na(tempdf)] = 0;
+  tempdf$Sample = rownames(tempdf);
+  tempdf1 = melt(tempdf, id.vars = "Sample");
+  tempdf1$Group = sampleinfo_df[tempdf1$Sample,2];
+  png(outplot, width = 6, height = 6, units = 'in', res=300);
+  # bitmap(outplot, "png16m");
+  if(fill_leg=="Yes")
+  {
+    g = ggplot(tempdf1, aes(x=Sample, y=value, fill=Group)) + geom_boxplot() + labs(x=user_xlab) + labs(y=user_ylab)
+  }else{
+    if(fill_leg=="No")
+    {
+      tempdf1$Group = c("case", "control")
+      g = ggplot(tempdf1, aes(x=Sample, y=value, fill=Group)) + geom_boxplot() + labs(x=user_xlab) + labs(y=user_ylab)
+    }
+  }
+  plot(g);
+  dev.off();
+}
+
+
+#===============================================================================
+# Mean or Median of Replicates
+#===============================================================================
+
+mergeReplicates = function(TE_df,PE_df, sampleinfo_df, method)
+{
+grps = unique(sampleinfo_df[,2]);
+
+TE_df_merged <<- sapply(grps, function(x){
+  tempsample = sampleinfo_df[which(sampleinfo_df$Group==x),1]
+  if(length(tempsample)!=1){
+    apply(TE_df[,tempsample],1,method);
+  }else{
+    return(TE_df[,tempsample]);
+  }
+});
+TE_df_merged <<-   data.frame(as.character(TE_df[,1]), TE_df_merged);
+colnames(TE_df_merged) = c(colnames(TE_df)[1], grps);
+
+PE_df_merged <<- sapply(grps, function(x){
+  tempsample = sampleinfo_df[which(sampleinfo_df$Group==x),1]
+  if(length(tempsample)!=1){
+    apply(PE_df[,tempsample],1,method);
+  }else{
+    return(PE_df[,tempsample]);
+  }
+});
+
+PE_df_merged <<-   data.frame(as.character(PE_df[,1]), PE_df_merged);
+colnames(PE_df_merged) = c(colnames(PE_df)[1], grps);
+
+#sampleinfo_df_merged =  data.frame(Sample = grps, Group = grps, stringsAsFactors = F);
+sampleinfo_df_merged =  data.frame(Sample = grps, Group = "Group", stringsAsFactors = F);
+
+return(list(TE_df_merged = TE_df_merged, PE_df_merged = PE_df_merged, sampleinfo_df_merged = sampleinfo_df_merged));
+}
+
+#===============================================================================
+# (T-Test or Wilcoxon ranksum test) and Volcano Plot
+#===============================================================================
+
+perform_Test_Volcano = function(TE_df_data,PE_df_data,TE_df_logfold, PE_df_logfold,sampleinfo_df, method, correction_method,volc_with)
+{
+
+PE_colnames = colnames(PE_df_data);
+control_sample = sampleinfo_df[which(sampleinfo_df$Group=="control"),1];
+control_ind <<- sapply(control_sample, function(x){temp_ind = which(PE_colnames==x); as.numeric(temp_ind)});
+condition_sample = sampleinfo_df[which(sampleinfo_df$Group=="case"),1];
+condition_ind <<- sapply(condition_sample, function(x){temp_ind = which(PE_colnames==x); as.numeric(temp_ind)});
+
+if(method=="mean"){
+  #PE_pval = apply(PE_df_data[2:length(colnames(PE_df_data))],1,function(x) t.test(x[condition_ind-1], x[control_ind-1])$p.value);
+  PE_pval = apply(PE_df_data[2:length(colnames(PE_df_data))],1,function(x) {obj<-try(t.test(x[condition_ind-1], x[control_ind-1]),silent=TRUE); if(is(obj, "try-error")){return(NA)}else{return(obj$p.value)}})
+}else{
+if(method=="median"){
+    PE_pval = apply(PE_df_data[2:length(colnames(PE_df_data))],1,function(x) {obj<-try(wilcox.test(x[condition_ind-1], x[control_ind-1]),silent=TRUE); if(is(obj, "try-error")){return(NA)}else{return(obj$p.value)}})
+    # PE_pval = apply(PE_df_data[2:length(colnames(PE_df_data))],1,function(x) wilcox.test(x[condition_ind-1], x[control_ind-1])$p.value);
+  }
+}
+PE_adj_pval = p.adjust(PE_pval, method = correction_method, n = length(PE_pval))
+
+
+TE_colnames = colnames(TE_df_data);
+control_sample = sampleinfo_df[which(sampleinfo_df$Group=="control"),1];
+control_ind <<- sapply(control_sample, function(x){temp_ind = which(TE_colnames==x); as.numeric(temp_ind)});
+condition_sample = sampleinfo_df[which(sampleinfo_df$Group=="case"),1];
+condition_ind <<- sapply(condition_sample, function(x){temp_ind = which(TE_colnames==x); as.numeric(temp_ind)});
+
+if(method=="mean"){
+  # TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) t.test(x[condition_ind-1], x[control_ind-1])$p.value);
+  TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) {obj<-try(t.test(x[condition_ind-1], x[control_ind-1]),silent=TRUE); if(is(obj, "try-error")){return(NA)}else{return(obj$p.value)}})
+}else{
+  if(method=="median"){
+  TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) {obj<-try(wilcox.test(x[condition_ind-1], x[control_ind-1]),silent=TRUE); if(is(obj, "try-error")){return(NA)}else{return(obj$p.value)}})
+  # TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) wilcox.test(x[condition_ind-1], x[control_ind-1])$p.value);
+  }
+}
+TE_adj_pval = p.adjust(TE_pval, method = correction_method, n = length(TE_pval))
+
+
+PE_TE_logfold_pval = data.frame(TE_df_logfold$Gene, TE_df_logfold$LogFold, TE_pval, TE_adj_pval, PE_df_logfold$LogFold, PE_pval, PE_adj_pval);
+colnames(PE_TE_logfold_pval) = c("Gene", "Transcript log fold-change", "p-value (transcript)", "adj p-value (transcript)", "Protein log fold-change", "p-value (protein)", "adj p-value (protein)");
+outdatafile = paste(outdir,"/PE_TE_logfold_pval.txt", sep="", collapse="");
+write.table(PE_TE_logfold_pval, file=outdatafile, row.names=F, sep="\t", quote=F);
+cat("<br><br><font size=5><b><a href='PE_TE_logfold_pval.txt' target='_blank'>Download the complete fold change data here</a></b></font><br>\n",
+    file = htmloutfile, append = TRUE);
+
+    if(length(condition_ind)!=1)
+        {
+        # Volcano Plot
+
+        if(volc_with=="adj_pval")
+        {
+            PE_pval = PE_adj_pval
+            TE_pval = TE_adj_pval
+            volc_ylab = "-log10 Adjusted p-value";
+        }else{
+          if(volc_with=="pval")
+          {
+            volc_ylab = "-log10 p-value";
+          }
+        }
+        outplot = paste(outdir,"/PE_volcano.png",sep="",collapse="");
+          png(outplot, width = 10, height = 10, units = 'in', res=300);
+          # bitmap(outplot, "png16m");
+          par(mfrow=c(1,1));
+  
+        plot(PE_df_logfold$LogFold, -log10(PE_pval),
+             xlab="log2 fold change", ylab=volc_ylab,
+             type="n")
+        sel <- which((PE_df_logfold$LogFold<=log(2,base=2))&(PE_df_logfold$LogFold>=log(0.5, base=2))) # or whatever you want to use
+        points(PE_df_logfold[sel,"LogFold"], -log10(PE_pval[sel]),col="black")
+        #sel <- which((PE_df_logfold$LogFold>log(2,base=2))&(PE_df_logfold$LogFold<log(0.5,base=2))) # or whatever you want to use
+        sel <- which((PE_df_logfold$LogFold>log(2,base=2))|(PE_df_logfold$LogFold<log(0.5, base=2)))
+        sel1 <- which(PE_pval<=0.05)
+        sel=intersect(sel,sel1)
+        points(PE_df_logfold[sel,"LogFold"], -log10(PE_pval[sel]),col="red")
+        sel <- which((PE_df_logfold$LogFold>log(2,base=2))|(PE_df_logfold$LogFold<log(0.5, base=2)))
+        sel1 <- which(PE_pval>0.05)
+        sel=intersect(sel,sel1)
+        points(PE_df_logfold[sel,"LogFold"], -log10(PE_pval[sel]),col="blue")
+        abline(h = -log(0.05,base=10), col="red", lty=2)
+        abline(v = log(2,base=2), col="red", lty=2)
+        abline(v = log(0.5,base=2), col="red", lty=2)
+        dev.off();
+  
+
+
+        outplot = paste(outdir,"/TE_volcano.png",sep="",collapse="");
+          png(outplot, width = 10, height = 10, units = 'in', res=300);
+          # bitmap(outplot, "png16m");
+          par(mfrow=c(1,1));
+
+        plot(TE_df_logfold$LogFold, -log10(TE_pval),
+             xlab="log2 fold change", ylab=volc_ylab,
+             type="n")
+        sel <- which((TE_df_logfold$LogFold<=log(2,base=2))&(TE_df_logfold$LogFold>=log(0.5, base=2))) # or whatever you want to use
+        points(TE_df_logfold[sel,"LogFold"], -log10(TE_pval[sel]),col="black")
+        #sel <- which((TE_df_logfold$LogFold>log(2,base=2))&(TE_df_logfold$LogFold<log(0.5,base=2))) # or whatever you want to use
+        sel <- which((TE_df_logfold$LogFold>log(2,base=2))|(TE_df_logfold$LogFold<log(0.5, base=2)))
+        sel1 <- which(TE_pval<=0.05)
+        sel=intersect(sel,sel1)
+        points(TE_df_logfold[sel,"LogFold"], -log10(TE_pval[sel]),col="red")
+        sel <- which((TE_df_logfold$LogFold>log(2,base=2))|(TE_df_logfold$LogFold<log(0.5, base=2)))
+        sel1 <- which(TE_pval>0.05)
+        sel=intersect(sel,sel1)
+        points(TE_df_logfold[sel,"LogFold"], -log10(TE_pval[sel]),col="blue")
+        abline(h = -log(0.05,base=10), col="red", lty=2)
+        abline(v = log(2,base=2), col="red", lty=2)
+        abline(v = log(0.5,base=2), col="red", lty=2)
+        dev.off();
+
+
+
+        cat('<br><table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Transcript Fold-Change</font></th><th><font color=#ffcc33>Protein Fold-Change</font></th></tr>\n', file = htmloutfile, append = TRUE);
+            cat("<tr><td align=center>", '<img src="TE_volcano.png" width=600 height=600></td>\n', file = htmloutfile, append = TRUE);
+            cat("<td align=center>",
+            '<img src="PE_volcano.png" width=600 height=600></td></tr></table><br>\n',
+            file = htmloutfile, append = TRUE);
+    }else{
+        cat('<br><br><b><font color=red>!!! No replicates found. Cannot perform test to check significance of differential expression. Thus, no Volcano plot generated !!!</font></b><br><br>',
+        file = htmloutfile, append = TRUE);
+    }
+
+}
+
+
+#***************************************************************************************************************************************
+# Functions: End
+#***************************************************************************************************************************************
+
+
+
+
+#===============================================================================
+# Arguments
+#===============================================================================
+noargs = 12;
+args = commandArgs(trailingOnly = TRUE);
+if(length(args) != noargs)
+{
+  stop(paste("Please check usage. Number of arguments is not equal to ",noargs,sep="",collapse=""));
+}
+
+mode = args[1]; # "multiple" or "logfold"
+method = args[2]; # "mean" or "median"
+sampleinfo_file = args[3];
+proteome_file = args[4];
+transcriptome_file = args[5];
+correction_method = args[6];
+cookdist_upper_cutoff = args[7];
+numCluster = args[8];
+hm_nclust = args[9];
+volc_with = args[10];
+
+htmloutfile = args[11]; # html output file
+outdir = args[12]; # html supporting files
+
+
+#===============================================================================
+# Check for file existance
+#===============================================================================
+if(! file.exists(proteome_file))
+{
+  stop(paste("Proteome Data file does not exists. Path given: ",proteome_file,sep="",collapse=""));
+}
+if(! file.exists(transcriptome_file))
+{
+  stop(paste("Transcriptome Data file does not exists. Path given: ",transcriptome_file,sep="",collapse=""));
+}
+
+#===============================================================================
+# Load library
+#===============================================================================
+options(warn=-1);
+
+suppressPackageStartupMessages(library(dplyr));
+suppressPackageStartupMessages(library(data.table));
+suppressPackageStartupMessages(library(gplots));
+suppressPackageStartupMessages(library(ggplot2));
+suppressPackageStartupMessages(library(ggfortify));
+
+#===============================================================================
+# Select mode and parse experiment design file
+#===============================================================================
+if(mode=="multiple")
+{
+    expDesign = fread(sampleinfo_file, header = FALSE, stringsAsFactors = FALSE, sep="\t") %>% data.frame();
+    expDesign_cc = expDesign[1:2,];
+    
+    sampleinfo_df = expDesign[3:nrow(expDesign),];
+    rownames(sampleinfo_df)=1:nrow(sampleinfo_df);
+    colnames(sampleinfo_df) =  c("Sample","Group");
+    
+    condition_cols = sampleinfo_df[which(sampleinfo_df[,2]==expDesign_cc[which(expDesign_cc[,1]=="case"),2]),1];
+    condition_g_name = "case";
+    control_cols = sampleinfo_df[which(sampleinfo_df[,2]==expDesign_cc[which(expDesign_cc[,1]=="control"),2]),1];
+    control_g_name = "control";
+    sampleinfo_df[which(sampleinfo_df[,2]==expDesign_cc[which(expDesign_cc[,1]=="case"),2]),2] = "case";
+    sampleinfo_df[which(sampleinfo_df[,2]==expDesign_cc[which(expDesign_cc[,1]=="control"),2]),2] = "control";
+    sampleinfo_df_orig = sampleinfo_df;
+}
+
+if(mode=="logfold")
+{
+    sampleinfo_df = data.frame("Sample"= c("LogFold"), "Group"=c("Fold_Change"))
+}
+
+#===============================================================================
+# Parse Transcriptome data
+#===============================================================================
+TE_df_orig = fread(transcriptome_file, sep="\t", stringsAsFactor=F, header=T) %>% data.frame();
+if(mode=="multiple")
+{
+    TE_df = TE_df_orig[,c(colnames(TE_df_orig)[1],condition_cols,control_cols)];
+}
+if(mode=="logfold")
+{
+    TE_df = TE_df_orig;
+    colnames(TE_df) = c("Genes", "LogFold");
+}
+#===============================================================================
+# Parse Proteome data
+#===============================================================================
+PE_df_orig = fread(proteome_file, sep="\t", stringsAsFactor=F, header=T) %>% data.frame();
+if(mode=="multiple")
+{
+    PE_df = PE_df_orig[,c(colnames(PE_df_orig)[1],condition_cols,control_cols)];
+}
+if(mode=="logfold")
+{
+    PE_df = PE_df_orig;
+    colnames(PE_df) = c("Genes", "LogFold");
+}
+
+#=============================================================================================================
+# Create directory structures and then set the working directory to output directory
+#=============================================================================================================
+if(! file.exists(outdir))
+{
+  dir.create(outdir);
+}
+#===============================================================================
+# Write initial data summary in html outfile
+#===============================================================================
+    cat("<html><head></head><body>\n", file = htmloutfile);
+    
+    cat("<h1><u>QuanTP: Association between abundance ratios of transcript and protein</u></h1><hr/>\n",
+    "<font><h3>Input data summary</h3></font>\n",
+    "<ul>\n",
+    "<li>Abbreviations used: PE (Proteome data) and TE (Transcriptome data)","</li><br>\n",
+    "<li>Input Proteome data dimension (Row Column): ", dim(PE_df)[1]," x ", dim(PE_df)[2],"</li>\n",
+    "<li>Input Transcriptome data dimension (Row Column): ", dim(TE_df)[1]," x ", dim(TE_df)[2],"</li></ul><hr/>\n",
+    file = htmloutfile, append = TRUE);
+    
+    cat("<h3 id=table_of_content>Table of Contents:</h3>\n",
+    "<ul>\n",
+    "<li><a href=#sample_dist>Sample distribution</a></li>\n",
+    "<li><a href=#corr_data>Correlation</a></li>\n",
+    "<li><a href=#regression_data>Regression analysis</a></li>\n",
+    "<li><a href=#inf_obs>Influential observations</a></li>\n",
+    "<li><a href=#cluster_data>Cluster analysis</a></li></ul><hr/>\n",
+    file = htmloutfile, append = TRUE);
+#===============================================================================
+# Find common samples
+#===============================================================================
+common_samples = intersect(sampleinfo_df[,1], colnames(TE_df)[-1]) %>% intersect(., colnames(PE_df)[-1]);
+
+if(length(common_samples)==0)
+{
+  stop("No common samples found ");
+  cat("<b>Please check your experiment design file. Sample names (column names) in the Transcriptome and the Proteome data do not match. </b>\n",file = htmloutfile, append = TRUE);
+}
+
+#===============================================================================
+# Create subsets based on common samples
+#===============================================================================
+TE_df =  select(TE_df, 1, common_samples);
+PE_df =  select(PE_df, 1, common_samples);
+sampleinfo_df = filter(sampleinfo_df, Sample %in% common_samples);
+rownames(sampleinfo_df) = sampleinfo_df[,1];
+
+#===============================================================================
+# Check for number of rows similarity
+#===============================================================================
+if(nrow(TE_df) != nrow(PE_df))
+{
+  stop("Number of rows in Transcriptome and Proteome data are not same i.e. they are not paired");
+  cat("<b>The correlation analysis expects paired TE and PE data i.e. (i)th gene/transcript of TE file should correspond to (i)th protein of PE file. In the current input provided there is mismatch in terms of number of rows of TE and PE file. Please make sure you provide paired data.</b>\n",file = htmloutfile, append = TRUE);
+}
+
+#===============================================================================
+# Number of groups
+#===============================================================================
+ngrps = unique(sampleinfo_df[,2]) %>% length();
+grps = unique(sampleinfo_df[,2]);
+names(grps) = grps;
+
+#===============================================================================
+# Change column1 name
+#===============================================================================
+colnames(TE_df)[1] = "Gene";
+colnames(PE_df)[1] = "Protein";
+
+#===============================================================================
+# Treat missing values
+#===============================================================================
+TE_nacount = sum(is.na(TE_df));
+PE_nacount = sum(is.na(PE_df));
+
+TE_df[is.na(TE_df)] = 0;
+PE_df[is.na(PE_df)] = 0;
+
+
+#===============================================================================
+# Decide based on analysis mode
+#===============================================================================
+if(mode=="logfold")
+{
+  cat('<h2 id="sample_dist"><font color=#ff0000>SAMPLE DISTRIBUTION</font></h2>\n',
+  file = htmloutfile, append = TRUE);
+  
+  # TE Boxplot
+  outplot = paste(outdir,"/Box_TE.png",sep="",collape="");
+  cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">\n',
+  '<tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n',
+  "<tr><td align=center>", '<img src="Box_TE.png" width=500 height=500></td>\n', file = htmloutfile, append = TRUE);
+  multisample_boxplot(TE_df, sampleinfo_df, outplot, "Yes", "Samples", "Transcript Abundance data");
+  
+  # PE Boxplot
+  outplot = paste(outdir,"/Box_PE.png",sep="",collape="");
+  cat("<td align=center>", '<img src="Box_PE.png" width=500 height=500></td></tr></table>\n', file = htmloutfile, append = TRUE);
+  multisample_boxplot(PE_df, sampleinfo_df, outplot, "Yes", "Samples", "Protein Abundance data");
+  
+  cat('<hr/><h2 id="corr_data"><font color=#ff0000>CORRELATION</font></h2>\n',
+  file = htmloutfile, append = TRUE);
+  
+  # TE PE scatter
+  outplot = paste(outdir,"/TE_PE_scatter.png",sep="",collape="");
+  cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatter plot between Proteome and Transcriptome Abundance</font></th></tr>\n', file = htmloutfile, append = TRUE);
+  cat("<tr><td align=center>", '<img src="TE_PE_scatter.png" width=800 height=800></td></tr>\n', file = htmloutfile, append = TRUE);
+  PE_TE_data = data.frame(PE_df, TE_df);
+  colnames(PE_TE_data) = c("PE_ID","PE_abundance","TE_ID","TE_abundance");
+  singlesample_scatter(PE_TE_data, outplot);  
+  
+  # TE PE Cor
+  cat("<tr><td align=center>", file = htmloutfile, append = TRUE);
+  singlesample_cor(PE_TE_data, htmloutfile, append=TRUE);
+  cat('<font color="red">*Note that <u>correlation</u> is <u>sensitive to outliers</u> in the data. So it is important to analyze outliers/influential observations in the data.<br> Below we use <u>Cook\'s distance based approach</u> to identify such influential observations.</font>\n',
+    file = htmloutfile, append = TRUE);
+  cat('</td></table>',
+    file = htmloutfile, append = TRUE);
+  
+  cat('<hr/><h2 id="regression_data"><font color=#ff0000>REGRESSION ANALYSIS</font></h2>\n',
+  file = htmloutfile, append = TRUE);
+  
+  # TE PE Regression
+  singlesample_regression(PE_TE_data,htmloutfile, append=TRUE);
+  
+  cat('<hr/><h2 id="cluster_data"><font color=#ff0000>CLUSTER ANALYSIS</font></h2>\n',
+  file = htmloutfile, append = TRUE);
+  
+  # TE PE Heatmap
+  singlesample_heatmap(PE_TE_data, htmloutfile, hm_nclust);
+  
+  
+  # TE PE Clustering (kmeans)
+  singlesample_kmeans(PE_TE_data, htmloutfile, nclust=as.numeric(numCluster))
+  
+}else{
+  if(mode=="multiple")
+  {
+    cat('<h2 id="sample_dist"><font color=#ff0000>SAMPLE DISTRIBUTION</font></h2>\n',
+    file = htmloutfile, append = TRUE);
+    
+    # TE Boxplot
+    outplot = paste(outdir,"/Box_TE_all_rep.png",sep="",collape="");
+    cat('<table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">\n',
+    '<tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n',
+    "<tr><td align=center>", '<img src="Box_TE_all_rep.png" width=500 height=500></td>\n', file = htmloutfile, append = TRUE);
+    temp_df_te_data = data.frame(TE_df[,1], log(TE_df[,2:length(TE_df)]));
+    colnames(temp_df_te_data) = colnames(TE_df);
+    multisample_boxplot(temp_df_te_data, sampleinfo_df, outplot, "Yes", "Samples", "Transcript Abundance (log)");
+    
+    # PE Boxplot
+    outplot = paste(outdir,"/Box_PE_all_rep.png",sep="",collape="");
+    cat("<td align=center>", '<img src="Box_PE_all_rep.png" width=500 height=500></td></tr></table>\n', file = htmloutfile, append = TRUE);
+    temp_df_pe_data = data.frame(PE_df[,1], log(PE_df[,2:length(PE_df)]));
+    colnames(temp_df_pe_data) = colnames(PE_df);
+    multisample_boxplot(temp_df_pe_data, sampleinfo_df, outplot, "Yes", "Samples", "Protein Abundance (log)");
+    
+    # Calc TE PCA
+    outplot = paste(outdir,"/PCA_TE_all_rep.png",sep="",collape="");
+    multisample_PCA(TE_df, sampleinfo_df, outplot);
+
+    # Calc PE PCA
+    outplot = paste(outdir,"/PCA_PE_all_rep.png",sep="",collape="");
+    multisample_PCA(PE_df, sampleinfo_df, outplot);
+    
+    
+    # Replicate mode
+    templist = mergeReplicates(TE_df,PE_df, sampleinfo_df, method);
+    TE_df = templist$TE_df_merged;
+    PE_df = templist$PE_df_merged;
+    sampleinfo_df = templist$sampleinfo_df_merged;
+    rownames(sampleinfo_df) = sampleinfo_df[,1];
+    
+    # TE Boxplot
+    outplot = paste(outdir,"/Box_TE_rep.png",sep="",collape="");
+    cat('<br><font color="#ff0000"><h3>Sample wise distribution (Box plot) after using ',method,' on replicates </h3></font><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n',
+    "<tr><td align=center>", '<img src="Box_TE_rep.png" width=500 height=500></td>\n', file = htmloutfile, append = TRUE);
+    temp_df_te_data = data.frame(TE_df[,1], log(TE_df[,2:length(TE_df)]));
+    colnames(temp_df_te_data) = colnames(TE_df);
+    multisample_boxplot(temp_df_te_data, sampleinfo_df, outplot, "No", "Sample Groups", "Mean Transcript Abundance (log)");
+
+    # PE Boxplot
+    outplot = paste(outdir,"/Box_PE_rep.png",sep="",collape="");
+    cat("<td align=center>", '<img src="Box_PE_rep.png" width=500 height=500></td></tr></table>\n', file = htmloutfile, append = TRUE);
+    temp_df_pe_data = data.frame(PE_df[,1], log(PE_df[,2:length(PE_df)]));
+    colnames(temp_df_pe_data) = colnames(PE_df);
+    multisample_boxplot(temp_df_pe_data, sampleinfo_df, outplot, "No", "Sample Groups", "Mean Protein Abundance (log)");
+
+    #===============================================================================
+    # Calculating log fold change and running the "single" code part 
+    #===============================================================================
+
+    TE_df = data.frame("Genes"=TE_df[,1], "LogFold"=apply(TE_df[,c(which(colnames(TE_df)==condition_g_name),which(colnames(TE_df)==control_g_name))],1,function(x) log(x[1]/x[2],base=2)));
+    PE_df = data.frame("Genes"=PE_df[,1], "LogFold"=apply(PE_df[,c(which(colnames(PE_df)==condition_g_name),which(colnames(PE_df)==control_g_name))],1,function(x) log(x[1]/x[2],base=2)));
+  
+      #===============================================================================
+      # Treat missing values
+      #===============================================================================
+  
+      TE_df[is.infinite(TE_df[,2]),2] = NA;
+      PE_df[is.infinite(PE_df[,2]),2] = NA;
+      TE_df[is.na(TE_df)] = 0;
+      PE_df[is.na(PE_df)] = 0;
+
+      sampleinfo_df = data.frame("Sample"= c("LogFold"), "Group"=c("Fold_Change"))
+      #===============================================================================
+      # Find common samples
+      #===============================================================================
+  
+      common_samples = intersect(sampleinfo_df[,1], colnames(TE_df)[-1]) %>% intersect(., colnames(PE_df)[-1]);
+      TE_df =  select(TE_df, 1, common_samples);
+      PE_df =  select(PE_df, 1, common_samples);
+      sampleinfo_df = filter(sampleinfo_df, Sample %in% common_samples);
+      rownames(sampleinfo_df) = sampleinfo_df[,1];
+  
+    # TE Boxplot
+    outplot = paste(outdir,"/Box_TE.png",sep="",collape="");
+    cat('<br><font color="#ff0000"><h3>Distribution (Box plot) of log fold change </h3></font>', file = htmloutfile, append = TRUE);
+    cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n',
+    "<tr><td align=center>", '<img src="Box_TE.png" width=500 height=500></td>\n', file = htmloutfile, append = TRUE);
+    multisample_boxplot(TE_df, sampleinfo_df, outplot, "Yes", "Sample (log2(case/control))", "Transcript Abundance fold-change (log2)");
+    
+    # PE Boxplot
+    outplot = paste(outdir,"/Box_PE.png",sep="",collape="");
+    cat("<td align=center>", '<img src="Box_PE.png" width=500 height=500></td></tr></table>\n', file = htmloutfile, append = TRUE);
+    multisample_boxplot(PE_df, sampleinfo_df, outplot, "Yes", "Sample (log2(case/control))", "Protein Abundance fold-change(log2)");
+    
+    
+    # Log Fold Data
+    perform_Test_Volcano(TE_df_orig,PE_df_orig,TE_df, PE_df,sampleinfo_df_orig,method,correction_method,volc_with)
+    
+    
+    
+    # Print PCA
+    
+    cat('<br><br><table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>PCA plot: Transcriptome data</font></th><th><font color=#ffcc33>PCA plot: Proteome data</font></th></tr>\n',
+    "<tr><td align=center>", '<img src="PCA_TE_all_rep.png" width=500 height=500></td>\n',
+    "<td align=center>", '<img src="PCA_PE_all_rep.png" width=500 height=500></td></tr></table>\n', 
+      file = htmloutfile, append = TRUE);
+    
+    
+    
+    cat('<hr/><h2 id="corr_data"><font color=#ff0000>CORRELATION</font></h2>\n',
+    file = htmloutfile, append = TRUE);
+    
+    # TE PE scatter
+    outplot = paste(outdir,"/TE_PE_scatter.png",sep="",collape="");
+    cat('<br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatter plot between Proteome and Transcriptome Abundance</font></th></tr>\n', file = htmloutfile, append = TRUE);
+    cat("<tr><td align=center>", '<img src="TE_PE_scatter.png" width=800 height=800></td>\n', file = htmloutfile, append = TRUE);
+    PE_TE_data = data.frame(PE_df, TE_df);
+    colnames(PE_TE_data) = c("PE_ID","PE_abundance","TE_ID","TE_abundance");
+    singlesample_scatter(PE_TE_data, outplot);  
+
+    # TE PE Cor
+    cat("<tr><td align=center>\n", file = htmloutfile, append = TRUE);
+    singlesample_cor(PE_TE_data, htmloutfile, append=TRUE);
+    cat('<font color="red">*Note that <u>correlation</u> is <u>sensitive to outliers</u> in the data. So it is important to analyze outliers/influential observations in the data.<br> Below we use <u>Cook\'s distance based approach</u> to identify such influential observations.</font>\n',
+      file = htmloutfile, append = TRUE);
+    cat('</td></table>',
+    file = htmloutfile, append = TRUE);
+    
+    cat('<hr/><h2 id="regression_data"><font color=#ff0000>REGRESSION ANALYSIS</font></h2>\n',
+    file = htmloutfile, append = TRUE);
+    
+    # TE PE Regression
+    singlesample_regression(PE_TE_data,htmloutfile, append=TRUE);
+    
+    cat('<hr/><h2 id="cluster_data"><font color=#ff0000>CLUSTER ANALYSIS</font></h2>\n',
+    file = htmloutfile, append = TRUE);
+    
+    #TE PE Heatmap
+    singlesample_heatmap(PE_TE_data, htmloutfile, hm_nclust);
+    
+    #TE PE Clustering (kmeans)
+    singlesample_kmeans(PE_TE_data, htmloutfile, nclust=as.numeric(numCluster))
+    
+  }
+}
+cat("<h3>Go To:</h3>\n",
+    "<ul>\n",
+    "<li><a href=#sample_dist>Sample distribution</a></li>\n",
+    "<li><a href=#corr_data>Correlation</a></li>\n",
+    "<li><a href=#regression_data>Regression analysis</a></li>\n",
+    "<li><a href=#inf_obs>Influential observations</a></li>\n",
+    "<li><a href=#cluster_data>Cluster analysis</a></li></ul>\n",
+    "<br><a href=#>TOP</a>",
+    file = htmloutfile, append = TRUE);
+cat("</body></html>\n", file = htmloutfile, append = TRUE);
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/quantp.xml	Fri Sep 14 12:22:31 2018 -0400
@@ -0,0 +1,195 @@
+<tool id="quantp" name="QuanTP" version="1.0.0">
+    <description>Correlation between protein and transcript abundances</description>
+    <requirements>
+        <requirement type="package" version="1.10.4">r-data.table</requirement>
+        <requirement type="package" version="3.0.1">r-gplots</requirement>
+        <requirement type="package" version="0.7.6">r-dplyr</requirement>
+        <requirement type="package" version="3.0.0">r-ggplot2</requirement>
+        <requirement type="package" version="0.4.5">r-ggfortify</requirement>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+Rscript '$__tool_directory__/quantp.r' 
+    $experiment_design_option.sample_type 
+    $experiment_design_option.method_type 
+    $experiment_design_option.exp_design 
+    '$pe_exp' 
+    '$te_exp' 
+    $experiment_design_option.correction_method 
+    $cooksd_upper 
+    $nclust 
+    $hm_nclust 
+    $experiment_design_option.volcano_with 
+    '$html_file' 
+    '$html_file.files_path'
+]]></command>
+    <inputs>
+        <param name="pe_exp" type="data" format="tabular" label="Input Protein Abundance File" help="Protein abundance input file"/>
+        <param name="te_exp" type="data" format="tabular" label="Input RNA Abundance File" help="Transcript abundance input file"/>
+        <conditional name="experiment_design_option">
+            <param name="sample_type" type="select" label="Select data input type" help="If the input files already have fold-change values, select Log fold-change. Else, select abundances and the tool will perform the fold-change analysis">
+                <option value="multiple" selected="True">Abundances from different conditions with or without replicates (in multiple columns)</option>
+                <option value="logfold">Log fold-change values (or single condition abundance without replicates in single column) data</option>
+            </param>
+            <when value="logfold">
+                <param name="exp_design" type="hidden" value="none" />
+                <param name="method_type" type="hidden" value="none" />
+                <param name="correction_method" type="hidden" value="none" />
+                <param name="volcano_with" type="hidden" value="pval" />
+            </when>
+            <when value="multiple">
+                <param name="exp_design" type="data" format="tabular" help="Please check the format of the experiment design file">
+                    <label>Experiment design File (Please see the format below)</label>
+                </param>
+                <param name="method_type" type="select" label="Data summarization method" help="Perform T-Test on selecting Mean; Wilcoxon Ranksum Test on selecting Median">
+                    <option value="mean" selected="True">Mean (Default)</option>
+                    <option value="median">Median</option>
+                </param>
+                <param name="correction_method" type="select" label="Multiple testing correction method">
+                    <option value="BH" selected="True">Benjamini and Hochberg (BH) (Default)</option>
+                    <option value="holm">Holm</option>
+                    <option value="hochberg">Hochberg</option>
+                    <option value="hommel">Hommel</option>
+                    <option value="bonferroni">Bonferroni</option>
+                    <option value="BY">Benjamini and Yekutieli (BY)</option>
+                    <option value="none">None</option>
+                </param>
+                <param name="volcano_with" type="select" display="radio" label="Volcano plot with p-value or adjusted p-value">
+                    <option value="pval" selected="True">P-value (Default)</option>
+                    <option value="adj_pval">Adjusted P-value</option>
+                </param>
+            </when>
+        </conditional>
+        <param name="cooksd_upper" type="integer" value="4" optional="false" >
+            <label>Influential Observation cutoff: Observations > value * mean of Cook's distances (Default: "4" * mean(Cook's Distance))</label>
+        </param>
+        <param name="nclust" type="select" label="K-mean clustering: Number of clusters">
+            <option value="1">1</option>
+            <option value="2">2</option>
+            <option value="3">3</option>
+            <option value="4" selected="True">4</option>
+            <option value="5">5</option>
+            <option value="6">6</option>
+            <option value="7">7</option>
+            <option value="8">8</option>
+            <option value="9">9</option>
+            <option value="10">10</option>
+        </param>
+      
+        <param name="hm_nclust" type="select" label="Hierarchical clustering: Number of clusters (from Heatmap)">
+            <option value="1">1</option>
+            <option value="2">2</option>
+            <option value="3">3</option>
+            <option value="4">4</option>
+            <option value="5" selected="True">5</option>
+            <option value="6">6</option>
+            <option value="7">7</option>
+            <option value="8">8</option>
+            <option value="9">9</option>
+            <option value="10">10</option>
+        </param>
+    </inputs>
+    <outputs>
+        <data format="html" name="html_file" label="protein transcript correlation on ${pe_exp.name} and ${te_exp.name}"/>
+    </outputs>
+    <tests>
+        <test>
+            <conditional name="experiment_design_option">
+                <param name="sample_type" value="multiple"/>
+                <param name="method_type" value="mean"/>
+                <param name="exp_design" value="exp_design_file.tabular" ftype="tabular" />
+                <param name="correction_method" value="BH"/>
+                <param name="volcano_with" value="pval"/>
+            </conditional>
+            <param name="pe_exp" value="protein_data.tabular" ftype="tabular" />
+            <param name="te_exp" value="transcript_data.tabular" ftype="tabular" />
+            <param name="cooksd_upper" value="4"/>
+            <param name="nclust" value="4"/>
+            <param name="hm_nclust" value="5"/>
+            <output name="html_file">
+                <assert_contents>
+                    <has_text text="SAMPLE DISTRIBUTION" />
+                </assert_contents>
+            </output>
+        </test>
+    </tests>
+    <help><![CDATA[
+
+**What it does**
+
+QuanTP correlates *transcript abundance* and *protein abundance* to examine the association between them.
+
+It either takes in the log fold-change of abundances as input or raw abundances from different conditions where it calculates the log ratios of abundances between two conditions.
+
+Transcript input file can be generated from the quantitative RNA-Seq study whereas Protein input file can be generated from quantitative analysis of mass-spectrometry-based protein data.
+
+-----
+
+**Input file formats**
+
+**Protein data file**
+
+First column - Gene
+
+Following columns - Abundance values (or log fold-change values)
+
+Example of Protein input file
+
+====== ========= ========= ========= ========= ========= ========= ========= =========
+Gene   sample1   sample2   sample3   sample4   sample5   sample6   sample7   sample8
+------ --------- --------- --------- --------- --------- --------- --------- ---------
+GeneX  value     value     value     value     value     value     value     value    
+GeneY  value     value     value     value     value     value     value     value    
+GeneZ  value     value     value     value     value     value     value     value    
+====== ========= ========= ========= ========= ========= ========= ========= =========
+
+
+**Transcript data file**
+
+First column - Gene
+
+Following columns - Abundance values (or log fold-change values)
+
+Example of Transcript input file
+
+====== ========= ========= ========= ========= ========= ========= ========= =========
+Gene   sample1   sample2   sample3   sample4   sample5   sample6   sample7   sample8 
+------ --------- --------- --------- --------- --------- --------- --------- ---------
+GeneX  value     value     value     value     value     value     value     value    
+GeneY  value     value     value     value     value     value     value     value    
+GeneZ  value     value     value     value     value     value     value     value    
+====== ========= ========= ========= ========= ========= ========= ========= =========
+
+
+**Data input type**
+
+If data input type is abundance, experiment design file is required.
+
+Example of experiment design file
+
+======== =========
+case     groupA   
+control  groupB   
+sample1  groupA   
+sample2  groupA   
+sample3  groupA   
+sample4  groupA   
+sample5  groupB   
+sample6  groupB   
+sample7  groupB   
+sample8  groupB   
+======== =========
+
+Note: No title/header in experiment design file and the first two lines of the experiment design must have keyword "case" and "control"
+
+  ]]>
+  </help>
+  <citations>
+        <citation type="bibtex">
+@misc{QuanTP: A software resource for quantitative proteo-transcriptomic comparative data analysis and informatics,
+    author={Praveen Kumar, Priyabrata Panigrahi, James Johnson, Wanda Weber, Subina Mehta, Ray Sajulga, Caleb Easterly, Brian Crooker, Mohammad Heydarian, Krishanpal Anamika, Timothy Griffin, and Pratik Jagtap},
+    year={2018},
+    title={QuanTP}
+}
+        </citation>
+    </citations>
+</tool>
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/exp_design_file.tabular	Fri Sep 14 12:22:31 2018 -0400
@@ -0,0 +1,6 @@
+case	G1
+control	G2
+D03_01	G1
+D03_02	G1
+M14_01	G2
+M14_02	G2
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.html	Fri Sep 14 12:22:31 2018 -0400
@@ -0,0 +1,155 @@
+<html><head></head><body>
+<h1><u>QuanTP: Association between abundance ratios of transcript and protein</u></h1><hr/>
+ <font><h3>Input data summary</h3></font>
+ <ul>
+ <li>Abbreviations used: PE (Proteome data) and TE (Transcriptome data) </li><br>
+ <li>Input Proteome data dimension (Row Column):  2817  x  5 </li>
+ <li>Input Transcriptome data dimension (Row Column):  2817  x  5 </li></ul><hr/>
+<h3 id=table_of_content>Table of Contents:</h3>
+ <ul>
+ <li><a href=#sample_dist>Sample distribution</a></li>
+ <li><a href=#corr_data>Correlation</a></li>
+ <li><a href=#regression_data>Regression analysis</a></li>
+ <li><a href=#inf_obs>Influential observations</a></li>
+ <li><a href=#cluster_data>Cluster analysis</a></li></ul><hr/>
+<h2 id="sample_dist"><font color=#ff0000>SAMPLE DISTRIBUTION</font></h2>
+<table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">
+ <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>
+ <tr><td align=center> <img src="Box_TE_all_rep.png" width=500 height=500></td>
+<td align=center> <img src="Box_PE_all_rep.png" width=500 height=500></td></tr></table>
+<br><font color="#ff0000"><h3>Sample wise distribution (Box plot) after using  mean  on replicates </h3></font><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>
+ <tr><td align=center> <img src="Box_TE_rep.png" width=500 height=500></td>
+<td align=center> <img src="Box_PE_rep.png" width=500 height=500></td></tr></table>
+<br><font color="#ff0000"><h3>Distribution (Box plot) of log fold change </h3></font><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>
+ <tr><td align=center> <img src="Box_TE.png" width=500 height=500></td>
+<td align=center> <img src="Box_PE.png" width=500 height=500></td></tr></table>
+<br><br><font size=5><b><a href='PE_TE_logfold_pval.txt' target='_blank'>Download the complete fold change data here</a></b></font><br>
+<br><table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Transcript Fold-Change</font></th><th><font color=#ffcc33>Protein Fold-Change</font></th></tr>
+<tr><td align=center> <img src="TE_volcano.png" width=600 height=600></td>
+<td align=center> <img src="PE_volcano.png" width=600 height=600></td></tr></table><br>
+<br><br><table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>PCA plot: Transcriptome data</font></th><th><font color=#ffcc33>PCA plot: Proteome data</font></th></tr>
+ <tr><td align=center> <img src="PCA_TE_all_rep.png" width=500 height=500></td>
+ <td align=center> <img src="PCA_PE_all_rep.png" width=500 height=500></td></tr></table>
+<hr/><h2 id="corr_data"><font color=#ff0000>CORRELATION</font></h2>
+<br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatter plot between Proteome and Transcriptome Abundance</font></th></tr>
+<tr><td align=center> <img src="TE_PE_scatter.png" width=800 height=800></td>
+<tr><td align=center>
+<table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Method 1</font></th><th><font color=#ffcc33>Method 2</font></th><th><font color=#ffcc33>Method 3</font></th></tr>
+<tr><td>Correlation method</td><td> Pearson's product-moment correlation </td><td> Spearman's rank correlation rho </td><td> Kendall's rank correlation tau </td></tr>
+ <tr><td>Correlation coefficient</td><td> 0.1173569 </td><td> 0.1608612 </td><td> 0.1093701 </td></tr>
+</table>
+<font color="red">*Note that <u>correlation</u> is <u>sensitive to outliers</u> in the data. So it is important to analyze outliers/influential observations in the data.<br> Below we use <u>Cook's distance based approach</u> to identify such influential observations.</font>
+</td></table><hr/><h2 id="regression_data"><font color=#ff0000>REGRESSION ANALYSIS</font></h2>
+<font><h3>Linear Regression model fit between Proteome and Transcriptome data</h3></font>
+ <p>Assuming a linear relationship between Proteome and Transcriptome data, we here fit a linear regression model.</p>
+ <table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr>
+<tr><td>Formula</td><td> PE_abundance~TE_abundance </td></tr>
+ <tr><td colspan='2' align='center'> <b>Coefficients</b></td> </tr>
+ <tr><td> (Intercept) </td><td> -0.06910598  (Pvalue: 1.220723e-05 ) </td></tr>
+ <tr><td> TE_abundance </td><td> 0.1712395  (Pvalue: 4.168015e-10 ) </td></tr>
+ <tr><td colspan='2' align='center'> <b>Model parameters</b></td> </tr>
+ <tr><td>Residual standard error</td><td> 0.8363295  ( 2815  degree of freedom)</td></tr>
+ <tr><td>F-statistic</td><td> 39.31142  ( on  1  and   2815  degree of freedom)</td></tr>
+ <tr><td>R-squared</td><td> 0.01377265 </td></tr>
+ <tr><td>Adjusted R-squared</td><td> 0.0134223 </td></tr>
+</table>
+<font color='#ff0000'><h3>Regression and diagnostics plots</h3></font>
+<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "><tr bgcolor="#7a0019"><th> <font color='#ffcc33'><h4>1) <u>Residuals vs Fitted plot</h4></font></u></th>
+ <th><font color=#ffcc33><h4>2) <u>Normal Q-Q plot of residuals</h4></font></u></th></tr>
+<tr><td align=center><img src="PE_TE_lm_1.png" width=600 height=600></td><td align=center><img src="PE_TE_lm_2.png" width=600 height=600></td></tr>
+<tr><td align=center>This plot checks for linear relationship assumptions.<br>If a horizontal line is observed without any distinct patterns, it indicates a linear relationship.</td>
+ <td align=center>This plot checks whether residuals are normally distributed or not.<br>It is good if the residuals points follow the straight dashed line i.e., do not deviate much from dashed line.</td></tr></table>
+<br><h2 id="inf_obs"><font color=#ff0000>Outliers based on the residuals from regression analysis</font></h2>
+<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">
+ <tr bgcolor="#7a0019"><th colspan=2><font color=#ffcc33>Residuals from Regression</font></th></tr>
+ <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr>
+<tr><td>Mean Residual value</td><td> 1.942328e-17 </td></tr>
+ <tr><td>Standard deviation (Residuals)</td><td> 0.836181 </td></tr>
+ <tr><td>Total outliers (Residual value > 2 standard deviation from the mean)</td><td> 164  <font size=4>(<b><a href=PE_TE_outliers_residuals.txt target="_blank">Download these  164  data points with high residual values here</a></b>)</font></td>
+ <tr><td colspan=2 align=center><font size=4>(<b><a href=PE_TE_abundance_residuals.txt target="_blank">Download the complete residuals data here</a></b>)</font></td></td>
+ </table><br><br>
+<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "><tr bgcolor="#7a0019"><th><font color=#ffcc33><h4>3) <u>Residuals vs Leverage plot</h4></font></u></th></tr>
+<tr><td align=center><img src="PE_TE_lm_5.png" width=600 height=600></td></tr>
+<tr><td align=center>This plot is useful to identify any influential cases, that is outliers or extreme values.<br>They might influence the regression results upon inclusion or exclusion from the analysis.</td></tr></table><br>
+<hr/><h2 id="inf_obs"><font color=#ff0000>INFLUENTIAL OBSERVATIONS</font></h2>
+<p><b>Cook's distance</b> computes the influence of each data point/observation on the predicted outcome. i.e. this measures how much the observation is influencing the fitted values.<br>In general use, those observations that have a <b>Cook's distance > than  4  times the mean</b> may be classified as <b>influential.</b></p>
+<img src="PE_TE_lm_cooksd.png" width=800 height=800> <br>In the above plot, observations above red line ( 4  * mean Cook's distance) are influential. Genes that are outliers could be important. These observations influences the correlation values and regression coefficients<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr>
+<tr><td>Mean Cook's distance</td><td> 0.0004875011 </td></tr>
+ <tr><td>Total influential observations (Cook's distance >  4  * mean Cook's distance)</td><td> 115 </td>
+ <tr><td>Observations with Cook's distance <  4  * mean Cook's distance</td><td> 2702 </td>
+ </table><br><br>
+<table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatterplot: Before removal</font></th><th><font color=#ffcc33>Scatterplot: After removal</font></th></tr>
+<tr><td align=center><!--<font color='#ff0000'><h3>Scatter plot between Proteome and Transcriptome Abundance</h3></font>
+--> <img src="TE_PE_scatter.png" width=600 height=600></td>
+<td align=center>
+ <img src="AbundancePlot_scatter_without_outliers.png" width=600 height=600></td></tr>
+<tr><td>
+<table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Method 1</font></th><th><font color=#ffcc33>Method 2</font></th><th><font color=#ffcc33>Method 3</font></th></tr>
+<tr><td>Correlation method</td><td> Pearson's product-moment correlation </td><td> Spearman's rank correlation rho </td><td> Kendall's rank correlation tau </td></tr>
+ <tr><td>Correlation coefficient</td><td> 0.1173569 </td><td> 0.1608612 </td><td> 0.1093701 </td></tr>
+</table>
+</td>
+<td><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Method 1</font></th><th><font color=#ffcc33>Method 2</font></th><th><font color=#ffcc33>Method 3</font></th></tr>
+<tr><td>Correlation method</td><td> Pearson's product-moment correlation </td><td> Spearman's rank correlation rho </td><td> Kendall's rank correlation tau </td></tr>
+ <tr><td>Correlation coefficient</td><td> 0.1334038 </td><td> 0.1611936 </td><td> 0.1082761 </td></tr>
+</table></td></tr></table>
+<br><br><font size=5><b><a href='PE_TE_influential_observation.txt' target='_blank'>Download the complete list of influential observations</a></b></font>&nbsp;&nbsp;&nbsp;&nbsp; <font size=5><b><a href='PE_TE_non_influential_observation.txt' target='_blank'>Download the complete list (After removing influential points)</a></b></font><br>
+ <br><font color="brown"><h4>Top  10  Influential observations (Cook's distance >  4  * mean Cook's distance)</h4></font>
+<table border=1 cellspacing=0 cellpadding=5> <tr bgcolor="#7a0019">
+<th><font color=#ffcc33>Gene</font></th><th><font color=#ffcc33>Protein Log Fold-Change</font></th><th><font color=#ffcc33>Transcript Log Fold-Change</font></th><th><font color=#ffcc33>Cook's Distance</font></th></tr>
+<tr> <td> CATHL2 </td>
+ <td> -1.960863 </td>
+ <td> 4.88565 </td>
+ <td> 0.1432189 </td></tr>
+<tr> <td> CD177 </td>
+ <td> -4.173263 </td>
+ <td> 2.057499 </td>
+ <td> 0.06826605 </td></tr>
+<tr> <td> CATHL1 </td>
+ <td> -0.9912973 </td>
+ <td> 4.835209 </td>
+ <td> 0.05767091 </td></tr>
+<tr> <td> HP </td>
+ <td> 2.570727 </td>
+ <td> 3.885549 </td>
+ <td> 0.04680496 </td></tr>
+<tr> <td> AZU1 </td>
+ <td> -2.226356 </td>
+ <td> -5.561874 </td>
+ <td> 0.03737565 </td></tr>
+<tr> <td> ELANE </td>
+ <td> -2.732479 </td>
+ <td> -2.914936 </td>
+ <td> 0.03266198 </td></tr>
+<tr> <td> PYGM </td>
+ <td> -0.06079228 </td>
+ <td> 6.071712 </td>
+ <td> 0.03242859 </td></tr>
+<tr> <td> LTF </td>
+ <td> -2.4294 </td>
+ <td> 2.129742 </td>
+ <td> 0.02725017 </td></tr>
+<tr> <td> ATP1A2 </td>
+ <td> 0.2871971 </td>
+ <td> 6.446299 </td>
+ <td> 0.01939256 </td></tr>
+<tr> <td> C13H20orf194 </td>
+ <td> -5.640732 </td>
+ <td> -0.6697401 </td>
+ <td> 0.01852927 </td></tr>
+</table><br><br>
+<hr/><h2 id="cluster_data"><font color=#ff0000>CLUSTER ANALYSIS</font></h2>
+<br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Heatmap of PE and TE abundance values (Hierarchical clustering)</font></th><th><font color=#ffcc33>Number of clusters to extract:  5 </font></th></tr>
+<tr><td align=center colspan="2"><img src="PE_TE_heatmap.png" width=800 height=800></td></tr>
+<tr><td colspan="2" align=center><font size=5><a href="PE_TE_hc_clusterpoints.txt" target="_blank"><b>Download the hierarchical cluster list</b></a></font></td></tr></table>
+<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>K-mean clustering</font></th><th><font color=#ffcc33>Number of clusters:  4 </font></th></tr>
+<tr><td colspan="2" align=center><img src="PE_TE_kmeans.png" width=800 height=800></td></tr>
+<tr><td colspan="2" align=center><font size=5><a href="PE_TE_kmeans_clusterpoints.txt" target="_blank"><b>Download the cluster list</b></a></font></td></tr></table><br><hr/>
+<h3>Go To:</h3>
+ <ul>
+ <li><a href=#sample_dist>Sample distribution</a></li>
+ <li><a href=#corr_data>Correlation</a></li>
+ <li><a href=#regression_data>Regression analysis</a></li>
+ <li><a href=#inf_obs>Influential observations</a></li>
+ <li><a href=#cluster_data>Cluster analysis</a></li></ul>
+ <br><a href=#>TOP</a></body></html>
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/protein_data.tabular	Fri Sep 14 12:22:31 2018 -0400
@@ -0,0 +1,2818 @@
+Gene	D03_01	D03_02	M14_01	M14_02
+ELANE	90604000.0	100390000.0	623380000.0	645960000.0
+AGL	976501500.0	960830000.0	697130000.0	719100000.0
+AGK	19878000.0	16422000.0	15565000.0	20390000.0
+HSPA6	67707000.0	176540000.0	117020000.0	121110000.0
+HSPA5	11009000000.0	6568200000.0	4795000000.0	5734100000.0
+HSPA4	117790000.0	30519000.0	90678000.0	87974000.0
+HSPA9	3544300000.0	3817500000.0	2235000000.0	2270700000.0
+HSPA8	7526200000.0	7289900000.0	3520000000.0	4052400000.0
+AGA	240460000.0	291780000.0	221830000.0	173620000.0
+LGALS3	186500000.0	258100000.0	242190000.0	301190000.0
+CSNK2A2	132720000.0	155250000.0	150050000.0	161600000.0
+PMM2	1632800000.0	1802500000.0	1614800000.0	1711000000.0
+PMM1	0.0	10979000.0	8686700.0	6732400.0
+ASS1	18134000000.0	18876000000.0	12966000000.0	12270000000.0
+SPR	1922200000.0	1865500000.0	1698600000.0	1728400000.0
+RNF114	183640000.0	174460000.0	178010000.0	173870000.0
+UCHL3	274130000.0	282050000.0	254500000.0	274000000.0
+ENSBTAG00000047256	57543000.0	71609000.0	71386000.0	61095000.0
+ABCD3	55147000.0	49370000.0	55492000.0	45594000.0
+SCLY	299950000.0	329280000.0	324400000.0	272070000.0
+DHX9	213070000.0	112580000.0	88925000.0	91000000.0
+OSGEP	601030000.0	575840000.0	580200000.0	637320000.0
+NUP98	2508700.0	0.0	6975000.0	9160400.0
+PPP2R2B	29533000.0	27090000.0	35168000.0	28130000.0
+GOLIM4	55757000.0	13447000.0	28969000.0	24689000.0
+ENSBTAG00000039928	20107200000.0	17840800000.0	22329600000.0	21414400000.0
+OPA3	238630000.0	258440000.0	236320000.0	194580000.0
+OPA1	170770000.0	88756000.0	124230000.0	135980000.0
+PIK3C3	624380.0	0.0	0.0	0.0
+GK	79958000.0	91689000.0	55521000.0	67336000.0
+PTRH2	630860000.0	635300000.0	792900000.0	721580000.0
+HNRNPDL	184260000.0	197110000.0	180510000.0	178040000.0
+ENSBTAG00000024233	6670900000.0	7453900000.0	5794600000.0	6289800000.0
+TRAPPC3	350960000.0	386150000.0	288830000.0	254040000.0
+TRAPPC5	110960000.0	91402000.0	94205000.0	99412000.0
+ACLY	121870000.0	11334000.0	52304000.0	57826000.0
+CTBP2	134900000.0	121550000.0	139080000.0	127730000.0
+ITGA9	2480600.0	0.0	0.0	0.0
+ENSBTAG00000021558	0.0	0.0	0.0	0.0
+HACL1	3750000000.0	4002800000.0	4908400000.0	5092300000.0
+ITGA1	1398200.0	0.0	0.0	0.0
+NOP2	4928000.0	0.0	0.0	0.0
+ITGA6	97078000.0	42673000.0	116280000.0	130990000.0
+PDCD10	142700000.0	124020000.0	194810000.0	170930000.0
+ENSBTAG00000014423	100010000.0	99218000.0	191870000.0	184010000.0
+EDC4	10047000.0	6662500.0	0.0	13081000.0
+APOA4	5577600000.0	5684300000.0	2510700000.0	2632000000.0
+APOA5	257940000.0	333300000.0	197140000.0	201230000.0
+UFSP2	8208200.0	6119600.0	0.0	7495400.0
+XPO1	6101300.0	0.0	21163000.0	15852000.0
+CSDE1	6845000.0	10436000.0	29616000.0	35114000.0
+XPO7	1621500.0	0.0	13304000.0	5299000.0
+MRPL28	82108000.0	97797000.0	62910000.0	50735000.0
+TIPRL	223950000.0	199410000.0	192400000.0	118190000.0
+LSS	346220000.0	249770000.0	234680000.0	220640000.0
+PSMG2	66523000.0	96697000.0	25795000.0	29839000.0
+GHDC	24907000.0	27587000.0	38327000.0	43611000.0
+COL4A2	116880000.0	133790000.0	113920000.0	117830000.0
+COL4A1	145000000.0	165870000.0	135390000.0	135500000.0
+MOGAT1	23667000.0	33273000.0	24912000.0	38349000.0
+RBM12B	2074800.0	0.0	2384000.0	3078100.0
+GART	497380000.0	153590000.0	363030000.0	301340000.0
+GARS	14525000.0	0.0	35874000.0	22116000.0
+PSMD14	416870000.0	577850000.0	409550000.0	320620000.0
+ATAD1	429390000.0	455220000.0	383550000.0	484670000.0
+PSMD10	52335000.0	25236000.0	32444000.0	40355000.0
+PSMD13	1450700000.0	1441200000.0	1525200000.0	1463400000.0
+PSMD12	16339000.0	14892000.0	22908000.0	29622000.0
+ENSBTAG00000039321	939690.0	0.0	0.0	9768400.0
+FTCD	2776600000.0	2757000000.0	3259800000.0	3388300000.0
+GIT1	1902600.0	0.0	1368700.0	0.0
+SERBP1	70643000.0	58520000.0	63639000.0	60842000.0
+PPP4C	40084000.0	147770000.0	127090000.0	154860000.0
+OCRL	2204200.0	0.0	2652100.0	0.0
+GANAB	1342100000.0	362290000.0	592930000.0	651410000.0
+NUP133	506790.0	0.0	3742200.0	4045600.0
+LRPAP1	91487000.0	84230000.0	35103000.0	65126000.0
+VAPB	997680000.0	1018000000.0	1556300000.0	1647900000.0
+VAPA	1386600000.0	1518300000.0	1349600000.0	1252800000.0
+SHROOM1	5030600.0	0.0	0.0	7964200.0
+NDUFAF6	42874000.0	55512000.0	57678000.0	38059000.0
+MTIF3	105690000.0	122720000.0	103820000.0	87694000.0
+MIOS	2457000.0	0.0	0.0	0.0
+SNTB2	20388000.0	14076000.0	18128000.0	16156000.0
+IGF2R	27998000.0	5516500.0	2374800.0	0.0
+CTH	409230000.0	431710000.0	319960000.0	371070000.0
+ARL6IP1	21143000.0	75315000.0	28387000.0	107280000.0
+SPART	2390600.0	0.0	0.0	0.0
+ABHD14B	14750000000.0	13104000000.0	19532000000.0	19336000000.0
+NECAP2	15456000.0	16744000.0	49493000.0	30865000.0
+ARF4	121490000.0	160450000.0	88446000.0	64683000.0
+FAHD1	819630000.0	809200000.0	778190000.0	756100000.0
+ENSBTAG00000009622	0.0	0.0	0.0	0.0
+AP3D1	1363000.0	0.0	0.0	0.0
+EIF4B	19344000.0	10687000.0	24139000.0	10856000.0
+ZSWIM2	33653000.0	58920000.0	34138000.0	52336000.0
+ENOPH1	306200000.0	350650000.0	429480000.0	443590000.0
+SULT1A1	40564000000.0	41927000000.0	45069000000.0	47894000000.0
+TCEA1	37826000.0	40868000.0	24264000.0	21920000.0
+COX4I1	158000000.0	167970000.0	170600000.0	185850000.0
+PRKAR1A	31080000.0	32496000.0	43949000.0	55932000.0
+MOB2	48613000.0	49291000.0	43029000.0	46282000.0
+SAR1A	271310000.0	293370000.0	211060000.0	290000000.0
+SAR1B	1734000000.0	1716700000.0	1811800000.0	1703200000.0
+NIPSNAP2	330440000.0	405490000.0	367500000.0	377650000.0
+NIPSNAP1	19276000000.0	18856000000.0	18877000000.0	18937000000.0
+PIPOX	625330000.0	653730000.0	645380000.0	575450000.0
+GAA	63738000.0	62018000.0	44322000.0	49582000.0
+FGL1	493960000.0	569280000.0	681940000.0	617400000.0
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+GSTK1	3823200000.0	3929400000.0	4085200000.0	4575800000.0
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/transcript_data.tabular	Fri Sep 14 12:22:31 2018 -0400
@@ -0,0 +1,2818 @@
+Gene	D03_01	D03_02	M14_01	M14_02
+ELANE	-0.08223053805901	0.128367421476871	0.168575604288361	0.179386061121957
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+AGK	4.96373192231441	4.0307358661387	4.69732878268058	5.64327232883133
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+HSPA5	413.409614153405	438.011930274678	197.673685843884	193.727515486021
+HSPA4	14.8468997976502	15.3061633055397	15.275973226593	14.7835817685183
+HSPA9	170.253077768686	169.323500684362	159.978594056721	161.35877481037
+HSPA8	486.867209766023	498.041303389437	352.185123018411	340.356699934716
+AGA	55.2930712045865	50.7686596245107	69.9129100715734	66.07384169934
+LGALS3	12.3566659422422	12.6797640900226	18.7284481408449	16.497115341329
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+PMM2	21.8385112586243	23.0035148281517	16.003600132562	17.2109074501839
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+ASS1	1516.70059430521	1464.12181445199	987.76849371654	994.644000920779
+SPR	29.8024212677962	31.4255886777817	31.79677566883	28.6670221909796
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+NUDT1	18.8793782493198	18.8527260787607	12.3038809833938	13.2866843020744
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+TK2	33.9979320424689	33.0844481516052	26.6390511054135	32.643749648284
+IGFBP7	96.1715883245993	96.9849127734383	73.8446854539096	80.1413700430556
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+LARS	11.2227190970869	11.5871577365339	12.621647507795	13.8301461988261
+PROZ	116.809886640552	104.858776046389	117.957069400365	117.529049770823
+CYP8B1	412.065295525826	424.999207684934	464.440371255463	456.332400512045
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+ATP5F1B	513.546065335708	528.33262563355	387.362696330798	388.315109609166
+ATP5F1C	165.22570160091	163.302687551094	132.846351531124	127.31907389261
+CYP27A1	330.369783913126	310.29234287436	349.399104678752	343.446147391654
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+PROC	257.135492596388	250.759657656163	324.180045008162	321.635705884631
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+PRELP	2.26786018926251	1.96606581443142	1.68936641506156	1.76613680814355
+TAGLN	32.099452031918	29.1484424074593	17.5917391803785	16.509390173962
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+MPO	0.638537569145182	1.6048407108389	0.776611274284478	0.609909899586089
+MTTP	106.952506694673	106.604276279429	71.9439708477298	67.7793469094763
+AIFM2	17.4423658636693	18.1709026898264	12.5515593645162	12.8320996750878
+EBF3	0.105236049814663	0.545542665262452	0.135058347300121	0.267658174938101
+AIFM1	79.0677093147206	79.2641055553824	62.6734336633198	66.9346848592757
+AGO1	4.82205265695339	4.6541461135091	5.15318374644362	3.72644594375745
+A1CF	39.6551599778483	34.8880001594435	56.0678383295113	53.4949335447586
+AGO2	4.57843423254205	5.06374578977108	4.87232199665973	4.39429112233587
+GRPEL1	52.1368211193569	56.8334213812845	41.3282000306447	40.3287425691148
+GAMT	662.985056779826	592.468517715923	762.581010565525	747.642269332419
+VBP1	30.2687663583794	31.3624446313768	30.1849557753635	33.7541099629333
+FAM120B	3.75800869902227	3.85112316196843	5.69850299555534	5.34797934175275
+FAM120C	5.07031880992206	4.11046302992319	5.40716488727401	4.20957181745709
+LAP3	105.119749606569	108.211017385905	101.19351292897	104.685754505682
+FAM120A	35.4588492990109	37.1603461774973	31.7367172020692	30.16660675632
+MAPK14	22.7956165603384	21.9694793577163	23.8655944228792	24.4572592344419
+CD302	68.2378289404061	74.755195136137	139.84932508478	136.189014398459
+GADL1	-0.0758996600312405	0.135819323886117	-0.00624862361024775	0.00569716975406065
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+AKR1B10	19.24486696767	18.9340282995295	52.3117831932445	48.4616353939086
+OGFOD3	12.901530611645	12.5200708738938	11.9865471532262	9.82260308949049
+IQUB	0.00402581315982881	0.130551186661855	-0.0015762168067058	0.163005705537859
+PM20D1	65.2139899160153	64.1278109242474	54.9867302077062	58.2476887933487
+CACNB1	0.722452572994927	1.27686952110553	1.10904994126225	0.895125405243225
+REPS1	11.0862189497719	10.92062438615	11.4509555556661	11.4841834076827
+ACMSD	154.468880362731	153.477281319784	54.2309248175533	55.112044338842
+NECTIN2	72.1444960153682	70.036211875307	69.0771971851763	67.0017480975632
+NEURL1	0.621756635062024	0.539265314273631	1.10629701361336	1.28538292732688
+BCNT2	27.1162704711526	25.2898187580256	35.8024982217088	34.892318768884
+UGT2B10	69.0900923220943	72.903006958683	45.3353525022334	42.4101598625752
+GPN1	10.5427632921661	12.5847083350205	15.5297833580984	13.8273108856277
+TRIM32	8.14816919344869	6.88474382428201	5.60153763952083	5.93532810369625
+S100A9	5.64056218152131	6.77578612282133	2.03470144737551	2.61471102953769
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+DNPEP	39.1460493286512	36.3399231108868	31.9352201938593	36.3646146399634
+RAP2C	35.2619300801374	33.1605461501772	60.3754221772593	60.0628661879753
+OLFML3	19.7915830847905	20.8743221262631	22.2022161051959	24.5536494315336
+FAM49A	18.7937695205866	18.8163081726435	21.1226896212602	18.1786365115365
+FAM49B	23.575391225476	18.9728664628644	28.5359265423879	33.4361731851645
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+USO1	42.7714455540291	47.3149698623466	43.7967294804329	46.2263769146741
+PLCD1	5.98067498812222	5.96451651421754	4.24242057083689	5.47422033864012
+ADO	7.97454918320147	5.9513471127042	5.2825464211403	5.67628502544168
+ADK	191.390449533644	206.998058276329	236.563198952226	235.115068841514
+HIST1H1C	5.9217792738612	4.95524693721065	3.08599686608936	2.93740002519881
+ADH6	1105.43081797987	1116.25050983947	1266.06451985291	1251.73708446904
+ADH5	174.76512208266	185.888757103885	171.288369593734	175.091193938471
+ADH4	627.747730143551	665.047237057203	974.081942508456	961.472166593059
+TACC2	0.770287877312979	0.684999726390006	0.35643197729882	0.296964381205269
+ENSBTAG00000046172	106.818184222524	111.685602371835	183.042220193776	183.496149676734
+SQOR	87.7011716232612	87.3282444680352	81.5827142810118	81.8866299413566
+PRKACA	29.5060980856036	27.9793635411781	31.8585724658279	32.6768098867138
+PSMB9	15.9025727904202	14.4797083105454	21.7870823605466	16.800394484726
+PRKACB	11.5368562072577	12.2495657531745	17.036479975094	16.3751071270207
+PSMB7	133.75341334523	127.51549048811	113.975164470229	118.59431503862
+PSMB6	113.535773436955	121.379826981675	102.487633671618	103.578500276761
+PSMB5	67.6175240591193	78.6933239697642	60.1992832968828	61.1840823463543
+PSMB4	157.511104185295	152.768294949916	143.155835016009	137.435626255964
+PSMB3	105.742052116652	92.0603001724571	93.3708644729009	95.7050555465877
+PSMB2	123.457762308973	129.473826287437	116.664015972097	117.876040621308
+PSMB1	145.740161313837	141.687848524893	145.508168235908	150.388254356633
+HDHD3	35.9719533342262	35.4537406248179	36.8845295020537	36.9346573441944
+HDHD2	12.3029949121922	12.1217706321042	14.456566910412	13.9822447076885
+CAB39	16.6656839740857	18.8139458413882	17.4463282420011	18.0365077258724
+HDLBP	99.2168803166131	92.2345142229552	67.000123067042	65.5173770809122
+SERPINA3	801.220767771298	803.401401930203	633.71608220141	643.908202017346
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+CORO7	3.0891970219448	2.81317627439183	2.83680531744018	2.61290698816854
+BCL2L13	50.6869633684315	48.5132932256727	30.8973674153838	31.1988647512833
+HADHB	276.497268041029	290.311923822831	252.75831039752	256.299083958476
+ENSBTAG00000046008	1.38094178205221	0.840426513979766	1.57904220415248	0.085804335057465
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+SAE1	23.5463571125196	28.5815712111712	21.5115557599852	21.2545662398848
+NUP43	5.20859465265674	5.50868915258357	6.34510652934563	5.67647582555492
+STBD1	131.775625930627	148.520648358928	185.709447638873	183.471012989348
+IDNK	154.566391641516	166.213443274996	214.124991055638	192.598325547537
+ADRM1	52.3617709637931	43.5686668195816	43.7949505927479	42.2439074597917
+HSD11B1	582.464906368977	615.231975756486	903.396755604835	913.099744333952
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+RMDN2	52.2186300421565	68.0383837700869	113.26462341627	114.71846490206
+PGRMC2	124.391304672086	134.20384464969	191.065494933781	187.035785454001
+PGRMC1	283.032304612449	306.822427791502	403.905526236531	409.085608539176
+RMDN1	40.6497431302991	38.7777801905708	49.3162426777265	44.5241622205645
+HOOK3	25.4922185969054	26.5875028080602	30.4837901483831	31.9271816640764
+NUP160	5.44806262951116	7.3309970552893	7.62475252103354	7.78738925391181
+HOOK1	6.53856480972397	5.85913656015185	11.7737274045939	9.92077459520354
+TMPRSS6	82.1339306719672	74.7704335353574	91.260050162965	94.2966731132863
+CRYM	7.07309792439572	6.89898178087216	5.10337147998345	5.32079976827509
+ESD	75.6442538453343	83.3345118532909	87.4341711118774	85.8795136051105
+ADHFE1	42.6363219154336	38.0024047832191	65.3554079929732	65.808507216216
+LRRC57	7.10907325443469	7.84034005052354	7.44318522111976	8.18365542362289
+LRRC59	35.6563096465125	34.1620778773981	29.7593149806825	25.9955727305129
+ABCC10	2.47236758427963	2.72568275498941	2.14127736590441	2.41650266717104
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+COL14A1	10.6822852963128	10.9464837673729	14.6873610110264	13.8831736209655
+RPL30	761.559800967068	741.768290192404	721.083764661762	704.659336934124
+GNA14	14.1090563149206	13.0636652279344	13.7822098527049	14.0784654205362
+GNA11	23.3086519549469	19.7505644650818	16.3744419087467	16.9182240811841
+SDHA	342.895721620255	328.645028155033	270.644392605132	271.563817742724
+ENSBTAG00000030683	4201.19554469149	4486.69776611606	4712.05225597502	4684.59907221519
+SDHB	240.2926032366	238.48797808147	219.156895752741	218.100336343922
+PPCS	12.0715340682994	12.4629526563177	16.4991276565095	14.3552054709709
+SMARCC2	13.4757062113739	13.0785707339008	13.779148869123	14.2056171389136
+MOV10	21.3615481424063	19.4542587631743	19.9166720442971	19.8599920845002
+AP1M1	29.183002953371	28.4627321390151	34.7620874939573	36.4914966220525
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+MOCS3	64.3243285933029	68.620939433399	83.9104604269488	79.9182065177737
+MOCS2	73.5970258125575	78.3834013340148	84.7420648538138	83.2626861669147
+MOCS1	61.2719436983883	54.4532836132022	59.3588543754456	57.3641808320027
+DBN1	7.62062667354876	9.42531076114656	3.93402127945625	4.10000182254912
+EEF2K	40.1691060165791	37.2196537110254	34.7725108011912	33.2447300554713
+ENSBTAG00000017938	10.4744885422073	10.3479477514345	12.0141035629637	11.0443097772425
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+BLOC1S3	4.08576563460671	3.00180422321126	3.00658915148632	4.8083988238156
+ENSBTAG00000039356	7.30691844591151	6.55009685047002	16.3881140889242	11.9274098108731
+COMMD2	5.38576040064327	5.35250177679201	7.20317757835002	5.93599951242827
+COMMD1	20.3735454782713	17.9100258321285	17.1067247381315	22.1357017731009
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+COMMD5	14.9729052768571	12.0119703280403	10.1518732721567	10.6240689329861
+FAM114A1	10.9614110026776	10.6768022497942	14.9010459406044	12.1095165055568
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+METTL13	4.66237668907224	4.45263582848967	4.22166534269016	3.34234547634677
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+MTUS2	4.31718926531321	5.54641885151013	7.38959579507357	6.88167947570886
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+ENSBTAG00000032350	814.833615049731	862.160755335367	834.393616949251	846.807463464239
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+UBE4B	14.0926461344646	12.3794305253518	14.9016206806154	14.6225200903442
+UBE4A	28.4702742009415	31.1338448614896	31.9628644503406	35.8269852048431
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+TUBB4B	112.093821930856	102.462282787532	44.1243213418541	43.9310024417296
+TUBB4A	217.162920683109	199.26521356582	116.15526810975	108.810364912596
+AKAP9	7.4913571170672	8.7323281105844	8.85776793859657	9.42303492817262
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+ENSBTAG00000048013	331.754579222598	359.827410382149	304.035714488791	310.334461003324
+STARD4	101.605810464958	115.698789576689	90.803439582177	93.4816079378502
+STARD5	20.3422620375773	20.4404606723093	16.5704862916314	18.8236605467884
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+ENSBTAG00000047632	0.0768529813312971	0.364114005526894	0.363463933908203	0.930332115479812
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+NTN4	0.437671141825327	0.49257043239633	0.389281370404683	0.254815966281363
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+TANGO2	14.2002202152655	14.2006518370555	22.39373419956	14.0855878751688
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+STIP1	43.8469359741177	42.760932885763	29.9000390318798	29.6895986042318
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+PIKFYVE	2.9653036010696	2.59690924386061	3.75056548627301	3.64681689681478
+PEPD	56.2130501761185	53.9755344052818	56.2522569559011	58.0814672888588
+AHSA1	51.4445821432798	50.8848861521773	44.7811816147132	44.6500708064545
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+MRI1	10.8976568071377	9.11153183309112	9.67317602882818	12.0588202970203
+GTF2I	34.1177433031055	36.3557636248256	37.9522942584999	38.9292289822082
+ATP1A2	-0.0349324249447941	0.134890184156165	-0.00542455267001526	0.00657082332543535
+NMRAL1	62.4420154252066	57.2880298346563	51.4896876139228	52.6963777765173
+UCK1	10.6189688597911	7.62972382477382	8.0676926243714	7.76291448538602
+PRPF39	13.4955095592332	15.5856609733065	14.9433242898817	14.8182053536539
+DSP	23.2482415933666	22.5945403700082	27.2420982167781	26.5315189009892
+SAMM50	47.1562419350304	44.4575168328968	54.5163783747121	50.1480343496199
+RNF40	8.39437492670073	7.41720397857919	8.75947234367798	7.43479091332121
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+DNAJC9	6.9674834314421	8.60217897859582	11.365013964634	9.59291442335075
+GLYATL3	88.3069651281587	96.3898545694085	166.25009894012	171.481526558662
+ENSBTAG00000023978	3.94359952176421	1.99618469037055	1.96462804870536	2.70838143219408
+DNAJC3	81.4550274854325	92.6973842945913	76.6373671543527	78.099066125512
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+ECHDC3	86.7908655131638	78.8997697344205	143.794159305775	156.892095646374
+ECHDC2	108.868406937957	107.219721131948	137.233129511055	139.514787955081
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+ACP1	44.3739721220789	42.0193985682398	45.7929408149619	45.5636851062907
+RAP1B	31.9646413856528	32.8393258833764	45.9772741268579	46.4082042485821
+RAP1A	51.9239423204728	64.3117082817351	71.8486976076003	70.9906569388079
+ENSBTAG00000016551	11.2351360898322	12.4023476480082	12.2812090084989	13.9183912973504
+ACO1	83.0880251448824	86.2651542051764	93.381209742703	95.4248823149936
+CHDH	19.0566218189403	17.7467915147982	17.9703360727163	16.6044094046569
+RHOA	177.382073759642	171.018922395861	189.297872811329	181.368605583589
+NCOA2	11.40396876407	10.2829911201532	12.9978790945243	14.1935224099012
+ACO2	64.7859019499235	66.3275466812052	55.0700701053573	55.0120885074504
+SPCS2	126.545530609058	141.751704078963	135.340268256128	128.916427250438
+FAM91A1	24.4433677061076	24.7884717307621	26.3538034565623	27.0748405477942
+NT5C3A	15.1841115005913	15.5858111069821	22.2490253608455	20.7390259403289
+ACAT1	489.602002890684	519.86938801814	420.149296258797	425.228381256268
+ACAT2	195.308056900315	191.042368742265	120.170851890242	126.558366653556
+CXHXorf38	3.97441389177732	4.1959472931092	3.31802592472746	4.51454285172491
+F11R	82.6984484302132	75.9448090710253	72.5316115639844	72.8997309283194
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+EPN3	0.680695611362103	0.915719765958501	0.538524748572583	0.657401345415665
+DDT	456.140859335508	461.076431482139	405.842447579189	390.619258078315
+MPP1	56.1575732443173	53.7773195522338	65.6345620265985	67.6262565752931
+GIMAP8	52.418621261932	53.5244731580421	71.4533196205006	72.8217369076633
+GIMAP4	163.902925354278	168.039801817497	224.586579564619	220.650844298731
+GIMAP5	10.8084860752459	14.1655143924013	15.3592837801596	17.590673367266
+GIMAP7	34.1417077540259	36.2692955857571	55.3327157597768	54.9171481987596
+GPHN	12.4854530498343	16.9456536003351	15.3482134939232	14.8134055966408
+LMNB2	9.80591161629618	10.5376288141908	5.9340962168183	6.14977561656177
+ENSBTAG00000010033	18.5048225333198	23.8293074774158	26.5203258090674	25.4809726398062
+ALDH4A1	188.764128310987	181.584121100204	133.63064647398	133.58536307802
+GSTT2	154.530175778565	137.774162961773	97.4911934420664	98.1815662569734
+TOLLIP	63.3779746794835	55.1006853087437	46.579907518088	45.1233813394448
+MRPS26	39.5272673178502	38.4483629119456	29.5747001896468	35.6317774339382
+WARS	14.6751388423804	17.0370705187884	22.5425859750506	20.2102265355975
+MRPS25	29.7546221628323	23.9383836107144	24.1949673899487	19.0223732526572
+MRPS22	24.786736836429	24.6735752844437	23.8307250914322	20.6999627371634
+MRPS23	83.3850122660442	75.4382114559938	76.5610296579299	74.5611235724088
+LAMTOR1	35.0355120509502	36.8403600157567	38.0416189906403	41.7588875256681
+LCMT1	18.5278231988461	19.9963998497432	25.8536833206248	24.4957414935736
+RASIP1	6.79255980027564	6.87837005557046	4.97674559956816	4.85387096865022
+EARS2	0.971824474837355	1.57246484781682	2.27138088185709	2.00642086729512
+ACTC1	-0.0758996600312405	0.135819323886117	-0.00624862361024775	0.00569716975406065
+NAMPT	38.9589035095105	40.6541827038555	50.8852874879381	48.7885576753385
+ENSBTAG00000016032	31.2444465380257	35.8225260337061	50.0791569567943	46.7631583278899
+CTNND1	27.8174111805192	25.4189638501312	25.2318638913447	25.7602638074172
+GAREM1	5.22199228605201	5.4510134708088	5.07878651936323	4.03796223722093
+SH3GL1	23.3422689331258	21.5583363400783	14.4490415710475	16.0602045999279
+A2M	157.958754224679	161.866071725067	342.903703376926	338.436076079355
+OPTN	66.7039529250351	73.9532103765072	51.4290251578203	51.8825380573127
+BHMT	755.459028063828	762.088905266398	485.905844829927	490.335617971772
+THOP1	85.483907005801	79.1738854028349	105.177269723523	100.760817128123
+GOLGA2	15.3003897967169	13.8613345376293	15.0628470696857	14.3812467148659
+TTC24	-0.0758996600312405	0.135819323886117	-0.00624862361024775	0.00569716975406065
+FTL	4700.5375644698	4672.76061765461	4015.72534841193	3965.60362753349
+VARS2	5.5028278873002	7.12317835198444	4.55554038456043	4.92849262721275
+FHL1	4.31513962277386	4.96225267628503	1.75778521280258	1.77499844153788
+DCXR	300.055572439801	305.504028399992	265.759837958271	257.939262985731
+FTH1	2640.39841870471	2507.39669726747	3492.96774687286	3453.91687615061
+UGGT2	0.0498156499183176	0.253699590689055	0.203844394056743	0.242667698355064
+UGGT1	12.737230703257	12.8531792017474	15.803340262683	15.4492456548219
+RPS25	1262.26949165852	1297.5898683836	1253.54623373784	1227.30818522197
+APPL2	18.8551603324056	18.4594051613982	23.7146040847652	23.4646589823115
+ALDH7A1	53.0222837285836	50.6601879329357	61.8291588180666	59.3779554804581
+TOMM70	34.1711637691063	36.9920653244559	40.186834921149	43.6545574467661
+MEMO1	18.9079198181827	24.1440961995689	28.3763486785549	24.1338529488712
+NT5DC1	35.6134514901466	38.0223576878052	32.9657671254383	35.9204601974167
+PPIF	97.7068337839493	89.1364178211414	113.612052135248	125.348787526871
+PPIE	6.25929821131159	5.31403458478402	6.58224400333557	6.07569629604576
+PPID	35.1210950899941	28.2607017941829	32.5234580812798	31.7466853089409
+SIGMAR1	77.6520527205585	76.6092865671267	38.5977533253211	42.8456485156958
+PPIA	1034.35019844318	1020.13434868876	772.515482349054	751.145693114705
+SARS2	11.0916480960254	10.1542241810923	9.1588015156475	8.78322244764339
+SIRT3	22.7674286556364	18.8961932997058	27.7276111237912	27.3390030133762
+CYFIP1	24.5200446139301	22.1631199917117	29.6515214186433	28.4842227438395
+SNTB1	59.5404491323339	51.5312835067093	53.2925441809757	51.6032913842737
+HYI	40.2585599045089	33.2577691166209	50.4552498225121	46.3187319217738
+AIMP2	13.501989372869	13.0711861031751	10.1455379093704	11.6298317970372
+KIAA0408	0.265486069753887	0.241814480262823	0.455131303501472	0.19204910332876
+AIMP1	37.1971494618183	39.8419793191808	43.6164207792951	42.4922174549254
+CORO1B	42.558050898828	39.6751337369581	33.1869194532228	33.4859668310759
+CORO1C	25.2422522369417	26.6684375155322	19.7530171077342	18.9690651402694
+CORO1A	17.7215135762406	14.4539449859356	23.1897048067267	22.348632948456
+ZBTB8OS	32.3762631477558	47.9720890550815	42.1564576982225	38.5151665343183
+MDH2	221.170772581968	230.79241300938	162.314805032393	166.060620496365
+MDH1	112.107013928413	116.459303263565	69.3533046399373	69.2391687438853
+MOXD1	76.4649234720073	72.0721712445999	102.374107442164	98.3444023563963
+DDX3X	73.9263403987162	71.6539543506195	91.8204247450065	89.7690814721301
+TTPAL	4.1898077270929	4.02096272999204	5.34221934771448	5.93404543161027
+DCPS	3.48004029602297	3.97528538819521	3.24305467732726	3.44679457011092
+THUMPD3	23.7645238346924	24.2273848504228	23.4233110605197	21.9419957630896
+KYNU	40.460266630843	43.3559778114317	67.4912355059149	68.5188669824548
+LHPP	49.5384774706164	43.6060232463584	54.177050070137	48.9653163756007
+UVRAG	7.10645035697105	7.83621349044726	8.54626392671575	7.98419962513033
+SMARCA5	23.2521482729566	23.4351497927815	30.8226224950681	29.719787877834
+FAH	262.588648852183	254.328408913691	332.60223579053	322.934341527019
+COPG2	3.6941803903453	4.89291097917079	6.56985842847109	6.41259856176545
+HNRNPA2B1	350.441670238391	379.307631535797	344.743800118154	339.154002032841
+COPG1	53.2807286570002	49.7338941279748	45.838441949253	43.3969123831918
+CYP4A22	2.46753530422916	2.07474869786093	2.60767796403849	1.75003352368046
+NBAS	5.41598821324479	4.97331838195157	4.68955496059768	5.305396956002
+PDZK1	132.835872270642	142.378934621004	143.05088005242	144.255752391207
+RENBP	20.1291969648876	19.9718651384863	19.9883891991432	15.1708745899588
+ENSBTAG00000001851	4.96888983574246	3.92919420355106	12.5421033267194	11.9316246842058
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+GTPBP1	13.0583846982455	12.0103235771002	9.72315354395715	9.78182285418381
+NIF3L1	17.1351945260441	19.3420861315404	21.8143344631773	21.5899098913782
+GTPBP3	8.78793542825082	9.81751826284006	8.99460008887479	7.18157632632933
+GTPBP2	7.71415311926382	6.88755073988943	4.37972235698723	5.06136072599241
+HELLS	3.07584724189016	3.38195802124624	2.98131060368764	3.07404164111412
+CASP6	30.103493119971	25.0565260097277	39.0410858686213	40.9377075861795
+UPP2	75.4060996877082	82.0654953624369	111.966040790801	115.545280159526
+SNRNP40	13.0399665224397	12.6968421234781	12.2138786617923	13.4498183133513
+BPNT1	20.0910266148906	19.9066516511147	20.7471005387983	22.3814306892582
+SHTN1	21.5464549400895	22.3440754973299	27.6059257546359	28.3254405724441
+ENSBTAG00000031738	0.734573971264471	0.117437725779074	0.0100543503631274	0.0229810596966554
+CASP8	10.658798438012	12.6455137866218	16.3874406215285	15.429300326113
+PURB	5.75048191983693	2.33572988678973	6.04701346675588	5.52013714925041
+PURA	3.65174795764468	7.70207283455392	7.67111405081849	5.74723893972894
+CFDP2	40.1525945258458	45.311133940249	44.8061005638147	45.6548763883384
+EIF4A2	188.050223012901	209.774181287971	294.522804066963	295.815543757548
+PICALM	147.16771180242	150.396521745931	162.835437194455	164.723045292576
+HECTD3	14.3189924343358	12.4683965744063	12.2809208337099	12.2938148883325
+HECTD1	26.372651217564	26.3468331929756	26.5393070552693	26.6330830760713
+ADPRHL2	9.39752883549689	8.8303290686298	7.07432691653351	6.04042756161582
+MAN2B1	72.4700051846057	70.131726460536	113.608472124417	116.791477649484
+DARS	38.7224080599416	34.3533605383033	37.8409715208489	40.8180094012176
+LYPLA1	93.4001823759454	93.7952703532382	110.207003230437	110.723737076862
+NPEPL1	11.1632999525471	11.411934144719	8.63404108137208	7.81197403479475
+CLMN	12.5467232858162	10.4012280846921	14.2745285958042	16.1101636493898
+CCS	19.2485876030541	17.2478068892439	14.486814840687	13.9066399867751
+GLRX3	51.3850823935252	47.6172579509854	59.4405830022299	51.8390756843507
+AKT2	39.9786849937726	35.9228388330875	40.9413837100873	41.1320350111131
+LRFN4	5.34899711497463	4.25752145132734	2.66538471320064	2.45874014925214
+UPB1	137.978978939196	117.601515985786	180.572225672557	179.599232945968
+MRC1	28.4250340467392	28.2415990516146	54.7160669245901	53.6058504720389
+AHCYL2	3.14236397784828	3.2466131871954	3.44395538021302	2.92255430767174
+STUB1	68.7602393743469	60.2286653440648	53.8368877804345	56.9809862383841
+GRK2	15.4424443741516	12.4465089076506	14.6096205697856	14.6979459524562
+OTC	138.466156338252	157.57799348469	273.808880613112	270.052942255212
+CAPN5	21.2091673020992	18.8875021284175	9.46963318385199	10.5951609536104
+FLII	16.1000398425055	15.977897746186	13.2168400146394	13.9640154014491
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+CAPN1	13.0308394293735	13.7726319291778	11.9358814901519	11.7901864237316
+PIR	15.2946068503444	15.9577617737012	14.4557060586665	12.2676571541403
+SEPT11	12.1716981005475	11.5179918501659	8.8053138616636	9.01664543880227
+SEPT10	33.3858108594322	33.6869348724123	28.7685437918761	26.4009442263406
+ENTPD5	295.080059799864	294.350284733747	223.765640687606	218.668397525718
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+COG3	11.697956701029	13.9581192902399	10.8186555200671	10.7493535362385
+SMS	57.4371276473835	61.9864574684399	74.5484192211007	74.2507461783843
+P4HB	1447.78091838092	1399.47746106023	598.515886351566	615.429739393368
+CHTOP	43.309920545955	40.467826724763	51.2290345038511	49.4078965132042
+COG7	7.17257278743795	6.11541636546198	6.73774243610311	6.43388202282192
+SARAF	185.583976326125	191.213173803603	177.075509770733	179.809417970622
+ENTPD8	72.6921074732973	64.6388182353148	58.2568482012416	63.1454014347341
+OSTF1	38.4311153166049	36.4337419554113	39.4525968704377	37.2159956043741
+SERPINB1	9.70591634395183	12.9350331288031	15.2544376665142	18.9272188352807
+SERPINB2	3.36774443407445	3.97574712494179	4.71205691991618	4.58658322589248
+SERPINB4	-0.079047357682769	0.132114255551186	-0.0029625310582175	0.175690979646858
+SERPINB6	162.369611934834	161.940232439158	183.684335742308	180.968287661977
+NFS1	30.9683740880421	29.2402474692227	28.4601785077488	31.7671330363925
+ENSBTAG00000048257	0.559431411320046	0.390026669491505	0.263614598678864	0.275281043168892
+UBC	1306.11381614139	1228.08585929291	1045.93308779187	1039.44809370649
+ELP2	17.5944837571123	14.7766978755244	18.2182386109071	16.2965752014127
+ELP1	73.2041064135595	80.7819713180969	56.2647918431716	54.8709648767207
+SPC24	86.1397068760754	72.7344787397279	20.3332128267164	17.3105536637349
+NRDC	22.1661984379327	24.1618529101052	24.5454870617835	22.6647869248971
+METAP1	5.17419288094435	5.99570353591726	7.07168660528538	7.59782938588372
+DES	5.26185003078461	5.56901216174059	6.36068772714828	7.13056794448223
+MSH2	6.64893482014723	7.30223379281484	5.73180848539311	6.43761531104763
+DCTN2	80.2842285464399	74.9257488465867	79.3876186822184	77.8795203321137
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+SPRYD4	23.0067002331194	19.1468818185694	22.2044904337328	22.692682989685
+ST13	87.0669283980403	91.5086119883478	91.6834698239056	91.9448474108569
+PTGFRN	5.73416939018536	5.03098184103303	9.06727716933296	8.63705450374878
+PLXNB2	48.1564434456056	43.4556075122238	46.1731564130092	44.9997823336943
+TRIM56	2.74196888595672	3.22997464425685	3.88701020634711	2.65570302490392
+TCTN3	1.37332802360419	2.80599521456911	2.59419533594251	2.29494403644991
+DDX6	27.0966481220196	29.3211086323724	34.9288770618356	34.467821336165
+ENSBTAG00000022590	76.7461540081417	79.9509449949744	100.880974689103	103.339624303716
+HDDC3	13.1484680090362	13.0004662107067	15.8692797328677	16.7209377799052
+CMAS	67.6653261821093	76.8772043563647	60.1290182027096	61.8646415071146
+LANCL1	13.7590502930066	14.1162526414281	17.8112926819781	18.0975442325741
+PCBP2	286.876453642224	287.232570350163	297.36452601442	289.712150031604
+PCBP1	195.593312761682	183.448512196927	171.049128586068	171.065887188144
+GBA3	119.667137044925	128.548174445968	199.465584574088	188.450597724014
+NPEPPS	13.3064761799487	14.9107892176481	12.6302260365787	12.0646615424779
+AMT	39.578926327583	35.4897873667742	23.1374715107363	23.6069621857005
+SMC1A	14.4820471921868	17.0341152087521	18.7425067260268	17.9399727555068
+SIRT2	90.9545287654828	78.5697906924449	77.3881523531207	75.3460744535111
+RAB7A	97.6443843580372	79.0139903102592	88.8245674864513	86.3231924596
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+GNPNAT1	28.789991598033	29.7986842095603	24.1037290013149	24.9579941692512
+SIRT5	29.8358541205021	29.4789259254726	32.8573233601972	32.743199431297
+NDUFV2	116.507947118683	127.593259652414	93.8320711894945	98.4922249395658
+SGTA	27.9843254888222	27.3284315762744	27.1685366631665	26.0608612474229
+TMEM11	13.7691597725947	11.8109772587446	11.3788685462792	11.1743267586244
+TRIM47	9.65115652325246	10.4699729838649	8.09976244810246	7.55098511026538
+PLEK	30.8323882786404	30.4111674296428	57.2282908228883	58.5159295875576
+KIF3A	4.89786567524035	4.28368246721868	3.49175850451969	4.60957602997618
+PEX1	15.4544197838958	16.0244906457564	18.9561782921643	18.2255231472698
+HADH	210.689026188234	217.9191795525	249.708589160134	254.125083762801
+PEX5	32.1369626101149	31.9458648987082	29.1619532182806	29.7444510949946
+PEX6	11.5789877435837	11.7746629922933	12.4246599050693	10.8149484928433
+TKFC	113.413234004007	106.441641387868	113.764144436118	114.423299096433
+NWD2	0.0087352876352545	0.164049820185391	0.0231257392180691	0.00873832802978505
+RBM25	15.1477331274945	16.1323097339573	18.1302063803734	20.502739073014
+PPP2R1A	52.6290620342151	52.3267720036388	44.4479961627477	43.07173459743
+ENSBTAG00000012692	0.763230968602553	0.363473722405054	2.21189803338214	1.50967310374531
+ATP5S	9.52186499214402	8.37182392681633	10.8649920755077	10.750845107456
+ENSBTAG00000026812	0.216520059278631	0.599221559684165	0.107533575916178	0.119803628735598
+STAMBP	6.82228858025201	11.202332123536	10.3726215272431	11.6931646522005
+NEB	-0.0486945133955257	0.175696734303736	0.0326596235567223	0.0384734738321251
+APOOL	19.9277057841037	25.1628668643323	21.6014542614713	22.2459889042822
+HNRNPH1	159.531708330291	172.906314087914	185.661697361728	181.74399171698
+HNRNPH3	46.9982939180379	46.2490424151402	55.050384311013	52.1387804290025
+HNRNPH2	33.0032853362216	28.8578692242181	31.6099306421129	32.933576088082
+PLRG1	22.269317819574	23.0818556408505	22.2341588839468	19.4486314912393
+GNAI2	49.3856108025701	48.1374939930287	49.4805540435982	45.4799019776422
+GNAI3	39.3498009708115	38.6076017598183	40.8273707028315	40.9975692158404
+GNAI1	5.38717079521801	6.55105101163798	5.7904173013258	5.74007366365365
+MRPS34	52.705045705434	45.7650214449315	45.3620274487769	43.1297154798177
+AFMID	14.220222829881	19.8597985643439	37.6868628464579	38.366878278672
+MRPS30	11.7492006541413	14.1029736098962	13.274926142087	15.6184397328951
+HSPA12B	2.9030654336603	3.77027914129879	1.80896022062297	2.09210800717526
+STRN3	8.13498273725566	9.03287948300691	9.96295487170499	9.74576994193983
+ABHD16A	25.3054122555458	26.233175139912	26.234971162757	24.540897169109
+MTHFR	43.7095678334941	40.3222262829902	34.7976976778121	30.7237438948094
+MTHFS	70.3833137291695	70.679509811558	102.556433076768	100.724993245525
+PDSS2	5.25999828472556	5.84962797660508	8.22558650975668	7.80933981180359
+DTNB	27.4313027239405	26.4798904549263	28.3829068441411	27.5076278153581
+VCAM1	13.680654708315	17.3293688002081	38.4925311352473	37.6009766180847
+PREP	15.4744582349564	16.4324443864566	16.7954108709843	15.594271211927
+SYNE1	11.0916406094501	9.21704055885757	16.3298111824976	15.1668017273714
+SYNE3	3.4446382739788	4.01611649224488	3.8549713323667	4.07364934958814
+GOPC	6.33820876794039	7.8874717583141	12.4015336870358	12.6362543998745
+HNRNPUL1	42.40032600177	38.6102688921157	40.5058304569221	39.0894072854499
+YTHDC1	21.5377532702991	23.7265581376159	32.8803561238731	30.9353196589496
+PCMT1	41.61148897908	37.4395975309916	35.7497783754378	32.2292270688327
+BSG	663.785069995599	632.722524179613	601.694224705993	601.133236499985
+CNTLN	2.23485941057118	1.97886648814255	1.99177526945859	2.63606441370194
+DCHS1	1.36557467048848	1.25064183852721	1.40062046026667	1.2296385964705
+STOML2	56.8286035016604	50.5260871677952	48.7696370839438	44.9884366104539
+PDE4DIP	11.1423223963941	13.7719434810164	10.4336922887578	10.2364032945622
+ADA	26.338096785246	22.9731678890392	42.8827458443435	43.2618442115953
+VPS16	12.5295200567067	11.6675007150994	10.373029920922	10.4987674553771
+EP400	3.47402502081295	3.22421262142185	4.2240158951896	4.26426291360327
+RNH1	35.9380261163109	32.4428801804597	28.1988346840832	32.0434157279718
+RPS13	522.867553382692	476.389183703835	583.955752581489	542.373904666156
+CLPX	82.8384393793781	86.7446804663212	78.4926414724142	75.1445061085399
+RPS11	1300.27222977494	1263.75677368172	1107.3997992002	1075.72506242072
+RPS10	773.66442045445	700.250863105942	621.707706799805	599.645940406373
+RPS17	1097.80248416591	1074.74808676663	994.761525464613	1023.29527693177
+RPS16	693.49583992307	659.756831891863	596.849837193293	608.448053821041
+RPS14	814.303129173723	769.076385732174	804.065056879404	806.113208187967
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+CCDC22	5.90040309658244	5.08327308934923	5.68813930547208	4.7336845474858
+CCDC25	32.4659933535804	27.1194858920637	43.1976813779266	43.6545262873574
+FIBP	25.8057688206942	23.4025493699871	22.6728081635671	22.0077244549518
+ALDH3B1	4.46352587715083	4.09715096214455	2.42315710412378	2.37007095906671
+DCAF11	136.885754549088	131.757345348128	95.4010531900795	97.1412219513151
+DDOST	85.0129547809851	73.5086852947942	67.239745678429	68.1401793610268
+MGC152281	102.415875552095	104.685711832215	129.315818173451	125.232138372636
+RRM1	48.3806745577983	49.1148715360649	39.0920661012792	37.5880640591606
+CLPB	3.50635886825423	3.78197143718082	5.29679088433362	5.22156068923039
+PUSL1	2.13622208970231	1.99192840696824	1.19592393393788	1.55894630451249
+DERA	49.1860075519892	54.8260709502859	47.0988173171878	49.2614372027992
+BROX	14.7426855767455	15.3451037982629	16.6553714851895	18.2604718071955
+TXN	506.086095336058	515.47711746809	447.946314697442	444.253307951748
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+SLC9A3R2	66.4374827655652	56.7694377822109	69.8254270255267	69.1811549274692
+SLC9A3R1	185.615220840609	175.391593167531	137.437815169588	136.489240314059
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+LSM12	19.918177286138	18.6245000221205	21.2007742447329	19.2031389600604
+GCLC	44.7367346882868	39.8881248318436	45.1429474689259	50.5907908082858
+CASP7	10.419954448464	13.060099156642	15.3134620135647	14.6167893297013
+TF	8599.29461693456	9413.46115141257	14863.2413697457	14658.9887904924
+CYP2C18	37.6036495412475	39.973800687212	87.4972610661016	86.6294637668997
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+OAS1X	13.8527954937797	13.6602642078607	10.2841849717523	13.4172035850375
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+SSSCA1	13.2121493626686	13.4288704338149	12.106571944501	10.4596059211301
+ENSBTAG00000000229	119.736087504913	125.132146656163	166.191572173849	164.294745269921
+GGT1	40.0843787247375	36.8874604868939	33.9901946014926	36.1954946909655
+TMX1	15.9009292608672	13.8914500843861	14.9617040218007	15.5309986516919
+NAGA	26.6121506957114	21.7050881819476	17.3447774476791	16.7438518243868
+NAGK	13.5529503195623	13.2757539739551	14.522493003992	13.5344730711148
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+ACTN1	24.1018203431991	22.019177687976	26.4045345156663	26.2671766468744
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+PTPRM	9.15356066519333	8.57338509420922	13.8393657908032	13.0773782679421
+MYO7B	0.317952609778197	0.385858442819635	0.290044468187566	0.281728139410924
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+GAPVD1	9.86245274838146	9.32589783262592	11.5069316375072	10.7711795313778
+SGPL1	35.1386435476099	30.9329642058262	54.1032160617031	54.0236410927655
+NDUFB8	116.834440126871	106.77329850885	108.730135845698	105.119014894694
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+MANF	126.068146487134	126.773584164756	58.1224386487062	55.5531406663572
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+ZFYVE21	68.3132874381588	52.4480368665496	59.2629841310042	61.0504589739928
+APRT	77.4568082297743	67.9739048495469	77.492231948105	83.0519088357615
+CSNK1A1	51.2260737435172	54.0172337031947	58.5773442109201	61.1836671772787
+ABLIM1	20.9208625745434	20.1022950185073	24.4217838324811	22.7551418029438
+JCHAIN	30.0430054978903	23.5539451773765	22.3582374231222	25.7092912501054
+SULT1E1	10.8335871648389	12.9503437186598	18.0102528899749	15.7331713087382
+L2HGDH	4.47577243397739	4.79422041196904	7.36094951756975	6.69809994979611
+SNAP23	15.9538302317801	16.9981803623118	23.6728808248383	23.8363449567666
+TOP2B	17.0653177217484	17.9526171454953	26.278514094742	26.5253226266516
+GM2A	18.2362831739595	21.117271532756	36.731549162245	32.3249918586048
+LACTB	16.1950415825112	15.1687301096019	16.9444987027527	16.5549321922178
+CSRP1	19.1158506905572	14.4858187010906	12.0871307380947	12.8503511823462
+CSRP2	42.7902963085357	44.4432386922299	47.2066861371693	39.1844294702836
+SUCLA2	10.4391346986634	13.5307853969249	11.0637850299061	8.76010784363216
+HHIP	14.5039157346216	11.8517190223921	17.4821834687607	18.8113867036974
+HSD17B12	77.7719103294517	82.3126812575161	48.2217763211841	46.7361450055157
+HSD17B13	1614.69404422453	1690.40087160756	721.683579870367	723.524640259076
+HSD17B10	891.400486239166	840.692626126069	673.07621316047	658.419640203595
+20ALPHA-HSD	1.94219504579959	0.855479552240382	1.37886004846924	0.973692840855602
+ATP6V1A	21.9172580964765	21.0254969442724	27.0481564564741	27.9115409524926
+HSD17B14	47.1228561730785	43.7225291170798	53.8278653093109	60.2647092117435
+BBOX1	208.791109755464	218.781940540238	261.552208259923	259.24452608955
+EMD	25.3227991241643	26.3464811374135	18.2107640920701	20.2162347649282
+ATP6V1E1	36.2535465594273	30.4338578853761	31.1395105692237	33.3171865243901
+UOX	16.9418883020323	12.9545322847907	81.251734915381	77.1966659797797
+ATP6V1H	10.1054999422333	10.7974511717945	11.8287212195442	11.6651404038888
+RAB6B	-0.0796433237878804	0.29498976023963	0.0321186379033785	0.00984058321829792
+MX1	13.5871860929952	11.9785586416949	13.2066483556842	16.4032429052869
+SFXN5	4.79762115778554	6.98575552079888	8.17098087735151	6.15268160158786
+SFXN2	17.6044106332051	19.7122601039609	13.2443461514536	13.1194745665728
+SFXN1	41.5031262618955	45.8867996687204	47.4492995083688	47.3756481245606
+UROD	57.3160850539888	56.333613544218	56.2698947638903	57.4559948253489
+IFIT5	31.8349872983945	34.995449472613	37.6889524507771	36.2242327459588
+LMO7	15.8348911355402	15.2718539418278	16.6079589370498	16.5589920860694
+CCT8	65.2096353452967	75.0874590546435	63.6872681420151	63.3444330706543
+ENSBTAG00000032453	-0.109586111976307	1.05907792777529	0.440291921400709	0.450676672660541
+CCT2	73.4862288415183	71.6893477904709	71.3385445545297	73.86749649934
+CCT3	76.5428835932819	78.7147281760493	62.7291075803548	63.7578986708214
+CCT7	65.2982449940596	59.3812202066599	48.765886822457	47.1382178885552
+CCT4	78.3678484120151	82.3677441349927	66.3962409609487	70.2438018787458
+CCT5	53.5370726039037	50.5769668076478	40.8800204518481	37.6624937890349
+EFTUD2	14.1034930826789	12.2061057315692	12.9859663721995	12.3626121406912
+C4A	801.169854757233	775.852008407187	1038.39579920479	1038.7108087384
+ENSBTAG00000026133	-0.0758996600312405	0.135819323886117	-0.00624862361024775	0.00569716975406065
+C14H8orf82	12.9685783409366	11.7896829341325	10.8945936531737	9.76712837584875
+PDLIM5	24.4523194909809	26.21478008159	30.2204227493468	30.5144087803354
+PDLIM1	48.9441650688375	48.4924288013859	51.109801888032	44.4207242959783
+PDLIM3	4.98471758129181	6.0683167986955	4.9344709988019	4.36717785844193
+BRCC3	18.3537614112121	20.6539025650382	24.099741237318	28.4432797609603
+AKTIP	10.202903112929	11.2959771811719	10.2591814243062	11.0434153316157
+ENSBTAG00000048287	149.05477666269	99.7346905915885	141.419375673324	115.214214044895
+RAP1GDS1	7.57914208897516	10.0291935433649	6.46253529867549	7.36991257217177
+GDI2	188.676057401403	200.45626894746	172.647652487407	168.412847546611
+DHRS1	171.909874851457	160.762362121018	106.672713726251	102.789276421766
+GDI1	47.0244804225268	45.2204308839204	45.855978939476	48.0025472140323
+MCCC2	16.0314104791439	20.865612904431	23.9911712685723	22.4239222791931
+MCCC1	38.0454538940768	36.8027989657298	42.8439918204121	44.7044962684895
+PAFAH2	44.0469544575759	41.3618576212055	32.9528830772532	35.4582251643708
+ERLIN2	68.0978853046623	65.3694653269437	78.6288219066459	78.4463650261116
+GFM1	29.9801082462597	31.6974498499095	36.7371995301849	35.6763765938689
+GFM2	18.1180888512257	19.0706325565877	18.6437316190765	18.8678516983431
+CBS	50.275469678549	50.7061605026127	41.8564919045229	41.1033917891441
+ENSBTAG00000034850	7.19255438753177	9.07771322969325	4.89673347612519	7.23189695492511
+FLNB	15.1599416220712	13.5285964590462	14.9154961383453	14.4015827376836
+EIF4A1	95.0807961297107	94.5152709723596	77.0997675129749	75.6243230370453
+EIF4A3	35.6749587239255	35.3978151101994	33.8199276975183	32.2993887324336
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+CAPNS1	86.9562244268314	84.7842609047457	88.1861844618468	89.3024479094432
+CROT	225.143883617479	239.268141825478	200.295875820315	195.980140971379
+LETM1	18.7933925033172	16.6379513377632	12.5957388231499	10.9328391475656
+TMPRSS3	-0.0758996600312405	0.135819323886117	-0.00624862361024775	0.00569716975406065
+PRKCSH	99.6668486746383	94.4108487755093	86.6658798074426	88.5917296376608
+TAF15	37.4864719347854	34.6520579740238	44.8786809555305	46.7109671660824
+FH	99.427560200047	108.767823645757	111.589459406576	109.787671728654
+DCTN3	28.2141590590715	30.347221263962	30.639630320406	25.2663941692082
+DCTN1	16.8835816086212	17.8557283155233	17.7829887929069	17.6683513508846
+RNASET2	28.0397273261063	27.7651311363531	35.3051340456282	35.2290014780888
+NDUFV3	45.1067976349019	47.4721643782035	36.2475069858111	35.4449385592524
+PKLR	40.281523908341	34.2991045623882	68.7811449831403	66.0826426071171
+NDUFV1	89.7912403998105	79.4699969480042	65.7334369347194	66.2146523215109
+RPS4Y1	59.6872774571047	50.357517369007	36.6320736415744	41.3407159445932
+ASPA	2.62833635034221	2.88632930108215	2.74381609101679	2.91334361050854
+FAM186A	-0.0758996600312405	0.135819323886117	-0.00624862361024775	0.00569716975406065
+ASPN	23.4557853898405	25.9195127497061	33.3056106011246	32.1552124892458
+SERF2	1220.89729708647	1159.98486074744	831.972393427435	838.48508352013
+ASPH	72.0647703953054	66.9963068048453	68.3878682774177	70.5719675637984
+CYP39A1	23.9290557700249	24.6572744691595	26.892708348116	27.7742496982802
+RBM15	5.37528324737828	4.17041563941325	5.01944209583531	6.21856864373041
+RBM14	29.3536158786837	24.5546159293507	22.3542103480944	21.2932829280388
+PARVA	19.7052744111987	20.084570326936	32.1304426470257	36.2068742144069
+GGH	68.1429129930792	73.4535733281278	54.0227443123657	56.2613565928326
+NEXN	0.605094853940956	0.756782756357016	0.345223717661467	0.356747857340383
+MVP	35.5323014157723	33.4652297049853	28.2955565985349	28.560847775963
+LAMA2	0.170572527481738	0.543253387069354	0.17315800626322	0.131801598359233
+PSMD6	42.9764534127594	40.4951715867558	40.7268914985099	39.5200663098277
+LAMA5	5.37977218063479	5.09480839712849	3.8840305439793	3.64405317823273
+ARVCF	28.4167579749895	21.144480988249	20.6116172053356	21.7296136533985
+NAT1	45.3624929481399	45.0042938580826	53.3196661379433	49.0306915720927
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+FAM96A	83.5604033977766	81.8753339485662	100.705098471446	97.7509813736637
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+BICD2	8.08825997009739	8.13016509366486	7.54569660889039	8.09658275324354
+SEC22B	28.0158406759932	26.2168927410539	25.8085478130071	26.2476955958624
+NFIA	18.7998060312645	20.6019630622779	25.8027529792118	24.0930147370757
+CD163	41.2947020425552	45.7036852574451	72.9594414051051	75.7918900293897
+AARS	34.2338462472451	28.8187366028439	28.2737262835897	29.253852156971
+PDCD6IP	47.5366726918095	49.4878158869482	49.3569707972503	51.6081599918002
+TSGA10	0.532544422811476	0.549561083730723	0.808442535970308	0.993907565460829
+DNM1L	10.9793186842086	11.8429623497427	10.0686886407995	10.7556465831498
+SERPINH1	15.3198304548624	15.9048127634568	8.28307047703207	9.71105820398736
+FOLR3	138.428905969161	116.281636988597	85.8755941382458	89.4439500612999
+C5orf51	17.2935605353597	15.0249063894766	19.5560379723125	22.1981123516811
+GSTK1	217.176269127719	211.304802899312	239.829423960609	229.9821520916