# HG changeset patch
# User rsajulga
# Date 1545336766 18000
# Node ID 0072d7fe861a0217bcec2571c7f44d8a5a61d811

planemo upload

diff -r 000000000000 -r 0072d7fe861a quantp.r
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/quantp.r	Thu Dec 20 15:12:46 2018 -0500
@@ -0,0 +1,1232 @@
+#***************************************************************************************************************************************
+# 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$Group = tempgrp;
+  png(outfile, width = 6, height = 6, units = 'in', res=300);
+  # bitmap(outfile, "png16m");
+  g = autoplot(prcomp(select(tempdf, -Group)), data = tempdf, colour = 'Group', size=3);
+  saveWidget(ggplotly(g), file.path(gsub("\\.png", "\\.html", outplot)))
+  plot(g);
+  dev.off();
+}
+
+#===============================================================================
+# Regression and Cook's distance
+#===============================================================================
+singlesample_regression = function(PE_TE_data,htmloutfile, append=TRUE)
+{
+  rownames(PE_TE_data) = PE_TE_data$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$adj.r.squared,"</td></tr>\n",
+      file = htmloutfile, append = TRUE);
+  
+  cat("</table>\n", file = htmloutfile, append = TRUE);
+  
+  cat(
+    "<font color='#ff0000'><h3>Regression and diagnostics plots</h3></font>\n",
+    file = htmloutfile, append = TRUE);
+  
+  outplot = paste(outdir,"/PE_TE_lm_1.png",sep="",collapse="");
+  png(outplot, width = 10, height = 10, units = 'in',res=300);
+  # bitmap(outplot, "png16m");
+  par(mfrow=c(1,1));
+  plot(regmodel, 1, cex.lab=1.5);
+  dev.off();
+  
+  g <- autoplot(regmodel, label = FALSE)[[1]] +
+    geom_point(aes(text=sprintf("Residual: %.2f<br>Fitted value: %.2f<br>Gene: %s", .fitted, .resid, PE_TE_data$PE_ID)),
+               shape = 1, size = .1, stroke = .2) +
+    theme_light()
+  saveWidget(ggplotly(g, tooltip= c("text")), file.path(gsub("\\.png", "\\.html", outplot)))
+  
+  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));
+  g <- plot(regmodel, 2, cex.lab=1.5);
+  ggplotly(g)
+  dev.off();
+  
+  g <- autoplot(regmodel, label = FALSE)[[2]] +
+    geom_point(aes(text=sprintf("Standarized residual: %.2f<br>Theoretical quantile: %.2f<br>Gene: %s", .qqx, .qqy, PE_TE_data$PE_ID)),
+               shape = 1, size = .1) +
+    theme_light()
+  saveWidget(ggplotly(g, tooltip = "text"), file.path(gsub("\\.png", "\\.html", outplot)))
+  
+  
+  outplot = paste(outdir,"/PE_TE_lm_5.png",sep="",collapse="");
+  png(outplot, width = 10, height = 10, units = 'in',res=300);
+  # bitmap(outplot, "png16m");
+  par(mfrow=c(1,1));
+  plot(regmodel, 5, cex.lab=1.5);
+  dev.off();
+  
+  cd_cont_pos <- function(leverage, level, model) {sqrt(level*length(coef(model))*(1-leverage)/leverage)}
+  cd_cont_neg <- function(leverage, level, model) {-cd_cont_pos(leverage, level, model)}
+  
+  g <- autoplot(regmodel, label = FALSE)[[4]] +
+    aes(label = PE_TE_data$PE_ID) + 
+    geom_point(aes(text=sprintf("Leverage: %.2f<br>Standardized residual: %.2f<br>Gene: %s", .hat, .stdresid, PE_TE_data$PE_ID))) +
+    theme_light()
+  saveWidget(ggplotly(g, tooltip = "text"), file.path(gsub("\\.png", "\\.html", outplot)))
+  
+  cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">', file = htmloutfile, append = TRUE);
+  
+  cat(
+    '<tr bgcolor="#7a0019"><th>', "<font color='#ffcc33'><h4>1) <u>Residuals vs Fitted plot</h4></font></u></th>\n",
+    '<th><font color=#ffcc33><h4>2) <u>Normal Q-Q plot of residuals</h4></font></u></th></tr>\n',
+    file = htmloutfile, append = TRUE);
+  
+  cat(
+    '<tr><td align=center><img src="PE_TE_lm_1.png" width=600 height=600>',
+    gsub("width:500px;height:500px", "width:600px;height:600px", extractWidgetCode(paste(outdir,"/PE_TE_lm_1.png",sep="",collapse=""))$widget_div),
+    '</td><td align=center><img src="PE_TE_lm_2.png" width=600 height=600>',
+    gsub("width:500px;height:500px", "width:600px;height:600px", extractWidgetCode(paste(outdir,"/PE_TE_lm_2.png",sep="",collapse=""))$widget_div),
+    '</td></tr>\n', file = htmloutfile, append = TRUE);
+  
+  cat(
+    '<tr><td align=center>This plot checks for linear relationship assumptions.<br>If a horizontal line is observed without any distinct patterns, it indicates a linear relationship.</td>\n',
+    '<td align=center>This plot checks whether residuals are normally distributed or not.<br>It is good if the residuals points follow the straight dashed line i.e., do not deviate much from dashed line.</td></tr></table>\n',
+    file = htmloutfile, append = TRUE);
+  
+  
+  #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+  # Residuals data
+  #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+  res_all = regmodel$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></tr>\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>', 
+    gsub("width:500px;height:500px", "width:600px;height:600px", extractWidgetCode(paste(outdir,"/PE_TE_lm_5.png",sep="",collapse=""))$widget_div)
+    , '</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();
+  
+  cooksd_df <- data.frame(cooksd)
+  cooksd_df$genes <- row.names(cooksd_df)
+  cooksd_df$index <- 1:nrow(cooksd_df)
+  cooksd_df$colors <- "black"
+  cutoff <- as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T)
+  cooksd_df[cooksd_df$cooksd > cutoff,]$colors <- "red"
+  
+  g <- ggplot(cooksd_df, aes(x = index, y = cooksd, label = row.names(cooksd_df), color=as.factor(colors), 
+                             text=sprintf("Gene: %s<br>Cook's Distance: %.3f", row.names(cooksd_df), cooksd))) +
+    ggtitle("Influential Obs. by Cook's distance") + xlab("Observations") + ylab("Cook's Distance") + 
+    #xlim(0, 3000) + ylim(0, .15) + 
+    scale_shape_discrete(solid=F) +
+    geom_point(size = 2, shape = 8)  + 
+    geom_hline(yintercept = cutoff, 
+               linetype = "dashed", color = "red") + 
+    scale_color_manual(values = c("black" = "black", "red" = "red")) + 
+    theme_light() + theme(legend.position="none")
+  saveWidget(ggplotly(g, tooltip= "text"), file.path(gsub("\\.png", "\\.html", outplot)))
+  
+  cat(
+    '<img src="PE_TE_lm_cooksd.png" width=800 height=800>',
+    gsub("width:500px;height:500px", "width:800px;height:800px", extractWidgetCode(outplot)$widget_div),
+    '<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, label=PE_ID)) + geom_smooth() + 
+    xlab("Transcript abundance log fold-change") + ylab("Protein abundance log fold-change") + 
+    xlim(min_lim,max_lim) + ylim(min_lim,max_lim) +
+    geom_point(aes(text=sprintf("Gene: %s<br>Transcript Abundance (log fold-change): %.3f<br>Protein Abundance (log fold-change): %.3f",
+                                PE_ID, TE_abundance, PE_abundance)))
+  suppressMessages(plot(g));
+  suppressMessages(saveWidget(ggplotly(g, tooltip="text"), file.path(gsub("\\.png", "\\.html", outplot))));
+  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>',
+      gsub('id="html', 'id="secondhtml"',
+           gsub("width:500px;height:500px", "width:600px;height:600px", extractWidgetCode(paste(outdir,"/TE_PE_scatter.png",sep="",collapse=""))$widget_div)),
+      '</td>\n',
+      file = htmloutfile, append = TRUE);
+  
+  # After
+  cat("<td align=center>\n",
+      '<img src="AbundancePlot_scatter_without_outliers.png" width=600 height=600>',
+      gsub("width:500px;height:500px", "width:600px;height:600px", extractWidgetCode(outplot)$widget_div),
+      
+      '</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();
+  
+  
+  p <- d3heatmap(as.matrix(PE_TE_data[,c("PE_abundance","TE_abundance")]), scale = "col",
+                 dendrogram = "row", colors = greenred(100),
+                 hclustfun = hclust, distfun = dist,
+                 show_grid = FALSE)
+  saveWidget(p, file.path(gsub("\\.png", "\\.html", outplot)))
+  
+  cat('<tr><td align=center colspan="2">',
+      '<img src="PE_TE_heatmap.png" width=800 height=800>',
+      gsub("width:960px;height:500px", "width:800px;height:800px", extractWidgetCode(outplot)$widget_div),
+      '</td></tr>\n',
+      file = htmloutfile, append = TRUE);
+  
+  
+  temp_PE_TE_data = data.frame(PE_TE_data$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();
+  
+  # Interactive plot for k-means clustering
+  g <- ggplot(PE_TE_data, aes(x = TE_abundance, y = PE_abundance, label = row.names(PE_TE_data),
+                              text=sprintf("Gene: %s<br>Transcript Abundance: %.3f<br>Protein Abundance: %.3f",
+                                           PE_ID, TE_abundance, PE_abundance),
+                              color=as.factor(k1$cluster))) +
+    xlab("Transcript Abundance") + ylab("Protein Abundance") + 
+    scale_shape_discrete(solid=F) + geom_smooth(method = "loess", span = 2/3) +
+    geom_point(size = 1, shape = 8) +
+    theme_light() + theme(legend.position="none")
+  saveWidget(ggplotly(g, tooltip=c("text")), file.path(gsub("\\.png", "\\.html", outplot)))
+  
+  cat('<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>K-mean clustering</font></th><th><font color=#ffcc33>Number of clusters: ',nclust,'</font></th></tr>\n',
+      file = htmloutfile, append = TRUE);
+  
+  tempind = order(k1$cluster);
+  tempoutfile = paste(outdir,"/PE_TE_kmeans_clusterpoints.txt",sep="",collapse="");
+  write.table(data.frame(PE_TE_data_kdata[tempind, ], Cluster=k1$cluster[tempind]), file=tempoutfile, row.names=F, quote=F, sep="\t", eol="\n")
+  
+  #paste(outdir,"/PE_TE_heatmap.png",sep="",collapse="");
+  cat('<tr><td colspan="2" align=center><img src="PE_TE_kmeans.png" width=800 height=800>',
+      gsub("width:500px;height:500px", "width:800px;height:800px", extractWidgetCode(outplot)$widget_div), '</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, label=PE_ID)) + geom_smooth() + 
+    xlab("Transcript abundance log fold-change") + ylab("Protein abundance log fold-change") + 
+    xlim(min_lim,max_lim) + ylim(min_lim,max_lim) +
+    geom_point(aes(text=sprintf("Gene: %s<br>Transcript Abundance (log fold-change): %.3f<br>Protein Abundance (log fold-change): %.3f",
+                                PE_ID, TE_abundance, PE_abundance)),
+               size = .5)
+  suppressMessages(plot(g));
+  suppressMessages(saveWidget(ggplotly(g, tooltip = "text"), file.path(gsub("\\.png", "\\.html", outfile))))
+  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$Sample = rownames(tempdf);
+  tempdf1 = melt(tempdf, id.vars = "Sample");
+  
+  if("Gene" %in% colnames(df)){
+    tempdf1$Name = df$Gene;
+  } else if ("Protein" %in% colnames(df)){
+    tempdf1$Name = df$Protein;
+  } else if ("Genes" %in% colnames(df)){
+    tempdf1$Name = df$Genes;
+  }
+  
+  tempdf1$Group = sampleinfo_df[tempdf1$Sample,2];
+  png(outplot, width = 6, height = 6, units = 'in', res=300);
+  # bitmap(outplot, "png16m");
+  if(fill_leg=="No"){
+    tempdf1$Group = c("case", "control")
+  }
+  
+  g = ggplot(tempdf1, aes(x=Sample, y=value, fill=Group)) + 
+    geom_boxplot()+
+    labs(x=user_xlab) + labs(y=user_ylab)
+  saveWidget(ggplotly(g), file.path(gsub("\\.png", "\\.html", outfile)))
+  plot(g);
+  dev.off();
+}
+
+## A wrapper to saveWidget which compensates for arguable BUG in
+## saveWidget which requires `file` to be in current working
+## directory.
+saveWidget <- function (widget,file,...) {
+  wd<-getwd()
+  on.exit(setwd(wd))
+  outDir<-dirname(file)
+  file<-basename(file)
+  setwd(outDir);
+  htmlwidgets::saveWidget(widget,file=file,selfcontained = FALSE)
+}
+
+#===============================================================================
+# 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$LogFold, PE_pval, PE_adj_pval);
+  colnames(PE_TE_logfold_pval) = c("Gene", "Transcript log fold-change", "p-value (transcript)", "adj p-value (transcript)", "Protein log fold-change", "p-value (protein)", "adj p-value (protein)");
+  outdatafile = paste(outdir,"/PE_TE_logfold_pval.txt", sep="", collapse="");
+  write.table(PE_TE_logfold_pval, file=outdatafile, row.names=F, sep="\t", quote=F);
+  cat("<br><br><font size=5><b><a href='PE_TE_logfold_pval.txt' target='_blank'>Download the complete fold change data here</a></b></font><br>\n",
+      file = htmloutfile, append = TRUE);
+  
+  if(length(condition_ind)!=1)
+  {
+    # Volcano Plot
+    
+    if(volc_with=="adj_pval")
+    {
+      PE_pval = PE_adj_pval
+      TE_pval = TE_adj_pval
+      volc_ylab = "-log10 Adjusted p-value";
+    }else{
+      if(volc_with=="pval")
+      {
+        volc_ylab = "-log10 p-value";
+      }
+    }
+    outplot_PE = paste(outdir,"/PE_volcano.png",sep="",collapse="");
+    png(outplot_PE, width = 10, height = 10, units = 'in', res=300);
+    # bitmap(outplot, "png16m");
+    par(mfrow=c(1,1));
+    
+    plot(PE_df_logfold$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")
+    PE_df_logfold$color <- "black"
+    #sel <- which((PE_df_logfold$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$LogFold<log(0.5, base=2)))
+    sel1 <- which(PE_pval<=0.05)
+    sel=intersect(sel,sel1)
+    points(PE_df_logfold[sel,"LogFold"], -log10(PE_pval[sel]),col="red")
+    PE_df_logfold[sel,]$color <- "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")
+    PE_df_logfold[sel,]$color <- "blue"
+    abline(h = -log(0.05,base=10), col="red", lty=2)
+    abline(v = log(2,base=2), col="red", lty=2)
+    abline(v = log(0.5,base=2), col="red", lty=2)
+    dev.off();
+    
+    g <- ggplot(PE_df_logfold, aes(x = LogFold, -log10(PE_pval), color = as.factor(color),
+                                   text=sprintf("Gene: %s<br>Log2 Fold-Change: %.3f<br>-log10 p-value: %.3f<br>p-value: %.3f",
+                                                Genes, LogFold, -log10(PE_pval), PE_pval))) +
+      xlab("log2 fold change") + ylab("-log10 p-value") + 
+      geom_point(shape=1, size = 1.5, stroke = .2) +
+      scale_color_manual(values = c("black" = "black", "red" = "red", "blue" = "blue")) + 
+      geom_hline(yintercept = -log(0.05,base=10), linetype="dashed", color="red") +
+      geom_vline(xintercept = log(2,base=2), linetype="dashed", color="red") +
+      geom_vline(xintercept = log(0.5,base=2), linetype="dashed", color="red") +
+      theme_light() + theme(legend.position="none")
+    saveWidget(ggplotly(g, tooltip="text"), file.path(gsub("\\.png", "\\.html", outplot_PE)))
+    
+    outplot_TE = paste(outdir,"/TE_volcano.png",sep="",collapse="");
+    png(outplot_TE, width = 10, height = 10, units = 'in', res=300);
+    # bitmap(outplot, "png16m");
+    par(mfrow=c(1,1));
+    
+    plot(TE_df_logfold$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")
+    TE_df_logfold$color <- "black"
+    #sel <- which((TE_df_logfold$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$LogFold<log(0.5, base=2)))
+    sel1 <- which(TE_pval<=0.05)
+    sel=intersect(sel,sel1)
+    points(TE_df_logfold[sel,"LogFold"], -log10(TE_pval[sel]),col="red")
+    TE_df_logfold[sel,]$color <- "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")
+    TE_df_logfold[sel,]$color <- "blue"
+    abline(h = -log(0.05,base=10), col="red", lty=2)
+    abline(v = log(2,base=2), col="red", lty=2)
+    abline(v = log(0.5,base=2), col="red", lty=2)
+    dev.off();
+    
+    g <- ggplot(TE_df_logfold, aes(x = LogFold, -log10(TE_pval), color = as.factor(color),
+                                   text=sprintf("Gene: %s<br>Log2 Fold-Change: %.3f<br>-log10 p-value: %.3f<br>p-value: %.3f",
+                                                Genes, LogFold, -log10(TE_pval), TE_pval))) +
+      xlab("log2 fold change") + ylab("-log10 p-value") + 
+      geom_point(shape=1, size = 1.5, stroke = .2) +
+      scale_color_manual(values = c("black" = "black", "red" = "red", "blue" = "blue")) + 
+      geom_hline(yintercept = -log(0.05,base=10), linetype="dashed", color="red") +
+      geom_vline(xintercept = log(2,base=2), linetype="dashed", color="red") +
+      geom_vline(xintercept = log(0.5,base=2), linetype="dashed", color="red") +
+      theme_light() + theme(legend.position="none")
+    saveWidget(ggplotly(g, tooltip="text"), file.path(gsub("\\.png", "\\.html", outplot_TE)))
+    
+    
+    cat('<br><table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Transcript Fold-Change</font></th><th><font color=#ffcc33>Protein Fold-Change</font></th></tr>\n', file = htmloutfile, append = TRUE);
+    cat("<tr><td align=center>", 
+        '<img src="TE_volcano.png" width=600 height=600>',
+        extractWidgetCode(outplot_TE)$widget_div,
+        '</td>\n', file = htmloutfile, append = TRUE);
+    cat("<td align=center>",
+        '<img src="PE_volcano.png" width=600 height=600>',
+        extractWidgetCode(outplot_PE)$widget_div,
+        '</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));
+suppressPackageStartupMessages(library(plotly));
+suppressPackageStartupMessages(library(d3heatmap));
+
+#===============================================================================
+# 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;
+
+#===============================================================================
+# Obtain JS/HTML lines for interactive visualization
+#===============================================================================
+extractWidgetCode = function(outplot){
+  lines <- readLines(gsub("\\.png", "\\.html", outplot))
+  return(list(
+    'prescripts'  = c('',
+                      gsub('script', 'script',
+                           lines[grep('<head>',lines) + 3
+                                 :grep('</head>' ,lines) - 5]),
+                      ''),
+    'widget_div'  = paste('',
+                          gsub('width:100%;height:400px',
+                               'width:500px;height:500px',
+                               lines[grep(lines, pattern='html-widget')]),
+                          '', sep=''),
+    'postscripts' = paste('',
+                          gsub('script', 'script',
+                               lines[grep(lines, pattern='<script type')]),
+                          '', sep='')));
+}
+prescripts <- list()
+postscripts <- list()
+
+
+#===============================================================================
+# 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);
+  singlesample_scatter(PE_TE_data, outplot);  
+  lines <- extractWidgetCode(outplot);
+  postscripts <- c(postscripts, lines$postscripts);
+  cat("<tr><td align=center>", '<img src="TE_PE_scatter.png" width=800 height=800>', lines$widget_div, '</td></tr>\n', file = htmloutfile, append = TRUE);
+  PE_TE_data = data.frame(PE_df, TE_df);
+  colnames(PE_TE_data) = c("PE_ID","PE_abundance","TE_ID","TE_abundance");
+  
+  # TE PE Cor
+  cat("<tr><td align=center>", file = htmloutfile, append = TRUE);
+  singlesample_cor(PE_TE_data, htmloutfile, append=TRUE);
+  cat('<font color="red">*Note that <u>correlation</u> is <u>sensitive to outliers</u> in the data. So it is important to analyze outliers/influential observations in the data.<br> Below we use <u>Cook\'s distance based approach</u> to identify such influential observations.</font>\n',
+      file = htmloutfile, append = TRUE);
+  cat('</td></table>',
+      file = htmloutfile, append = TRUE);
+  
+  cat('<hr/><h2 id="regression_data"><font color=#ff0000>REGRESSION ANALYSIS</font></h2>\n',
+      file = htmloutfile, append = TRUE);
+  
+  # TE PE Regression
+  singlesample_regression(PE_TE_data,htmloutfile, append=TRUE);
+  postscripts <- c(postscripts, c(extractWidgetCode(paste(outdir,"/PE_TE_lm_1.png",sep="",collapse=""))$postscripts,
+                                  extractWidgetCode(paste(outdir,"/PE_TE_lm_2.png",sep="",collapse=""))$postscripts,
+                                  extractWidgetCode(paste(outdir,"/PE_TE_lm_5.png",sep="",collapse=""))$postscripts,
+                                  extractWidgetCode(paste(outdir,"/PE_TE_lm_cooksd.png",sep="",collapse=""))$postscripts,
+                                  extractWidgetCode(paste(outdir,"/AbundancePlot_scatter_without_outliers.png",sep="",collapse=""))$postscripts));
+  
+  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);
+  lines <- extractWidgetCode(paste(outdir,"/PE_TE_heatmap.png",sep="",collapse=""))
+  postscripts <- c(postscripts, lines$postscripts)
+  prescripts <- c(prescripts, lines$prescripts)
+  
+  
+  # TE PE Clustering (kmeans)
+  singlesample_kmeans(PE_TE_data, htmloutfile, nclust=as.numeric(numCluster))
+  postscripts <- c(postscripts, extractWidgetCode(paste(outdir,"/PE_TE_kmeans.png",sep="",collapse=""))$postscripts)
+  
+}else{
+  if(mode=="multiple")
+  {
+    cat('<h2 id="sample_dist"><font color=#ff0000>SAMPLE DISTRIBUTION</font></h2>\n',
+        file = htmloutfile, append = TRUE);
+    
+    # TE Boxplot
+    outplot = paste(outdir,"/Box_TE_all_rep.png",sep="",collape="");
+    temp_df_te_data = data.frame(TE_df[,1], log(TE_df[,2:length(TE_df)]));
+    colnames(temp_df_te_data) = colnames(TE_df);
+    multisample_boxplot(temp_df_te_data, sampleinfo_df, outplot, "Yes", "Samples", "Transcript Abundance (log)");
+    lines <- extractWidgetCode(outplot)
+    prescripts <- c(prescripts, lines$prescripts)
+    postscripts <- c(postscripts, lines$postscripts)
+    cat('<table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">\n',
+        '<tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n',
+        "<tr><td align=center>", file = htmloutfile, append = TRUE);
+    cat('<img src="Box_TE_all_rep.png" width=500 height=500>', 
+        lines$widget_div, '</td>', file = htmloutfile, append = TRUE);
+    
+    # PE Boxplot
+    outplot = paste(outdir,"/Box_PE_all_rep.png",sep="",collape="");
+    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)");
+    lines <- extractWidgetCode(outplot)
+    #prescripts <- c(prescripts, lines$prescripts)
+    postscripts <- c(postscripts, lines$postscripts)
+    cat("<td align=center>", '<img src="Box_PE_all_rep.png" width=500 height=500>',
+        lines$widget_div, '</td></tr></table>\n', file = htmloutfile, append = TRUE);
+    
+    # Calc TE PCA
+    outplot = paste(outdir,"/PCA_TE_all_rep.png",sep="",collape="");
+    multisample_PCA(TE_df, sampleinfo_df, outplot);
+    PCA_TE <- extractWidgetCode(outplot)
+    postscripts <- c(postscripts, PCA_TE$postscripts)
+    
+    # Calc PE PCA
+    outplot = paste(outdir,"/PCA_PE_all_rep.png",sep="",collape="");
+    multisample_PCA(PE_df, sampleinfo_df, outplot);
+    PCA_PE <- extractWidgetCode(outplot)
+    postscripts <- c(postscripts, PCA_PE$postscripts)
+    
+    # Replicate mode
+    templist = mergeReplicates(TE_df,PE_df, sampleinfo_df, method);
+    TE_df = templist$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="");
+    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)");
+    lines <- extractWidgetCode(outplot)
+    #prescripts <- c(prescripts, lines$prescripts)
+    postscripts <- c(postscripts, lines$postscripts)
+    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>', lines$widget_div, '</td>\n', file = htmloutfile, append = TRUE);
+    
+    # PE Boxplot
+    outplot = paste(outdir,"/Box_PE_rep.png",sep="",collape="");
+    temp_df_pe_data = data.frame(PE_df[,1], log(PE_df[,2:length(PE_df)]));
+    colnames(temp_df_pe_data) = colnames(PE_df);
+    multisample_boxplot(temp_df_pe_data, sampleinfo_df, outplot, "No", "Sample Groups", "Mean Protein Abundance (log)");
+    lines <- extractWidgetCode(outplot)
+    #prescripts <- c(prescripts, lines$prescripts)
+    postscripts <- c(postscripts, lines$postscripts)
+    cat("<td align=center>", '<img src="Box_PE_rep.png" width=500 height=500>', lines$widget_div, '</td></tr></table>\n', file = htmloutfile, append = TRUE);
+    
+    #===============================================================================
+    # 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="");
+    multisample_boxplot(TE_df, sampleinfo_df, outplot, "Yes", "Sample (log2(case/control))", "Transcript Abundance fold-change (log2)");
+    lines <- extractWidgetCode(outplot)
+    postscripts <- c(postscripts, lines$postscripts)
+    cat('<br><font color="#ff0000"><h3>Distribution (Box plot) of log fold change </h3></font>', file = htmloutfile, append = TRUE);
+    cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n',
+        "<tr><td align=center>", '<img src="Box_TE.png" width=500 height=500>', lines$widget_div, '</td>\n', file = htmloutfile, append = TRUE);
+    
+    # PE Boxplot
+    outplot = paste(outdir,"/Box_PE.png",sep="",collape="");
+    multisample_boxplot(PE_df, sampleinfo_df, outplot, "Yes", "Sample (log2(case/control))", "Protein Abundance fold-change(log2)");
+    lines <- extractWidgetCode(outplot)
+    postscripts <- c(postscripts, lines$postscripts)
+    cat("<td align=center>", '<img src="Box_PE.png" width=500 height=500>', lines$widget_div,'</td></tr></table>\n', file = htmloutfile, append = TRUE);
+    
+    
+    # Log Fold Data
+    perform_Test_Volcano(TE_df_orig,PE_df_orig,TE_df, PE_df,sampleinfo_df_orig,method,correction_method,volc_with)
+    postscripts <- c(postscripts, extractWidgetCode(paste(outdir,"/TE_volcano.png",sep="",collapse=""))$postscripts)
+    postscripts <- c(postscripts, extractWidgetCode(paste(outdir,"/PE_volcano.png",sep="",collapse=""))$postscripts)
+    
+    # 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>', PCA_TE$widget_div, '</td>\n',
+        "<td align=center>", '<img src="PCA_PE_all_rep.png" width=500 height=500>', PCA_PE$widget_div, '</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);
+    
+    PE_TE_data = data.frame(PE_df, TE_df);
+    colnames(PE_TE_data) = c("PE_ID","PE_abundance","TE_ID","TE_abundance");
+    
+    # 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);
+    singlesample_scatter(PE_TE_data, outplot);  
+    lines <- extractWidgetCode(outplot);
+    postscripts <- c(postscripts, lines$postscripts);  
+    cat("<tr><td align=center>", '<img src="TE_PE_scatter.png" width=800 height=800>', gsub('width:500px;height:500px', 'width:800px;height:800px' , lines$widget_div),
+        '</td>\n', file = htmloutfile, append = TRUE);
+    
+    # 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);
+    postscripts <- c(postscripts, c(extractWidgetCode(paste(outdir,"/PE_TE_lm_1.png",sep="",collapse=""))$postscripts,
+                                    extractWidgetCode(paste(outdir,"/PE_TE_lm_2.png",sep="",collapse=""))$postscripts,
+                                    extractWidgetCode(paste(outdir,"/PE_TE_lm_5.png",sep="",collapse=""))$postscripts,
+                                    extractWidgetCode(paste(outdir,"/PE_TE_lm_cooksd.png",sep="",collapse=""))$postscripts,
+                                    extractWidgetCode(paste(outdir,"/AbundancePlot_scatter_without_outliers.png",sep="",collapse=""))$postscripts,
+                                    gsub('data-for="html', 'data-for="secondhtml"', 
+                                         extractWidgetCode(paste(outdir,"/TE_PE_scatter.png",sep="",collapse=""))$postscripts)));
+    
+    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);
+    lines <- extractWidgetCode(paste(outdir,"/PE_TE_heatmap.png",sep="",collapse=""))
+    postscripts <- c(postscripts, lines$postscripts)
+    prescripts <- c(prescripts, lines$prescripts)
+    
+    #TE PE Clustering (kmeans)
+    singlesample_kmeans(PE_TE_data, htmloutfile, nclust=as.numeric(numCluster))
+    postscripts <- c(postscripts, extractWidgetCode(paste(outdir,"/PE_TE_kmeans.png",sep="",collapse=""))$postscripts);
+  }
+}
+cat("<h3>Go To:</h3>\n",
+    "<ul>\n",
+    "<li><a href=#sample_dist>Sample distribution</a></li>\n",
+    "<li><a href=#corr_data>Correlation</a></li>\n",
+    "<li><a href=#regression_data>Regression analysis</a></li>\n",
+    "<li><a href=#inf_obs>Influential observations</a></li>\n",
+    "<li><a href=#cluster_data>Cluster analysis</a></li></ul>\n",
+    "<br><a href=#>TOP</a>",
+    file = htmloutfile, append = TRUE);
+cat("</body></html>\n", file = htmloutfile, append = TRUE);
+
+
+#===============================================================================
+# Add masked-javascripts tags to HTML file in the head and end
+#===============================================================================
+
+htmllines <- readLines(htmloutfile)
+htmllines[1] <- paste('<html>\n<head>\n', paste(prescripts, collapse='\n'), '\n</head>\n<body>')
+cat(paste(htmllines, collapse='\n'), file = htmloutfile)
+cat('\n', paste(postscripts, collapse='\n'), "\n",
+    "</body>\n</html>\n", file = htmloutfile, append = TRUE);
diff -r 000000000000 -r 0072d7fe861a quantp.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/quantp.xml	Thu Dec 20 15:12:46 2018 -0500
@@ -0,0 +1,197 @@
+<tool id="quantp" name="QuanTP" version="1.0.0">
+    <description>Correlation between protein and transcript abundances</description>
+    <requirements>
+        <requirement type="package" version="1.10.4">r-data.table</requirement>
+        <requirement type="package" version="3.0.1">r-gplots</requirement>
+        <requirement type="package" version="0.7.6">r-dplyr</requirement>
+        <requirement type="package" version="3.0.0">r-ggplot2</requirement>
+        <requirement type="package" version="0.4.5">r-ggfortify</requirement>
+        <requirement type="package" version="4.8.0">r-plotly</requirement>
+        <requirement type="package" version="0.6.1.2">r-d3heatmap</requirement>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+Rscript '$__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>
diff -r 000000000000 -r 0072d7fe861a test-data/exp_design_file.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/exp_design_file.tabular	Thu Dec 20 15:12:46 2018 -0500
@@ -0,0 +1,6 @@
+case	G1
+control	G2
+D03_01	G1
+D03_02	G1
+M14_01	G2
+M14_02	G2
diff -r 000000000000 -r 0072d7fe861a test-data/output.html
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.html	Thu Dec 20 15:12:46 2018 -0500
@@ -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>
diff -r 000000000000 -r 0072d7fe861a test-data/protein_data.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/protein_data.tabular	Thu Dec 20 15:12:46 2018 -0500
@@ -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
+METTL27	27892000.0	39372000.0	28430000.0	46040000.0
+METTL26	1687400000.0	1635700000.0	1902400000.0	1944000000.0
+SPP2	119260000.0	137280000.0	172700000.0	152690000.0
+DDAH1	2547900000.0	2490000000.0	2409000000.0	2902700000.0
+SPHK2	24051000.0	19238000.0	12814000.0	17246000.0
+IFIH1	1711500.0	0.0	4157300.0	3620000.0
+DPT	151390000.0	167460000.0	260330000.0	286890000.0
+IPO11	1149900.0	0.0	0.0	0.0
+EEF1A1	8212600000.0	8517700000.0	7258600000.0	7119400000.0
+HMGB1	1081100000.0	1489500000.0	1323400000.0	1028700000.0
+NADSYN1	113770000.0	6961900.0	109690000.0	110230000.0
+FYCO1	25519000.0	32137000.0	0.0	0.0
+RAB2A	772460000.0	719200000.0	756150000.0	790920000.0
+RAB2B	25114000.0	16463000.0	0.0	0.0
+SRBD1	1105900.0	0.0	0.0	0.0
+ABAT	1164400000.0	1258700000.0	1034100000.0	1102200000.0
+ARHGEF2	5098900.0	8141700.0	8172000.0	11913000.0
+NFX1	5866900.0	0.0	6979000.0	8153300.0
+WDR45B	21886000.0	27828000.0	23068000.0	25707000.0
+DPYD	667600000.0	61144000.0	619680000.0	572518000.0
+AASS	169070000.0	52588000.0	81150000.0	76897000.0
+RDH5	15451000000.0	14009000000.0	16861000000.0	16638000000.0
+PEX14	23261000.0	27303000.0	29377000.0	95545000.0
+DPYS	7830700000.0	8062815000.0	9949728000.0	9439800000.0
+CLIP1	2594500.0	0.0	2500000.0	0.0
+ERAP2	2604600.0	0.0	7113200.0	5801100.0
+TIMM50	588350000.0	686280000.0	763690000.0	843270000.0
+ENSBTAG00000047529	941830000.0	1089800000.0	1011300000.0	1196300000.0
+DHRS7B	626430000.0	670140000.0	913450000.0	926770000.0
+CD46	31723000.0	38202000.0	19874000.0	31461000.0
+DMTN	3362400.0	0.0	10056000.0	0.0
+HIKESHI	279860000.0	249990000.0	92320000.0	92610000.0
+FPGT	0.0	0.0	0.0	10602000.0
+RAB21	722400000.0	730850000.0	617560000.0	708770000.0
+CFH	13710300.0	14328000.0	0.0	0.0
+PNP	17400000000.0	16460000000.0	13607000000.0	14151000000.0
+CALR	1951500000.0	2309300000.0	2060400000.0	1938700000.0
+CYB5A	43743000000.0	40782000000.0	43377000000.0	44614000000.0
+CYB5B	767030000.0	881930000.0	1064100000.0	1331500000.0
+NGP	0.0	20004000.0	90822000.0	76152000.0
+PIK3CB	720850.0	0.0	0.0	0.0
+HNRNPUL2	105420000.0	0.0	72945000.0	81483000.0
+ACTR1A	45179000.0	31150000.0	20313000.0	14220000.0
+PNN	80190000.0	15072000.0	18783000.0	9707700.0
+CHID1	237290000.0	249810000.0	215450000.0	208540000.0
+EWSR1	5933000.0	0.0	0.0	4365900.0
+PCK2	12761000000.0	13602000000.0	11250000000.0	11005000000.0
+RPN1	728920000.0	757820000.0	718510000.0	740780000.0
+TRIM13	10152000.0	0.0	0.0	0.0
+PCK1	4050800000.0	3979100000.0	2518500000.0	2623000000.0
+NTAN1	258360000.0	222820000.0	312640000.0	263420000.0
+NQO2	6187900000.0	6361700000.0	6594700000.0	7359800000.0
+NQO1	904130000.0	1059600000.0	746650000.0	832030000.0
+PRPF8	5955400.0	0.0	0.0	0.0
+CYP4F2	101860000.0	81436000.0	86279000.0	119150000.0
+PRPF6	11063000.0	0.0	15397000.0	13579000.0
+SAO	104140000.0	36364000.0	83749000.0	68739000.0
+CD177	2571100.0	0.0	22693000.0	23694000.0
+ESRP2	31452000.0	25819000.0	24752000.0	37392000.0
+CNPY4	78288000.0	107650000.0	113610000.0	105110000.0
+CNPY3	167770000.0	194140000.0	207030000.0	200080000.0
+CNPY2	732560000.0	947190000.0	440440000.0	500820000.0
+UFD1	126730000.0	145240000.0	95194000.0	111810000.0
+HDGF	68326000.0	73505000.0	81632000.0	92286000.0
+PIP4K2A	10673000.0	10341000.0	9843900.0	12262000.0
+PIP4K2B	67126000.0	87265000.0	102820000.0	106500000.0
+ARG1	58005000000.0	53362000000.0	43686000000.0	39897000000.0
+MRPL39	87432000.0	74092000.0	71958000.0	67845000.0
+PPP2R5A	17275000.0	18111000.0	24305000.0	22724000.0
+TBL2	282570000.0	292460000.0	313330000.0	315720000.0
+THOC6	32239000.0	32733000.0	52646000.0	41078000.0
+THOC5	16276000.0	22840000.0	1433900.0	11719000.0
+FAM45A	74621000.0	71954000.0	70919000.0	179150000.0
+CSAD	186550000.0	164720000.0	226960000.0	269820000.0
+MGC127055	720940000.0	743040000.0	1062500000.0	1197800000.0
+HSD3B7	484000000.0	383950000.0	694740000.0	655210000.0
+UQCRFS1	2101200000.0	1259200000.0	1537600000.0	2022200000.0
+ABCE1	56426000.0	64061000.0	70844000.0	85626000.0
+UQCRC2	3275600000.0	3474600000.0	3228500000.0	3262500000.0
+XPNPEP1	132910000.0	128610000.0	103340000.0	110780000.0
+XPNPEP2	7451300.0	26296000.0	37848000.0	32488000.0
+UQCRC1	2145600000.0	2058200000.0	1880000000.0	2180500000.0
+NUP88	73984000.0	25002000.0	28142000.0	40247000.0
+COPS8	357370000.0	344330000.0	422080000.0	446890000.0
+DDX42	2739200.0	0.0	5311700.0	7086400.0
+RETSAT	262670000.0	264170000.0	399930000.0	387770000.0
+NSDHL	4573500000.0	4450900000.0	5438900000.0	5131100000.0
+COPS2	42431000.0	44761000.0	32396000.0	37426000.0
+COPS3	24286000.0	30803000.0	16373000.0	14887000.0
+PGAM1	18876000000.0	18124000000.0	21795000000.0	21035000000.0
+KHDRBS1	84419000.0	124780000.0	109860000.0	111030000.0
+NUBPL	107590000.0	94953000.0	147250000.0	117260000.0
+COPS5	156510000.0	172850000.0	189960000.0	222370000.0
+PPOX	67183000.0	52809000.0	96132000.0	86917000.0
+ENSBTAG00000034189	10268000000.0	8968500000.0	8618000000.0	8947000000.0
+ENSBTAG00000014988	196250000.0	171830000.0	228260000.0	291040000.0
+DDX46	19016000.0	10204000.0	12633000.0	13227000.0
+TKT	5540000000.0	5862400000.0	6042700000.0	5681300000.0
+TNS3	74044000.0	80977000.0	59513000.0	76181000.0
+TNS2	6043500.0	0.0	4824700.0	3555700.0
+SDF2L1	1032600000.0	1028900000.0	1094100000.0	1060300000.0
+FDFT1	281340000.0	279520000.0	90249000.0	98227000.0
+NLN	4985700.0	10045000.0	0.0	16247000.0
+NUDT8	155510000.0	169630000.0	175090000.0	161590000.0
+NUDT9	80772000.0	80986000.0	63533000.0	65679000.0
+TARS2	363880000.0	134640000.0	340030000.0	387030000.0
+DPYSL2	350000000.0	333720000.0	380520000.0	417180000.0
+HEXA	75049000.0	76646000.0	87869000.0	82747000.0
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+PABPC4	75009000.0	83386000.0	50404000.0	61454000.0
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+JSP.1	54883000.0	103500000.0	59392000.0	94205000.0
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+PIK3R4	4249900.0	0.0	0.0	0.0
+PIK3R1	3783300.0	0.0	4150500.0	4660700.0
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+OS9	34952000.0	16090000.0	48385000.0	65828000.0
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+DSTN	128120000.0	123830000.0	36265000.0	33960000.0
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+ENSBTAG00000047868	88564000.0	110880000.0	107790000.0	96271000.0
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+G6PD	55189000.0	55741000.0	68413000.0	95358000.0
+HMOX1	2070900000.0	2136900000.0	1795300000.0	1898100000.0
+USP10	36746000.0	43844000.0	45100000.0	34493000.0
+HMOX2	615617000.0	726136000.0	527020000.0	638961000.0
+ELMO3	1760100.0	0.0	0.0	0.0
+AASDHPPT	97840000.0	105810000.0	89461000.0	99286000.0
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+TUBA8	34374000.0	37694000.0	30326000.0	27279000.0
+RAD23B	23657000.0	72828000.0	28078000.0	39228000.0
+EHD2	11750000.0	8164800.0	7500300.0	7027100.0
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+ZER1	931480.0	0.0	2859700.0	2585300.0
+RPS18	112970000.0	107120000.0	82309000.0	85997000.0
+IYD	148360000.0	174420000.0	125030000.0	108810000.0
+STOM	2047600000.0	2194800000.0	3056200000.0	2989300000.0
+RBKS	121110000.0	146550000.0	178560000.0	198220000.0
+NPLOC4	23630000.0	34367000.0	27489000.0	22626000.0
+IDE	6399000.0	0.0	14748000.0	13397000.0
+MBLAC2	88137000.0	86568000.0	81174000.0	68798000.0
+ENSBTAG00000038171	3519700.0	0.0	0.0	0.0
+FAHD2A	5571100000.0	5918600000.0	5208700000.0	4970800000.0
+TMED4	666600000.0	777420000.0	543140000.0	535360000.0
+SNX5	483730000.0	360640000.0	400850000.0	392090000.0
+TMED1	45074000.0	41812000.0	52257000.0	57954000.0
+CGN	133130000.0	5419300.0	44349000.0	45761000.0
+HNRNPR	220300000.0	232720000.0	219180000.0	268950000.0
+ENSBTAG00000035584	4798300.0	0.0	8169600.0	10749000.0
+TMED9	2893300000.0	3302600000.0	2770400000.0	2433800000.0
+AP2M1	174340000.0	208170000.0	246860000.0	254070000.0
+HNRNPK	2413900000.0	2438900000.0	1992700000.0	1777600000.0
+HNRNPL	463100000.0	534120000.0	464780000.0	478980000.0
+HNRNPM	291523900.0	312651000.0	334282000.0	364367000.0
+PML	56936000.0	27323000.0	38091000.0	34213000.0
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+HNRNPD	1250100000.0	1148800000.0	1062700000.0	1007800000.0
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+HNRNPF	277060000.0	253410000.0	378080000.0	291820000.0
+TMEM205	68459000.0	195260000.0	191020000.0	126030000.0
+FMO1	5945400000.0	6490600000.0	9987500000.0	10148000000.0
+ENSBTAG00000038549	232750000.0	249300000.0	373940000.0	424670000.0
+RCC2	27806000.0	28080000.0	16173000.0	18826000.0
+PBLD	3249700000.0	3513100000.0	4686300000.0	4380100000.0
+EEF1B2	562230000.0	481700000.0	667050000.0	653720000.0
+ERP44	1348000000.0	1498600000.0	1297900000.0	1316400000.0
+GCAT	5382400000.0	4961300000.0	5530800000.0	6278300000.0
+STAU1	29957000.0	37825000.0	31143000.0	28898000.0
+FAAH	17439000.0	9643300.0	23832000.0	14821000.0
+VPS26C	113060000.0	116510000.0	122570000.0	133220000.0
+PSPH	407370000.0	442680000.0	374170000.0	435570000.0
+UGT1A1	1518800000.0	1368130000.0	1768610000.0	1982390000.0
+ENSBTAG00000002943	108960000.0	116710000.0	66086000.0	62555000.0
+PGPEP1	318030000.0	246580000.0	315110000.0	345340000.0
+GLUD1	29655000000.0	29556000000.0	31977000000.0	29422000000.0
+ENSBTAG00000037613	79423000.0	55485000.0	131260000.0	149400000.0
+TIMM29	105770000.0	93994000.0	102570000.0	136000000.0
+HSP90AA1	5334700000.0	1532400000.0	1907700000.0	2031200000.0
+TIMM23	112960000.0	93899000.0	151110000.0	108220000.0
+TIMM22	0.0	0.0	0.0	0.0
+TIMM21	64127000.0	57016000.0	38849000.0	31862000.0
+KARS	115320000.0	99866000.0	98437000.0	106740000.0
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+TXNDC11	2247200.0	0.0	0.0	0.0
+CD58	23693000.0	25817000.0	72580000.0	44734000.0
+SCP2	5739600000.0	5868200000.0	6372300000.0	6054700000.0
+MSN	142050000.0	31677000.0	88961000.0	92443000.0
+ZNF326	5418500.0	0.0	20543000.0	21723000.0
+CACYBP	1195400000.0	1393100000.0	1200600000.0	913130000.0
+PDE2A	2195700.0	0.0	1515100.0	0.0
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+SEC31A	352840000.0	253140000.0	496690000.0	422670000.0
+PRRC1	173280000.0	144380000.0	105560000.0	97495000.0
+RAB35	184150000.0	210390000.0	210900000.0	146690000.0
+SRP54	16870000.0	0.0	17183000.0	0.0
+RAB32	199490000.0	188260000.0	351400000.0	358680000.0
+CAND1	143430000.0	0.0	113590000.0	111100000.0
+PRPS1	653600000.0	756410000.0	811960000.0	707820000.0
+DAO	333390000.0	272980000.0	483960000.0	495640000.0
+TATDN1	100810000.0	103650000.0	152670000.0	202680000.0
+ENSBTAG00000037509	3239100000.0	3031800000.0	5003900000.0	4721000000.0
+RPIA	529880000.0	493570000.0	486610000.0	396180000.0
+TTLL12	137580000.0	31001000.0	102560000.0	151130000.0
+LDHD	30163000.0	23224000.0	57165000.0	30997000.0
+GMPR2	1031600000.0	1196000000.0	1168800000.0	1186300000.0
+LDHA	14870000000.0	13195000000.0	7208500000.0	6815000000.0
+LDHB	90843000000.0	88904000000.0	60403000000.0	58145000000.0
+LDHC	536100000.0	426860000.0	448290000.0	374210000.0
+LONP2	43884000.0	15621000.0	38450000.0	34587000.0
+PLS3	741000000.0	726080000.0	702790000.0	743600000.0
+LONP1	553770000.0	100790000.0	301640000.0	541030000.0
+PRPS1L1	14452000.0	143990000.0	204130000.0	173310000.0
+PDIA6	5536300000.0	5153100000.0	5742700000.0	5418200000.0
+PDIA5	287100000.0	277620000.0	269670000.0	311580000.0
+PDIA4	3752300000.0	1290600000.0	1720300000.0	1680700000.0
+PDIA3	11361000000.0	11404000000.0	10557000000.0	10780000000.0
+GAPDHS	0.0	27573000.0	0.0	10917000.0
+CFL2	273520000.0	244060000.0	155080000.0	116930000.0
+CFL1	2096000000.0	1982600000.0	1271000000.0	1574900000.0
+PLAA	21632000.0	30059000.0	23076000.0	24565000.0
+BICD2	588990.0	0.0	0.0	0.0
+SEC22B	702980000.0	952600000.0	1044700000.0	1106500000.0
+NFIA	72864000.0	84889000.0	132520000.0	107650000.0
+CD163	11492000.0	0.0	11306000.0	5193400.0
+AARS	146460000.0	9450400.0	61691000.0	59180000.0
+PDCD6IP	385510000.0	34680000.0	144850000.0	133720000.0
+TSGA10	10537000.0	12086000.0	8208100.0	0.0
+DNM1L	11295000.0	18871000.0	21188000.0	19447000.0
+SERPINH1	65738000.0	86493000.0	87575000.0	75870000.0
+FOLR3	71727000.0	76916000.0	40142000.0	41058000.0
+C5orf51	101150000.0	94942000.0	112990000.0	90720000.0
+GSTK1	3823200000.0	3929400000.0	4085200000.0	4575800000.0
diff -r 000000000000 -r 0072d7fe861a test-data/transcript_data.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/transcript_data.tabular	Thu Dec 20 15:12:46 2018 -0500
@@ -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
+HSPA6	0.350288503153011	0.592013587104232	0.612568173305459	0.833794197245084
+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
+CSNK2A2	20.6258110499688	20.054472820524	18.5817134405737	18.3292680140834
+PMM2	21.8385112586243	23.0035148281517	16.003600132562	17.2109074501839
+PMM1	8.10344421010251	6.45300943797042	5.29673056867481	5.29511229568809
+ASS1	1516.70059430521	1464.12181445199	987.76849371654	994.644000920779
+SPR	29.8024212677962	31.4255886777817	31.79677566883	28.6670221909796
+RNF114	33.1294000243847	33.6664789344251	28.9252501819995	29.2816301523686
+UCHL3	24.423075481539	27.4024225580356	29.922955743965	26.0349789575326
+ENSBTAG00000047256	0.198684163979848	0.326976011762912	0.0965464670861903	0.195629128584535
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+SCLY	18.3455097325074	12.0439548297371	14.5431284057665	13.4342441387722
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+PTRH2	17.909813910486	11.7471606497093	10.9263216383985	12.0202004570597
+HNRNPDL	37.5925704923559	41.1578984184042	54.6425179347757	52.9859134623444
+ENSBTAG00000024233	37.7416967628295	39.5317543165011	38.5879336019462	39.9028585669302
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+TRAPPC5	45.0403443110014	42.0513255117671	28.9286451423378	32.7199255117285
+ACLY	13.4740474442297	14.3387262055932	9.58029287059529	10.3337981947598
+CTBP2	5.99360085192578	4.51762221965351	4.98093076698102	6.42950200799512
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+ENSBTAG00000021558	21.1194017135072	17.7807242658146	20.1526593853834	18.2217376654255
+HACL1	246.657620535078	256.567529495172	229.415834596451	220.138080113882
+ITGA1	9.29786949411024	9.76396771663312	13.5013428589913	14.8294210742118
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+ITGA6	11.8735075257508	12.6555131238251	15.482031234154	15.5906619691222
+PDCD10	18.7315219469136	22.601547803977	21.2323631686951	23.5166048769705
+ENSBTAG00000014423	145.95935840011	143.533614530968	183.076474422877	181.551319884355
+EDC4	5.52923753091842	5.7877027035443	5.23171974237195	5.96904629810924
+APOA4	1001.67305558535	942.744279140933	107.00083069641	106.440618749974
+APOA5	1029.86419861909	979.325280504821	674.842850735208	668.048257430335
+UFSP2	16.2749903128682	20.256738769108	18.7220238728349	15.9515918998196
+XPO1	16.485245004257	16.8668226905226	18.4411687253671	18.2090074546333
+CSDE1	88.2990636083208	94.874140787585	111.6583797805	112.40205326067
+XPO7	15.7401949501504	14.2091535237298	17.7515728413077	18.037145679691
+MRPL28	52.9893031681559	48.9584949101096	42.2163527456254	40.7325874477505
+TIPRL	20.3593806662543	18.8266323736025	22.1406027919464	23.9116420800724
+LSS	69.4405230982464	67.9177325717943	34.6165192487874	36.4756780332274
+PSMG2	24.3340985914468	23.1473288109428	20.1222558498083	19.2832537875864
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+COL4A1	18.7737518722348	17.9754153048966	14.7656650528014	14.1620652210673
+MOGAT1	29.214304014299	27.2106358144939	48.813385462078	45.6665374274383
+RBM12B	10.2220215777789	9.08546549239542	9.59408615710437	9.39417720968644
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+PSMD13	43.6839875293078	48.0199832997457	51.2565442656727	54.7745310242926
+PSMD12	32.3869688289328	35.9623944517067	34.9204575429278	32.647504142095
+ENSBTAG00000039321	112.259112376834	100.238484530008	58.6067447808408	59.3027474433137
+FTCD	309.573136539452	295.074871337398	274.566642289599	275.934257978524
+GIT1	5.98665615660445	4.13219088841018	4.31070407993306	4.07519319098542
+SERBP1	92.6844087711888	103.442915624876	117.427762984193	118.124478506835
+PPP4C	55.7197885548943	55.2493692622856	43.4455864375624	45.176077879304
+OCRL	10.3264092206792	10.9789449669842	11.959814900627	10.4136711254052
+GANAB	64.3122238655378	65.0695408590483	71.3203377582816	74.2726862141167
+NUP133	4.45902286123096	4.84535662254183	6.72438653093649	6.17882466580224
+LRPAP1	39.6607118298376	39.667324289904	29.5432412799297	27.3187118073329
+VAPB	33.8702904392001	30.2693811622097	31.4173326842444	29.2929310980619
+VAPA	76.1546605041493	81.3371215996031	81.6706014323395	82.1110532113614
+SHROOM1	21.4810018134023	18.4561566411643	21.5571482143965	20.697368126951
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+MTIF3	33.0636275228121	29.8367202861899	27.5634090338781	27.5477833560371
+MIOS	4.26591122111643	5.21359359064982	7.12579399384903	6.83589766649823
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+IGF2R	20.3869302286559	18.4169743231909	21.5848513751896	21.119359211056
+CTH	23.7452527235334	28.0730838392641	20.5257484503605	17.8868290710971
+ARL6IP1	366.492111230662	413.904559500269	341.945846819416	344.091482581901
+SPART	22.0039686136008	22.2710603819198	24.2563861964032	24.3687278917066
+ABHD14B	139.93182580896	142.076678793288	138.017203335669	141.447561047386
+NECAP2	32.342511082654	29.1683949165178	30.9004923222674	31.8135482422216
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+USE1	51.4294604903283	60.1765270531091	49.1277795881554	49.9988075593112
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+ENSBTAG00000005501	20.9531418186579	22.1767942941081	30.8568907869777	33.317244513808
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+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
+TRAF2	17.6922565355256	16.5567600933531	14.8677082484315	15.174662414789
+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
+KANK3	3.88160768562745	3.37206389016101	4.57182970077055	4.92008891661378
+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
+PSPC1	12.2012434006219	9.33301345103818	11.0509936320832	11.7103926257532
+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
+MIB1	11.2969217388449	12.6857075332999	14.6376093514358	14.8076575661683
+COMMD5	14.9729052768571	12.0119703280403	10.1518732721567	10.6240689329861
+FAM114A1	10.9614110026776	10.6768022497942	14.9010459406044	12.1095165055568
+GRWD1	8.13231664985954	8.50209199369757	6.91781807161184	8.18598869761708
+FAM114A2	12.7070834818734	13.5273777637497	14.000301626201	16.5049843578312
+METTL13	4.66237668907224	4.45263582848967	4.22166534269016	3.34234547634677
+NADK2	54.8159931271154	52.6332284940626	47.723223058269	47.9553164239419
+C8H9orf64	14.5302311481385	15.1689462703979	15.9533725986414	15.6793108137285
+TMED2	250.98492168902	245.229752263095	249.936636136819	239.138468221124
+CAMK2D	18.7884764987695	16.7699130914659	17.4368419615483	21.0532777003412
+PTGR1	728.696161850511	750.221196300767	495.366183414484	501.366318588195
+PTGR2	97.046283254187	106.374231616666	124.486181953854	125.05017515939
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+MTUS2	4.31718926531321	5.54641885151013	7.38959579507357	6.88167947570886
+MACROD1	71.3409101946829	56.6765415914034	61.2466842310515	66.1484926170904
+COQ8A	23.9487750736404	22.2144870984009	20.140197488807	18.2001640230715
+ENSBTAG00000032350	814.833615049731	862.160755335367	834.393616949251	846.807463464239
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+CYP3A5	2650.92568427163	2822.95728007586	3104.9676213905	3065.56732713001
+UBE4B	14.0926461344646	12.3794305253518	14.9016206806154	14.6225200903442
+UBE4A	28.4702742009415	31.1338448614896	31.9628644503406	35.8269852048431
+AKAP2	20.4793594806008	22.9233539157229	22.7779453437442	22.2099296365008
+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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+MTREX	6.90808150195829	8.32357076755438	10.7170103796345	10.0672229266973
+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
+CAPZB	52.0510055090945	56.3445745451837	59.6470091326416	51.4380298805275
+ENSBTAG00000047632	0.0768529813312971	0.364114005526894	0.363463933908203	0.930332115479812
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+TANGO2	14.2002202152655	14.2006518370555	22.39373419956	14.0855878751688
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+HIP1R	9.69122839482908	6.79727210896662	5.96980665961365	4.90301748935779
+PIKFYVE	2.9653036010696	2.59690924386061	3.75056548627301	3.64681689681478
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+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
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+GLYATL3	88.3069651281587	96.3898545694085	166.25009894012	171.481526558662
+ENSBTAG00000023978	3.94359952176421	1.99618469037055	1.96462804870536	2.70838143219408
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+RAP1B	31.9646413856528	32.8393258833764	45.9772741268579	46.4082042485821
+RAP1A	51.9239423204728	64.3117082817351	71.8486976076003	70.9906569388079
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+RHOA	177.382073759642	171.018922395861	189.297872811329	181.368605583589
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+CYP4A11	579.085869415823	587.848406930167	512.732683446841	514.542921241511
+ACTN4	100.633352280165	89.499926365866	67.1056812644732	68.4497784723153
+EZR	17.5041415080775	17.6369564693945	20.3298980060406	21.5370597198522
+GLYAT	1620.75641806911	1632.20607950237	1680.52602930246	1642.67906576899
+G3BP2	20.2581753634458	23.5313252466533	26.5404963119162	25.8373194464335
+PTPRM	9.15356066519333	8.57338509420922	13.8393657908032	13.0773782679421
+MYO7B	0.317952609778197	0.385858442819635	0.290044468187566	0.281728139410924
+STK10	15.4438125974873	15.1764646706086	17.0622387797986	16.8332595219954
+FKBP4	109.962968327887	102.241124718474	75.9741798496794	79.6174005777913
+SRSF10	48.3568872566459	51.4706979283994	50.5540306357587	49.1405405017313
+SEC23IP	9.77200823083704	11.0708718455968	11.213586181304	11.2258813683553
+FKBP3	43.266105478479	45.0021463291844	44.3906742907561	45.8929712493271
+FKBP8	99.574923967891	87.9015166600516	79.1429696024693	82.8317968948386
+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
+PPME1	14.5423492080999	17.2466823738509	14.8535177466358	15.9678749096679
+MANF	126.068146487134	126.773584164756	58.1224386487062	55.5531406663572
+HEBP1	155.206094699601	179.329719859147	208.236513315194	210.049818840196
+HBS1L	27.2220885680969	28.0024864704943	25.6632174505036	24.7007330892689
+VAC14	13.2801399855225	10.3819795490546	10.3087606047789	11.9164104939034
+WDR5	34.4262631249817	31.8444048037985	28.299631022021	33.1309167894989
+MAN2C1	8.23484281989396	8.05661177788604	7.93201461056424	8.63637774766572
+WDR1	30.7775670697141	28.4040154313901	26.3602696218109	25.6153008535878
+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
+KRT18	218.093238702214	216.3618602256	116.427632225427	113.747637654905
+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
+QPCTL	10.8934317641201	10.3611675832642	6.14801335202803	7.31840758432045
+FAM96A	83.5604033977766	81.8753339485662	100.705098471446	97.7509813736637
+ALDH9A1	369.60100412425	375.652906892365	467.513663330321	468.29409380829
+BPHL	37.0957702822177	36.3848642677326	36.8457200777081	37.6934445546045
+GLO1	224.717787601532	249.987787205914	256.943179224681	262.72104028686
+MVK	125.60023181104	108.228518627115	40.4961111521754	44.7611642620467
+ASRGL1	33.5773968102396	38.1004162020057	31.506081441533	23.8969301772807
+CMBL	244.547761199155	247.824305499274	212.5544579795	219.650090922384
+F7	89.3108883537718	79.657979284652	82.7400359105883	79.2599059032245
+F9	426.804996325805	449.261708949094	657.682836206112	671.683971414569
+LSP1	10.685143012409	9.16574359799813	9.44647670536338	8.79633772094805
+GSTZ1	481.724381644991	455.349139899186	373.854825072395	374.300472586966
+MARS	15.3764472503945	13.8439726646603	10.945887674502	11.726932700481
+TUBGCP2	15.5505209369877	16.5477224752994	14.9213950249214	12.7635934511203
+CUL3	15.8704700674725	17.4373319348964	19.5001816059124	20.0057604295144
+NBEAL1	4.9137281277519	4.53394265347196	5.67717614153071	5.75377544684986
+CFD	169.572889666805	153.74770435413	271.203175210289	265.400065153213
+VAV2	12.6516175724925	12.0941203194214	11.3925137259513	12.1189190596617
+PSMD9	23.584959735025	25.9427439127782	24.1683615907499	25.4170844088805
+PSMD8	78.9155023047106	72.084579475786	64.5484277164876	66.7238457648302
+PSMD5	19.2251994427441	19.7371391961841	18.1467352758453	19.6527044093147
+PSMD4	74.0916859676512	69.8503533971009	53.2759828432836	56.7207402594667
+PSMD7	66.3621270037664	67.8644378082165	56.0752946369566	56.8458842451995
+FLAD1	50.2968340545894	44.4697945876749	23.6174173285843	24.7786715369235
+PSMD1	45.6762609644219	49.9714910712316	42.9877891606649	41.2035152789946
+PSMD3	68.7214343109245	63.8492567116916	52.5687704959083	51.2853211038092
+PSMD2	66.4527031810412	69.9147686122521	54.8433695195866	53.3526890528518
+CTNNB1	90.4023761157358	94.2750388257147	108.506696375932	113.964268569156
+NGFR	19.1774697255707	18.0444728424581	26.6718407687433	27.510004777236
+CPSF7	13.8229252870417	12.5535338220972	14.0785685250771	14.13059212865
+MOB1A	17.1161090658869	19.8665198600251	27.3172587044961	25.489204435624
+CPSF3	13.5551056525383	10.815894113857	12.0101451807324	13.4628356627835
+CPSF2	12.7661162149239	11.993364680498	11.7267251606951	12.3843592141942
+CPSF1	10.5691038631671	9.71324877219874	8.44757539251843	8.37712425810752
+EPS15	26.6887488463586	28.6326444493031	28.1434880218617	29.9773339337446
+QKI	32.2514136114333	30.7416864445002	50.0541146635714	46.8031940398972
+ENSBTAG00000014220	306.638646627431	323.210113710557	673.515631149979	668.28946463748
+MAD1L1	3.55481954610918	3.91530353255797	3.71601168804164	2.78618622837252
+CATHL2	-0.0818278270922784	0.442435441480108	-5.98126814062572e-05	0.0122583485392512
+CATHL3	-0.0758996600312405	0.135819323886117	-0.00624862361024775	0.00569716975406065
+CATHL1	-0.0829887190073607	0.50247898753471	0.00115212019722091	0.0135432008935647
+HSD17B8	60.4396472715786	58.6775582149661	52.9804491462764	53.1333013601698
+HMGCL	81.6564342326211	71.1276535423798	105.068100355329	100.787743958003
+ACAA1	666.983826883534	626.614972747982	599.533308779113	584.456666588973
+ILVBL	43.9792942485372	40.2561199484798	36.5702195393003	39.2678639478191
+CNOT7	55.8648458541427	59.7597469723131	64.9942217227111	58.8878336221171
+HSD17B6	870.388208687256	893.578986665181	743.77417131561	752.508349522779
+HSD17B7	56.1195772229482	60.9560861879787	36.8843720374126	35.7389118187248
+ITGB1	80.0144485394017	90.6487986872208	87.0877351821985	86.517405225761
+ITGB3	14.8960084727247	17.3223072479106	23.7762578498425	21.4064864572844
+ENSBTAG00000039289	32.3199552668778	35.0880352545981	13.3313286585454	11.9553262202517
+ITGB5	53.2409932243542	52.8868652524672	63.3156349727449	65.0853331632312
+ITGB6	0.249409563345128	0.423590544960952	0.273549409492523	0.0775742434811864
+RTN4	234.420300375363	248.412045892992	262.078709884684	253.338160818239
+HAX1	29.6499633498052	27.2720483057226	27.4691710571091	25.5606703788432
+KPNB1	38.5863928993263	39.5934007060507	32.8980606930222	34.0116038913187
+HGS	19.8591419118765	17.0893346072986	16.2013949614545	16.5194381610456
+R3HDM1	3.73238709317183	4.51682543882832	3.2322272091072	3.51674048192889
+MST1	187.863010724972	176.96789577774	158.925524640767	159.791818306159
+HGD	907.379627269024	936.063625032695	911.160754243876	915.2077258314
+PRMT5	19.1367468832104	18.4210129182983	14.4054343631021	15.4746265283109
+COPS7B	10.8711954237715	8.60426859215828	8.62808031838259	8.81889986745711
+COPS7A	34.1163113080537	35.5617824243021	32.8001678081673	32.891594608121
+CRAT	138.027999952401	131.944493882948	95.2372236771034	97.4378295153783
+PPFIA1	10.5155743913038	12.2298732559008	8.73034800129126	9.73573303176059
+MYO6	10.9630981523458	12.5649633983582	15.3159298780359	16.0232880966266
+RABGGTB	46.5237313596009	48.3817508110802	60.8431745012983	58.6706823906761
+NAP1L4	14.974542241789	13.9449104036876	15.7924973150127	16.7832295545638
+KIF13B	3.08733216577438	3.27128041761237	4.49878228245069	4.38267392165231
+RABGGTA	7.3595766089214	6.47852923342972	6.18478112958811	5.59796425700703
+ENSBTAG00000019601	6.38828637783438	4.695896961653	8.0658783655894	8.40329733438033
+FSHB	-0.0758996600312405	0.135819323886117	-0.00624862361024775	0.00569716975406065
+ENSBTAG00000044137	-0.0758996600312405	0.135819323886117	-0.00624862361024775	0.00569716975406065
+HUWE1	21.953457255099	22.1564759750859	23.6551714742396	24.1416329925867
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+DLAT	9.76486274713574	10.0629384608034	10.252222535635	9.8843259031543
+DNM2	22.6381125407442	20.7047151059998	16.8311293711382	16.9572373751209
+DLGAP5	6.94168320497557	7.00679498784597	2.15170361399677	2.25614640502072
+ADPRH	19.0551036954799	21.6633529080608	20.5196433017802	17.3422729463392
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+TRABD	41.5119353517831	40.0943537624348	34.0813877205476	34.2955447657234
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+TUBA1D	2.86316992433849	1.98730260289615	0.991472233976692	0.913767360463091
+ARHGEF37	7.64007537466363	9.05655908795872	7.62489155376112	6.66173528565187
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+OS9	190.626531539601	182.701322081904	230.402995583996	228.470274325779
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+ENSBTAG00000038171	196.268578645567	207.760567600323	241.621757337222	240.881711567728
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+TMED4	46.6040708253926	49.970480253914	39.3924497379826	41.1908906015733
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+HNRNPL	63.0577494611125	62.4953862674554	63.5605075649486	63.8489052278032
+HNRNPM	81.4352431238127	80.2727854827795	64.8668230991941	64.0386505254954
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+ENSBTAG00000038549	108.747288945721	99.9133579846071	107.666944235911	110.615632930359
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+ENSBTAG00000002943	-0.0758996600312405	0.135819323886117	-0.00624862361024775	0.00569716975406065
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+ENSBTAG00000037613	450.803459117423	485.334795333524	458.480418159638	460.051420106146
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+TIMM23	48.2276937250432	49.5477610413749	38.2010716471407	39.5516764220633
+TIMM22	2.9779359455591	5.03513830922428	4.57766504719711	3.69019224325445
+TIMM21	22.9633660509231	25.5427128457841	22.9471924440751	22.7741109146881
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+CACYBP	65.8511268882502	67.8944752372627	52.7753585597001	60.9440250551207
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