# HG changeset patch
# User galaxyp
# Date 1545339965 18000
# Node ID bcc7a4c4cc299c4981c3cd353efb64e1531d3ad2
# Parent  75faf9a89f5bc83086fbcf0e38552aa54d9c0817
planemo upload commit 1887dff812162880d66b003a927867cd5000c98f

diff -r 75faf9a89f5b -r bcc7a4c4cc29 quantp.r
--- a/quantp.r	Fri Sep 14 12:22:31 2018 -0400
+++ b/quantp.r	Thu Dec 20 16:06:05 2018 -0500
@@ -16,6 +16,7 @@
   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();
 }
@@ -30,20 +31,20 @@
   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);
+      "<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$adj.r.squared,"</td></tr>\n",
-    file = htmloutfile, append = TRUE);
+      "<tr><td colspan='2' align='center'> <b>Coefficients</b></td>","</tr>\n",
+      "<tr><td>",names(regmodel$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);
   
@@ -58,13 +59,27 @@
   plot(regmodel, 1, cex.lab=1.5);
   dev.off();
   
+  suppressWarnings(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));
-  plot(regmodel, 2, cex.lab=1.5);
+  g <- plot(regmodel, 2, cex.lab=1.5);
+  ggplotly(g)
   dev.off();
   
+  suppressWarnings(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");
@@ -72,79 +87,94 @@
   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)}
+  
+  suppressWarnings(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(
+  cat(
     '<tr bgcolor="#7a0019"><th>', "<font color='#ffcc33'><h4>1) <u>Residuals vs Fitted plot</h4></font></u></th>\n",
     '<th><font color=#ffcc33><h4>2) <u>Normal Q-Q plot of residuals</h4></font></u></th></tr>\n',
     file = htmloutfile, append = TRUE);
   
   cat(
-    '<tr><td align=center><img src="PE_TE_lm_1.png" width=600 height=600></td><td align=center><img src="PE_TE_lm_2.png" width=600 height=600></td></tr>\n',
-    file = htmloutfile, append = TRUE);
+    '<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$residuals = res_all[tempind];
+  
+  
+  #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+  # Residuals data
+  #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+  res_all = regmodel$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);
+  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$PE_abundance, res_all)
-    colnames(temp_all_data) = c("Gene", "Transcript abundance", "Protein abundance", "Residual value")
-    outdatafile = paste(outdir,"/PE_TE_abundance_residuals.txt", sep="", collapse="");
-    write.table(temp_all_data, file=outdatafile, row.names=F, sep="\t", quote=F);
+  temp_all_data = data.frame(PE_TE_data$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);
+      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);
-    
+      '<tr bgcolor="#7a0019"><th colspan=2><font color=#ffcc33>Residuals from Regression</font></th></tr>\n',
+      '<tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr>\n',
+      file = htmloutfile, append = TRUE);
+  
   cat("<tr><td>Mean Residual value</td><td>",res_mean,"</td></tr>\n",
-    "<tr><td>Standard deviation (Residuals)</td><td>",res_sd,"</td></tr>\n",
-    '<tr><td>Total outliers (Residual value > 2 standard deviation from the mean)</td><td>',length(tempind),' <font size=4>(<b><a href=PE_TE_outliers_residuals.txt target="_blank">Download these ',length(tempind),' data points with high residual values here</a></b>)</font></td>\n',
-    '<tr><td colspan=2 align=center><font size=4>(<b><a href=PE_TE_abundance_residuals.txt target="_blank">Download the complete residuals data here</a></b>)</font></td></td>\n',
-    "</table><br><br>\n",
-    file = htmloutfile, append = TRUE);
-    
+      "<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></td></tr>\n',
+    '<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(
@@ -157,7 +187,7 @@
   # Cook's Distance
   #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
   cat('<hr/><h2 id="inf_obs"><font color=#ff0000>INFLUENTIAL OBSERVATIONS</font></h2>\n',
-    file = htmloutfile, append = TRUE);
+      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);
@@ -177,11 +207,31 @@
   #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];
@@ -195,49 +245,63 @@
     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);
+  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);
+  temp_no_outlier_data = data.frame(PE_TE_data_no_outlier$PE_ID, PE_TE_data_no_outlier$TE_abundance, PE_TE_data_no_outlier$PE_abundance, PE_TE_data_no_outlier$cooksd)
+  colnames(temp_no_outlier_data) = c("Gene", "Transcript abundance", "Protein abundance", "Cook's distance")
+  outdatafile = paste(outdir,"/PE_TE_non_influential_observation.txt", sep="", collapse="");
+  write.table(temp_no_outlier_data, file=outdatafile, row.names=F, sep="\t", quote=F);
   
   
   cat("<tr><td>Mean Cook\'s distance</td><td>",mean(cooksd, na.rm=T),"</td></tr>\n",
-    "<tr><td>Total influential observations (Cook\'s distance > ",cookdist_upper_cutoff," * mean Cook\'s distance)</td><td>",length(tempind),"</td>\n",
-    
-    "<tr><td>Observations with Cook\'s distance < ",cookdist_upper_cutoff," * mean Cook\'s distance</td><td>",length(which(cooksd<as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T))),"</td>\n",
-    "</table><br><br>\n",
-    file = htmloutfile, append = TRUE);
-    
-    
-    #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-    # Scatter plot after removal of influential points
-    #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-    outplot = paste(outdir,"/AbundancePlot_scatter_without_outliers.png",sep="",collapse="");
-    min_lim = min(c(PE_TE_data$PE_abundance,PE_TE_data$TE_abundance));
-    max_lim = max(c(PE_TE_data$PE_abundance,PE_TE_data$TE_abundance));
-    png(outplot, width = 10, height = 10, units = 'in', res=300);
-    # bitmap(outplot,"png16m");
-    g = ggplot(PE_TE_data_no_outlier, aes(x=TE_abundance, y=PE_abundance))+geom_point() + geom_smooth() + xlab("Transcript abundance log fold-change") + ylab("Protein abundance log fold-change") + xlim(min_lim,max_lim) + ylim(min_lim,max_lim);
-    suppressMessages(plot(g));
-    dev.off();
-    
-    cat('<table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatterplot: Before removal</font></th><th><font color=#ffcc33>Scatterplot: After removal</font></th></tr>\n', file = htmloutfile, append = TRUE);
-    # Before
-    cat("<tr><td align=center><!--<font color='#ff0000'><h3>Scatter plot between Proteome and Transcriptome Abundance</h3></font>\n-->", '<img src="TE_PE_scatter.png" width=600 height=600></td>\n', file = htmloutfile, append = TRUE);
-    
-    # After
-    cat("<td align=center>\n",
-    '<img src="AbundancePlot_scatter_without_outliers.png" width=600 height=600></td></tr>\n',
-    file = htmloutfile, append = TRUE);
-    #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-    
+      "<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");
+  suppressWarnings(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");
@@ -249,7 +313,7 @@
   
   
   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);
+      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",
@@ -267,13 +331,13 @@
   }
   
   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);
+      "<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);
+      file = htmloutfile, append = TRUE);
   
   
   for(i in 1:tab_n_row)
@@ -285,9 +349,9 @@
       '<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);
-    
-    
+  cat('</table><br><br>\n',file = htmloutfile, append = TRUE);
+  
+  
 }
 
 
@@ -297,7 +361,7 @@
 #===============================================================================
 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);
+      file = htmloutfile, append = TRUE);
   
   hc=hclust(dist(as.matrix(PE_TE_data[,c("PE_abundance","TE_abundance")])))
   hm_cluster = cutree(hc,k=hm_nclust);
@@ -306,11 +370,25 @@
   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);
+  hmap = heatmap.2(as.matrix(PE_TE_data[,c("PE_abundance","TE_abundance")]),
+                   trace="none", cexCol=1, col=greenred(100),Colv=F, 
+                   labCol=c("Proteins","Transcripts"), scale="col", 
+                   hclustfun = hclust, distfun = dist);
+  
   dev.off();
   
-  cat('<tr><td align=center colspan="2"><img src="PE_TE_heatmap.png" width=800 height=800></td></tr>\n',
-    file = htmloutfile, append = TRUE);
+  
+  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);
@@ -320,7 +398,7 @@
   
   
   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);
+      file = htmloutfile, append = TRUE);
 }
 
 
@@ -338,7 +416,6 @@
   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);
@@ -361,18 +438,30 @@
   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);
+      file = htmloutfile, append = TRUE);
   
   tempind = order(k1$cluster);
   tempoutfile = paste(outdir,"/PE_TE_kmeans_clusterpoints.txt",sep="",collapse="");
   write.table(data.frame(PE_TE_data_kdata[tempind, ], Cluster=k1$cluster[tempind]), file=tempoutfile, row.names=F, quote=F, sep="\t", eol="\n")
   
-  
-  cat('<tr><td colspan="2" align=center><img src="PE_TE_kmeans.png" width=800 height=800></td></tr>\n',
-    file = htmloutfile, append = TRUE);
+  #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);
+      file = htmloutfile, append = TRUE);
   
 }
 
@@ -385,9 +474,14 @@
   max_lim = max(c(PE_TE_data$PE_abundance,PE_TE_data$TE_abundance));
   png(outfile, width = 10, height = 10, units = 'in', res=300);
   # bitmap(outfile, "png16m");
-  g = ggplot(PE_TE_data, aes(x=TE_abundance, y=PE_abundance))+geom_point() + geom_smooth() + xlab("Transcript abundance log fold-change") + ylab("Protein abundance log fold-change") + xlim(min_lim,max_lim) + ylim(min_lim,max_lim);
-  suppressMessages(plot(g));
-  # plot(g);
+  suppressWarnings(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();
 }
 
@@ -422,23 +516,41 @@
   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$Sample,2];
   png(outplot, width = 6, height = 6, units = 'in', res=300);
   # bitmap(outplot, "png16m");
-  if(fill_leg=="Yes")
-  {
-    g = ggplot(tempdf1, aes(x=Sample, y=value, fill=Group)) + geom_boxplot() + labs(x=user_xlab) + labs(y=user_ylab)
-  }else{
-    if(fill_leg=="No")
-    {
-      tempdf1$Group = c("case", "control")
-      g = ggplot(tempdf1, aes(x=Sample, y=value, fill=Group)) + geom_boxplot() + labs(x=user_xlab) + labs(y=user_ylab)
-    }
+  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
@@ -446,35 +558,35 @@
 
 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));
+  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));
 }
 
 #===============================================================================
@@ -483,127 +595,162 @@
 
 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_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);
+  PE_adj_pval = p.adjust(PE_pval, method = correction_method, n = length(PE_pval))
+  
+  
+  TE_colnames = colnames(TE_df_data);
+  control_sample = sampleinfo_df[which(sampleinfo_df$Group=="control"),1];
+  control_ind <<- sapply(control_sample, function(x){temp_ind = which(TE_colnames==x); as.numeric(temp_ind)});
+  condition_sample = sampleinfo_df[which(sampleinfo_df$Group=="case"),1];
+  condition_ind <<- sapply(condition_sample, function(x){temp_ind = which(TE_colnames==x); as.numeric(temp_ind)});
+  
+  if(method=="mean"){
+    # TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) t.test(x[condition_ind-1], x[control_ind-1])$p.value);
+    TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) {obj<-try(t.test(x[condition_ind-1], x[control_ind-1]),silent=TRUE); if(is(obj, "try-error")){return(NA)}else{return(obj$p.value)}})
+  }else{
+    if(method=="median"){
+      TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) {obj<-try(wilcox.test(x[condition_ind-1], x[control_ind-1]),silent=TRUE); if(is(obj, "try-error")){return(NA)}else{return(obj$p.value)}})
+      # TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) wilcox.test(x[condition_ind-1], x[control_ind-1])$p.value);
+    }
   }
-}
-TE_adj_pval = p.adjust(TE_pval, method = correction_method, n = length(TE_pval))
-
-
-PE_TE_logfold_pval = data.frame(TE_df_logfold$Gene, TE_df_logfold$LogFold, TE_pval, TE_adj_pval, PE_df_logfold$LogFold, PE_pval, PE_adj_pval);
-colnames(PE_TE_logfold_pval) = c("Gene", "Transcript log fold-change", "p-value (transcript)", "adj p-value (transcript)", "Protein log fold-change", "p-value (protein)", "adj p-value (protein)");
-outdatafile = paste(outdir,"/PE_TE_logfold_pval.txt", sep="", collapse="");
-write.table(PE_TE_logfold_pval, file=outdatafile, row.names=F, sep="\t", quote=F);
-cat("<br><br><font size=5><b><a href='PE_TE_logfold_pval.txt' target='_blank'>Download the complete fold change data here</a></b></font><br>\n",
-    file = htmloutfile, append = TRUE);
-
-    if(length(condition_ind)!=1)
-        {
-        # Volcano Plot
-
-        if(volc_with=="adj_pval")
-        {
-            PE_pval = PE_adj_pval
-            TE_pval = TE_adj_pval
-            volc_ylab = "-log10 Adjusted p-value";
-        }else{
-          if(volc_with=="pval")
-          {
-            volc_ylab = "-log10 p-value";
-          }
-        }
-        outplot = paste(outdir,"/PE_volcano.png",sep="",collapse="");
-          png(outplot, width = 10, height = 10, units = 'in', res=300);
-          # bitmap(outplot, "png16m");
-          par(mfrow=c(1,1));
+  TE_adj_pval = p.adjust(TE_pval, method = correction_method, n = length(TE_pval))
+  
+  
+  PE_TE_logfold_pval = data.frame(TE_df_logfold$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);
   
-        plot(PE_df_logfold$LogFold, -log10(PE_pval),
-             xlab="log2 fold change", ylab=volc_ylab,
-             type="n")
-        sel <- which((PE_df_logfold$LogFold<=log(2,base=2))&(PE_df_logfold$LogFold>=log(0.5, base=2))) # or whatever you want to use
-        points(PE_df_logfold[sel,"LogFold"], -log10(PE_pval[sel]),col="black")
-        #sel <- which((PE_df_logfold$LogFold>log(2,base=2))&(PE_df_logfold$LogFold<log(0.5,base=2))) # or whatever you want to use
-        sel <- which((PE_df_logfold$LogFold>log(2,base=2))|(PE_df_logfold$LogFold<log(0.5, base=2)))
-        sel1 <- which(PE_pval<=0.05)
-        sel=intersect(sel,sel1)
-        points(PE_df_logfold[sel,"LogFold"], -log10(PE_pval[sel]),col="red")
-        sel <- which((PE_df_logfold$LogFold>log(2,base=2))|(PE_df_logfold$LogFold<log(0.5, base=2)))
-        sel1 <- which(PE_pval>0.05)
-        sel=intersect(sel,sel1)
-        points(PE_df_logfold[sel,"LogFold"], -log10(PE_pval[sel]),col="blue")
-        abline(h = -log(0.05,base=10), col="red", lty=2)
-        abline(v = log(2,base=2), col="red", lty=2)
-        abline(v = log(0.5,base=2), col="red", lty=2)
-        dev.off();
+  if(length(condition_ind)!=1)
+  {
+    # Volcano Plot
+    
+    if(volc_with=="adj_pval")
+    {
+      PE_pval = PE_adj_pval
+      TE_pval = TE_adj_pval
+      volc_ylab = "-log10 Adjusted p-value";
+    }else{
+      if(volc_with=="pval")
+      {
+        volc_ylab = "-log10 p-value";
+      }
+    }
+    outplot_PE = paste(outdir,"/PE_volcano.png",sep="",collapse="");
+    png(outplot_PE, width = 10, height = 10, units = 'in', res=300);
+    # bitmap(outplot, "png16m");
+    par(mfrow=c(1,1));
+    
+    plot(PE_df_logfold$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);
+  }
   
-
-
-        outplot = paste(outdir,"/TE_volcano.png",sep="",collapse="");
-          png(outplot, width = 10, height = 10, units = 'in', res=300);
-          # bitmap(outplot, "png16m");
-          par(mfrow=c(1,1));
-
-        plot(TE_df_logfold$LogFold, -log10(TE_pval),
-             xlab="log2 fold change", ylab=volc_ylab,
-             type="n")
-        sel <- which((TE_df_logfold$LogFold<=log(2,base=2))&(TE_df_logfold$LogFold>=log(0.5, base=2))) # or whatever you want to use
-        points(TE_df_logfold[sel,"LogFold"], -log10(TE_pval[sel]),col="black")
-        #sel <- which((TE_df_logfold$LogFold>log(2,base=2))&(TE_df_logfold$LogFold<log(0.5,base=2))) # or whatever you want to use
-        sel <- which((TE_df_logfold$LogFold>log(2,base=2))|(TE_df_logfold$LogFold<log(0.5, base=2)))
-        sel1 <- which(TE_pval<=0.05)
-        sel=intersect(sel,sel1)
-        points(TE_df_logfold[sel,"LogFold"], -log10(TE_pval[sel]),col="red")
-        sel <- which((TE_df_logfold$LogFold>log(2,base=2))|(TE_df_logfold$LogFold<log(0.5, base=2)))
-        sel1 <- which(TE_pval>0.05)
-        sel=intersect(sel,sel1)
-        points(TE_df_logfold[sel,"LogFold"], -log10(TE_pval[sel]),col="blue")
-        abline(h = -log(0.05,base=10), col="red", lty=2)
-        abline(v = log(2,base=2), col="red", lty=2)
-        abline(v = log(0.5,base=2), col="red", lty=2)
-        dev.off();
-
-
-
-        cat('<br><table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Transcript Fold-Change</font></th><th><font color=#ffcc33>Protein Fold-Change</font></th></tr>\n', file = htmloutfile, append = TRUE);
-            cat("<tr><td align=center>", '<img src="TE_volcano.png" width=600 height=600></td>\n', file = htmloutfile, append = TRUE);
-            cat("<td align=center>",
-            '<img src="PE_volcano.png" width=600 height=600></td></tr></table><br>\n',
-            file = htmloutfile, append = TRUE);
-    }else{
-        cat('<br><br><b><font color=red>!!! No replicates found. Cannot perform test to check significance of differential expression. Thus, no Volcano plot generated !!!</font></b><br><br>',
-        file = htmloutfile, append = TRUE);
-    }
-
 }
 
 
@@ -612,8 +759,6 @@
 #***************************************************************************************************************************************
 
 
-
-
 #===============================================================================
 # Arguments
 #===============================================================================
@@ -638,7 +783,6 @@
 htmloutfile = args[11]; # html output file
 outdir = args[12]; # html supporting files
 
-
 #===============================================================================
 # Check for file existance
 #===============================================================================
@@ -661,31 +805,33 @@
 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;
+  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"))
+  sampleinfo_df = data.frame("Sample"= c("LogFold"), "Group"=c("Fold_Change"))
 }
 
 #===============================================================================
@@ -694,12 +840,12 @@
 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)];
+  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");
+  TE_df = TE_df_orig;
+  colnames(TE_df) = c("Genes", "LogFold");
 }
 #===============================================================================
 # Parse Proteome data
@@ -707,12 +853,12 @@
 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)];
+  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");
+  PE_df = PE_df_orig;
+  colnames(PE_df) = c("Genes", "LogFold");
 }
 
 #=============================================================================================================
@@ -725,17 +871,17 @@
 #===============================================================================
 # 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",
+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",
+
+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",
@@ -793,6 +939,30 @@
 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
@@ -800,13 +970,13 @@
 if(mode=="logfold")
 {
   cat('<h2 id="sample_dist"><font color=#ff0000>SAMPLE DISTRIBUTION</font></h2>\n',
-  file = htmloutfile, append = TRUE);
+      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);
+      '<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
@@ -815,70 +985,93 @@
   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);
+      file = htmloutfile, append = TRUE);
   
   # TE PE scatter
   outplot = paste(outdir,"/TE_PE_scatter.png",sep="",collape="");
   cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatter plot between Proteome and Transcriptome Abundance</font></th></tr>\n', file = htmloutfile, append = TRUE);
-  cat("<tr><td align=center>", '<img src="TE_PE_scatter.png" width=800 height=800></td></tr>\n', file = htmloutfile, append = TRUE);
+  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");
-  singlesample_scatter(PE_TE_data, outplot);  
   
   # TE PE Cor
   cat("<tr><td align=center>", file = htmloutfile, append = TRUE);
   singlesample_cor(PE_TE_data, htmloutfile, append=TRUE);
   cat('<font color="red">*Note that <u>correlation</u> is <u>sensitive to outliers</u> in the data. So it is important to analyze outliers/influential observations in the data.<br> Below we use <u>Cook\'s distance based approach</u> to identify such influential observations.</font>\n',
-    file = htmloutfile, append = TRUE);
+      file = htmloutfile, append = TRUE);
   cat('</td></table>',
-    file = htmloutfile, append = TRUE);
+      file = htmloutfile, append = TRUE);
   
   cat('<hr/><h2 id="regression_data"><font color=#ff0000>REGRESSION ANALYSIS</font></h2>\n',
-  file = htmloutfile, append = TRUE);
+      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);
+      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);
+        file = htmloutfile, append = TRUE);
     
     # TE Boxplot
     outplot = paste(outdir,"/Box_TE_all_rep.png",sep="",collape="");
-    cat('<table  border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">\n',
-    '<tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n',
-    "<tr><td align=center>", '<img src="Box_TE_all_rep.png" width=500 height=500></td>\n', file = htmloutfile, append = TRUE);
     temp_df_te_data = data.frame(TE_df[,1], log(TE_df[,2:length(TE_df)]));
     colnames(temp_df_te_data) = colnames(TE_df);
     multisample_boxplot(temp_df_te_data, sampleinfo_df, outplot, "Yes", "Samples", "Transcript Abundance (log)");
+    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="");
-    cat("<td align=center>", '<img src="Box_PE_all_rep.png" width=500 height=500></td></tr></table>\n', file = htmloutfile, append = TRUE);
     temp_df_pe_data = data.frame(PE_df[,1], log(PE_df[,2:length(PE_df)]));
     colnames(temp_df_pe_data) = colnames(PE_df);
     multisample_boxplot(temp_df_pe_data, sampleinfo_df, outplot, "Yes", "Samples", "Protein Abundance (log)");
+    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);
@@ -889,107 +1082,131 @@
     
     # TE Boxplot
     outplot = paste(outdir,"/Box_TE_rep.png",sep="",collape="");
-    cat('<br><font color="#ff0000"><h3>Sample wise distribution (Box plot) after using ',method,' on replicates </h3></font><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n',
-    "<tr><td align=center>", '<img src="Box_TE_rep.png" width=500 height=500></td>\n', file = htmloutfile, append = TRUE);
     temp_df_te_data = data.frame(TE_df[,1], log(TE_df[,2:length(TE_df)]));
     colnames(temp_df_te_data) = colnames(TE_df);
     multisample_boxplot(temp_df_te_data, sampleinfo_df, outplot, "No", "Sample Groups", "Mean Transcript Abundance (log)");
-
+    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$widget_div, '</td>\n', file = htmloutfile, append = TRUE);
+    
     # PE Boxplot
     outplot = paste(outdir,"/Box_PE_rep.png",sep="",collape="");
-    cat("<td align=center>", '<img src="Box_PE_rep.png" width=500 height=500></td></tr></table>\n', file = htmloutfile, append = TRUE);
     temp_df_pe_data = data.frame(PE_df[,1], log(PE_df[,2:length(PE_df)]));
     colnames(temp_df_pe_data) = colnames(PE_df);
     multisample_boxplot(temp_df_pe_data, sampleinfo_df, outplot, "No", "Sample Groups", "Mean Protein Abundance (log)");
-
+    lines <- extractWidgetCode(outplot)
+    #prescripts <- c(prescripts, lines$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];
-  
+    
+    #===============================================================================
+    # 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$postscripts)
     cat('<br><font color="#ff0000"><h3>Distribution (Box plot) of log fold change </h3></font>', file = htmloutfile, append = TRUE);
     cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n',
-    "<tr><td align=center>", '<img src="Box_TE.png" width=500 height=500></td>\n', file = htmloutfile, append = TRUE);
-    multisample_boxplot(TE_df, sampleinfo_df, outplot, "Yes", "Sample (log2(case/control))", "Transcript Abundance fold-change (log2)");
+        "<tr><td align=center>", '<img src="Box_TE.png" width=500 height=500>', lines$widget_div, '</td>\n', file = htmloutfile, append = TRUE);
     
     # PE Boxplot
     outplot = paste(outdir,"/Box_PE.png",sep="",collape="");
-    cat("<td align=center>", '<img src="Box_PE.png" width=500 height=500></td></tr></table>\n', file = htmloutfile, append = TRUE);
     multisample_boxplot(PE_df, sampleinfo_df, outplot, "Yes", "Sample (log2(case/control))", "Protein Abundance fold-change(log2)");
+    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></td>\n',
-    "<td align=center>", '<img src="PCA_PE_all_rep.png" width=500 height=500></td></tr></table>\n', 
-      file = htmloutfile, append = TRUE);
+        "<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);
+        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);
-    cat("<tr><td align=center>", '<img src="TE_PE_scatter.png" width=800 height=800></td>\n', file = htmloutfile, append = TRUE);
-    PE_TE_data = data.frame(PE_df, TE_df);
-    colnames(PE_TE_data) = c("PE_ID","PE_abundance","TE_ID","TE_abundance");
     singlesample_scatter(PE_TE_data, outplot);  
-
+    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);
+        file = htmloutfile, append = TRUE);
     cat('</td></table>',
-    file = htmloutfile, append = TRUE);
+        file = htmloutfile, append = TRUE);
     
     cat('<hr/><h2 id="regression_data"><font color=#ff0000>REGRESSION ANALYSIS</font></h2>\n',
-    file = htmloutfile, append = TRUE);
+        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);
+        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=""))$postscripts);
   }
 }
 cat("<h3>Go To:</h3>\n",
@@ -1002,3 +1219,14 @@
     "<br><a href=#>TOP</a>",
     file = htmloutfile, append = TRUE);
 cat("</body></html>\n", file = htmloutfile, append = TRUE);
+
+
+#===============================================================================
+# Add masked-javascripts tags to HTML file in the head and end
+#===============================================================================
+
+htmllines <- readLines(htmloutfile)
+htmllines[1] <- paste('<html>\n<head>\n', paste(prescripts, collapse='\n'), '\n</head>\n<body>')
+cat(paste(htmllines, collapse='\n'), file = htmloutfile)
+cat('\n', paste(postscripts, collapse='\n'), "\n",
+    "</body>\n</html>\n", file = htmloutfile, append = TRUE);
diff -r 75faf9a89f5b -r bcc7a4c4cc29 quantp.xml
--- a/quantp.xml	Fri Sep 14 12:22:31 2018 -0400
+++ b/quantp.xml	Thu Dec 20 16:06:05 2018 -0500
@@ -1,4 +1,4 @@
-<tool id="quantp" name="QuanTP" version="1.0.0">
+<tool id="quantp" name="QuanTP" version="1.1.0">
     <description>Correlation between protein and transcript abundances</description>
     <requirements>
         <requirement type="package" version="1.10.4">r-data.table</requirement>
@@ -6,6 +6,8 @@
         <requirement type="package" version="0.7.6">r-dplyr</requirement>
         <requirement type="package" version="3.0.0">r-ggplot2</requirement>
         <requirement type="package" version="0.4.5">r-ggfortify</requirement>
+        <requirement type="package" version="4.8.0">r-plotly</requirement>
+        <requirement type="package" version="0.6.1.2">r-d3heatmap</requirement>
     </requirements>
     <command detect_errors="exit_code"><![CDATA[
 Rscript '$__tool_directory__/quantp.r' 
@@ -108,6 +110,7 @@
             <output name="html_file">
                 <assert_contents>
                     <has_text text="SAMPLE DISTRIBUTION" />
+                    <has_text text="plotly" />
                 </assert_contents>
             </output>
         </test>