Mercurial > repos > galaxyp > quantp
changeset 0:75faf9a89f5b draft
planemo upload commit a0e968c7bd2b6f7b963baeecb08f3a39e50f52d6
| author | galaxyp |
|---|---|
| date | Fri, 14 Sep 2018 12:22:31 -0400 |
| parents | |
| children | bcc7a4c4cc29 |
| files | quantp.r quantp.xml test-data/exp_design_file.tabular test-data/output.html test-data/protein_data.tabular test-data/transcript_data.tabular |
| diffstat | 6 files changed, 6996 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/quantp.r Fri Sep 14 12:22:31 2018 -0400 @@ -0,0 +1,1004 @@ +#*************************************************************************************************************************************** +# Functions: Start +#*************************************************************************************************************************************** + +#=============================================================================== +# PCA +#=============================================================================== +multisample_PCA = function(df, sampleinfo_df, outfile) +{ + tempdf = df[,-1]; + tempcol = colnames(tempdf); + tempgrp = sampleinfo_df[tempcol,2]; + tempdf = t(tempdf) %>% as.data.frame(); + tempdf[is.na(tempdf)] = 0; + tempdf$Group = tempgrp; + png(outfile, width = 6, height = 6, units = 'in', res=300); + # bitmap(outfile, "png16m"); + g = autoplot(prcomp(select(tempdf, -Group)), data = tempdf, colour = 'Group', size=3); + plot(g); + dev.off(); +} + +#=============================================================================== +# Regression and Cook's distance +#=============================================================================== +singlesample_regression = function(PE_TE_data,htmloutfile, append=TRUE) +{ + rownames(PE_TE_data) = PE_TE_data$PE_ID; + regmodel = lm(PE_abundance~TE_abundance, data=PE_TE_data); + regmodel_summary = summary(regmodel); + + cat("<font><h3>Linear Regression model fit between Proteome and Transcriptome data</h3></font>\n", + "<p>Assuming a linear relationship between Proteome and Transcriptome data, we here fit a linear regression model.</p>\n", + '<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr>\n', + file = htmloutfile, append = TRUE); + + cat("<tr><td>Formula</td><td>","PE_abundance~TE_abundance","</td></tr>\n", + "<tr><td colspan='2' align='center'> <b>Coefficients</b></td>","</tr>\n", + "<tr><td>",names(regmodel$coefficients[1]),"</td><td>",regmodel$coefficients[1]," (Pvalue:", regmodel_summary$coefficients[1,4],")","</td></tr>\n", + "<tr><td>",names(regmodel$coefficients[2]),"</td><td>",regmodel$coefficients[2]," (Pvalue:", regmodel_summary$coefficients[2,4],")","</td></tr>\n", + "<tr><td colspan='2' align='center'> <b>Model parameters</b></td>","</tr>\n", + "<tr><td>Residual standard error</td><td>",regmodel_summary$sigma," (",regmodel_summary$df[2]," degree of freedom)</td></tr>\n", + "<tr><td>F-statistic</td><td>",regmodel_summary$fstatistic[1]," ( on ",regmodel_summary$fstatistic[2]," and ",regmodel_summary$fstatistic[3]," degree of freedom)</td></tr>\n", + "<tr><td>R-squared</td><td>",regmodel_summary$r.squared,"</td></tr>\n", + "<tr><td>Adjusted R-squared</td><td>",regmodel_summary$adj.r.squared,"</td></tr>\n", + file = htmloutfile, append = TRUE); + + cat("</table>\n", file = htmloutfile, append = TRUE); + + cat( + "<font color='#ff0000'><h3>Regression and diagnostics plots</h3></font>\n", + file = htmloutfile, append = TRUE); + + outplot = paste(outdir,"/PE_TE_lm_1.png",sep="",collapse=""); + png(outplot, width = 10, height = 10, units = 'in',res=300); + # bitmap(outplot, "png16m"); + par(mfrow=c(1,1)); + plot(regmodel, 1, cex.lab=1.5); + dev.off(); + + outplot = paste(outdir,"/PE_TE_lm_2.png",sep="",collapse=""); + png(outplot,width = 10, height = 10, units = 'in', res=300); + # bitmap(outplot, "png16m"); + par(mfrow=c(1,1)); + plot(regmodel, 2, cex.lab=1.5); + dev.off(); + + outplot = paste(outdir,"/PE_TE_lm_5.png",sep="",collapse=""); + png(outplot, width = 10, height = 10, units = 'in',res=300); + # bitmap(outplot, "png16m"); + par(mfrow=c(1,1)); + plot(regmodel, 5, cex.lab=1.5); + dev.off(); + + cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">', file = htmloutfile, append = TRUE); + + cat( + '<tr bgcolor="#7a0019"><th>', "<font color='#ffcc33'><h4>1) <u>Residuals vs Fitted plot</h4></font></u></th>\n", + '<th><font color=#ffcc33><h4>2) <u>Normal Q-Q plot of residuals</h4></font></u></th></tr>\n', + file = htmloutfile, append = TRUE); + + cat( + '<tr><td align=center><img src="PE_TE_lm_1.png" width=600 height=600></td><td align=center><img src="PE_TE_lm_2.png" width=600 height=600></td></tr>\n', + file = htmloutfile, append = TRUE); + + cat( + '<tr><td align=center>This plot checks for linear relationship assumptions.<br>If a horizontal line is observed without any distinct patterns, it indicates a linear relationship.</td>\n', + '<td align=center>This plot checks whether residuals are normally distributed or not.<br>It is good if the residuals points follow the straight dashed line i.e., do not deviate much from dashed line.</td></tr></table>\n', + file = htmloutfile, append = TRUE); + + + #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # Residuals data + #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + res_all = regmodel$residuals; + res_mean = mean(res_all); + res_sd = sd(res_all); + res_diff = (res_all-res_mean); + res_zscore = res_diff/res_sd; + # res_outliers = res_all[which((res_zscore > 2)|(res_zscore < -2))] + + + tempind = which((res_zscore > 2)|(res_zscore < -2)); + res_PE_TE_data_no_outlier = PE_TE_data[-tempind,]; + res_PE_TE_data_no_outlier$residuals = res_all[-tempind]; + res_PE_TE_data_outlier = PE_TE_data[tempind,]; + res_PE_TE_data_outlier$residuals = res_all[tempind]; + + # Save the complete table for download (influential_observations) + temp_outlier_data = data.frame(res_PE_TE_data_outlier$PE_ID, res_PE_TE_data_outlier$TE_abundance, res_PE_TE_data_outlier$PE_abundance, res_PE_TE_data_outlier$residuals) + colnames(temp_outlier_data) = c("Gene", "Transcript abundance", "Protein abundance", "Residual value") + outdatafile = paste(outdir,"/PE_TE_outliers_residuals.txt", sep="", collapse=""); + write.table(temp_outlier_data, file=outdatafile, row.names=F, sep="\t", quote=F); + + + # Save the complete table for download (non influential_observations) + temp_all_data = data.frame(PE_TE_data$PE_ID, PE_TE_data$TE_abundance, PE_TE_data$PE_abundance, res_all) + colnames(temp_all_data) = c("Gene", "Transcript abundance", "Protein abundance", "Residual value") + outdatafile = paste(outdir,"/PE_TE_abundance_residuals.txt", sep="", collapse=""); + write.table(temp_all_data, file=outdatafile, row.names=F, sep="\t", quote=F); + + + cat('<br><h2 id="inf_obs"><font color=#ff0000>Outliers based on the residuals from regression analysis</font></h2>\n', + file = htmloutfile, append = TRUE); + cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">\n', + '<tr bgcolor="#7a0019"><th colspan=2><font color=#ffcc33>Residuals from Regression</font></th></tr>\n', + '<tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr>\n', + file = htmloutfile, append = TRUE); + + cat("<tr><td>Mean Residual value</td><td>",res_mean,"</td></tr>\n", + "<tr><td>Standard deviation (Residuals)</td><td>",res_sd,"</td></tr>\n", + '<tr><td>Total outliers (Residual value > 2 standard deviation from the mean)</td><td>',length(tempind),' <font size=4>(<b><a href=PE_TE_outliers_residuals.txt target="_blank">Download these ',length(tempind),' data points with high residual values here</a></b>)</font></td>\n', + '<tr><td colspan=2 align=center><font size=4>(<b><a href=PE_TE_abundance_residuals.txt target="_blank">Download the complete residuals data here</a></b>)</font></td></td>\n', + "</table><br><br>\n", + file = htmloutfile, append = TRUE); + + #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + + cat('<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">', file = htmloutfile, append = TRUE); + + cat( + '<tr bgcolor="#7a0019"><th><font color=#ffcc33><h4>3) <u>Residuals vs Leverage plot</h4></font></u></th></tr>\n', + file = htmloutfile, append = TRUE); + + cat( + '<tr><td align=center><img src="PE_TE_lm_5.png" width=600 height=600></td></tr>\n', + file = htmloutfile, append = TRUE); + + cat( + '<tr><td align=center>This plot is useful to identify any influential cases, that is outliers or extreme values.<br>They might influence the regression results upon inclusion or exclusion from the analysis.</td></tr></table><br>\n', + file = htmloutfile, append = TRUE); + + + + #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + # Cook's Distance + #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + cat('<hr/><h2 id="inf_obs"><font color=#ff0000>INFLUENTIAL OBSERVATIONS</font></h2>\n', + file = htmloutfile, append = TRUE); + cat( + '<p><b>Cook\'s distance</b> computes the influence of each data point/observation on the predicted outcome. i.e. this measures how much the observation is influencing the fitted values.<br>In general use, those observations that have a <b>Cook\'s distance > than ', cookdist_upper_cutoff,' times the mean</b> may be classified as <b>influential.</b></p>\n', + file = htmloutfile, append = TRUE); + + cooksd <- cooks.distance(regmodel); + + outplot = paste(outdir,"/PE_TE_lm_cooksd.png",sep="",collapse=""); + png(outplot, width = 10, height = 10, units = 'in', res=300); + # bitmap(outplot, "png16m"); + par(mfrow=c(1,1)); + plot(cooksd, main="Influential Obs. by Cook\'s distance", ylab="Cook\'s distance", xlab="Observations", type="n") # plot cooks distance + sel_outlier=which(cooksd>=as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T)) + sel_nonoutlier=which(cooksd<as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T)) + points(sel_outlier, cooksd[sel_outlier],pch="*", cex=2, cex.lab=1.5, col="red") + points(sel_nonoutlier, cooksd[sel_nonoutlier],pch="*", cex=2, cex.lab=1.5, col="black") + abline(h = as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T), col="red") # add cutoff line + #text(x=1:length(cooksd)+1, y=cooksd, labels=ifelse(cooksd>as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T),names(cooksd),""), col="red", pos=2) # add labels + dev.off(); + + cat( + '<img src="PE_TE_lm_cooksd.png" width=800 height=800>', + '<br>In the above plot, observations above red line (',cookdist_upper_cutoff,' * mean Cook\'s distance) are influential. Genes that are outliers could be important. These observations influences the correlation values and regression coefficients<br><br>', + file = htmloutfile, append = TRUE); + + tempind = which(cooksd>as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T)); + PE_TE_data_no_outlier = PE_TE_data[-tempind,]; + PE_TE_data_no_outlier$cooksd = cooksd[-tempind]; + PE_TE_data_outlier = PE_TE_data[tempind,]; + PE_TE_data_outlier$cooksd = cooksd[tempind]; + a = sort(PE_TE_data_outlier$cooksd, decreasing=T, index.return=T); + PE_TE_data_outlier_sorted = PE_TE_data_outlier[a$ix,]; + + cat( + '<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr>\n', + file = htmloutfile, append = TRUE); + + # Save the complete table for download (influential_observations) + temp_outlier_data = data.frame(PE_TE_data_outlier$PE_ID, PE_TE_data_outlier$TE_abundance, PE_TE_data_outlier$PE_abundance, PE_TE_data_outlier$cooksd) + colnames(temp_outlier_data) = c("Gene", "Transcript abundance", "Protein abundance", "Cook's distance") + outdatafile = paste(outdir,"/PE_TE_influential_observation.txt", sep="", collapse=""); + write.table(temp_outlier_data, file=outdatafile, row.names=F, sep="\t", quote=F); + + + # Save the complete table for download (non influential_observations) + temp_no_outlier_data = data.frame(PE_TE_data_no_outlier$PE_ID, PE_TE_data_no_outlier$TE_abundance, PE_TE_data_no_outlier$PE_abundance, PE_TE_data_no_outlier$cooksd) + colnames(temp_no_outlier_data) = c("Gene", "Transcript abundance", "Protein abundance", "Cook's distance") + outdatafile = paste(outdir,"/PE_TE_non_influential_observation.txt", sep="", collapse=""); + write.table(temp_no_outlier_data, file=outdatafile, row.names=F, sep="\t", quote=F); + + + cat("<tr><td>Mean Cook\'s distance</td><td>",mean(cooksd, na.rm=T),"</td></tr>\n", + "<tr><td>Total influential observations (Cook\'s distance > ",cookdist_upper_cutoff," * mean Cook\'s distance)</td><td>",length(tempind),"</td>\n", + + "<tr><td>Observations with Cook\'s distance < ",cookdist_upper_cutoff," * mean Cook\'s distance</td><td>",length(which(cooksd<as.numeric(cookdist_upper_cutoff)*mean(cooksd, na.rm=T))),"</td>\n", + "</table><br><br>\n", + file = htmloutfile, append = TRUE); + + + #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + # Scatter plot after removal of influential points + #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + outplot = paste(outdir,"/AbundancePlot_scatter_without_outliers.png",sep="",collapse=""); + min_lim = min(c(PE_TE_data$PE_abundance,PE_TE_data$TE_abundance)); + max_lim = max(c(PE_TE_data$PE_abundance,PE_TE_data$TE_abundance)); + png(outplot, width = 10, height = 10, units = 'in', res=300); + # bitmap(outplot,"png16m"); + g = ggplot(PE_TE_data_no_outlier, aes(x=TE_abundance, y=PE_abundance))+geom_point() + geom_smooth() + xlab("Transcript abundance log fold-change") + ylab("Protein abundance log fold-change") + xlim(min_lim,max_lim) + ylim(min_lim,max_lim); + suppressMessages(plot(g)); + dev.off(); + + cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatterplot: Before removal</font></th><th><font color=#ffcc33>Scatterplot: After removal</font></th></tr>\n', file = htmloutfile, append = TRUE); + # Before + cat("<tr><td align=center><!--<font color='#ff0000'><h3>Scatter plot between Proteome and Transcriptome Abundance</h3></font>\n-->", '<img src="TE_PE_scatter.png" width=600 height=600></td>\n', file = htmloutfile, append = TRUE); + + # After + cat("<td align=center>\n", + '<img src="AbundancePlot_scatter_without_outliers.png" width=600 height=600></td></tr>\n', + file = htmloutfile, append = TRUE); + #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ + + + cor_result_pearson = cor.test(PE_TE_data_no_outlier[,"TE_abundance"], PE_TE_data_no_outlier[,"PE_abundance"], method = "pearson"); + cor_result_spearman = cor.test(PE_TE_data_no_outlier[,"TE_abundance"], PE_TE_data_no_outlier[,"PE_abundance"], method = "spearman"); + cor_result_kendall = cor.test(PE_TE_data_no_outlier[,"TE_abundance"], PE_TE_data_no_outlier[,"PE_abundance"], method = "kendall"); + + cat('<tr><td>\n', file = htmloutfile, append=TRUE); + singlesample_cor(PE_TE_data, htmloutfile, append=TRUE); + cat('</td>\n', file = htmloutfile, append=TRUE); + + + cat('<td><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Method 1</font></th><th><font color=#ffcc33>Method 2</font></th><th><font color=#ffcc33>Method 3</font></th></tr>\n', + file = htmloutfile, append = TRUE); + + cat( + "<tr><td>Correlation method</td><td>",cor_result_pearson$method,"</td><td>",cor_result_spearman$method,"</td><td>",cor_result_kendall$method,"</td></tr>\n", + "<tr><td>Correlation coefficient</td><td>",cor_result_pearson$estimate,"</td><td>",cor_result_spearman$estimate,"</td><td>",cor_result_kendall$estimate,"</td></tr>\n", + file = htmloutfile, append = TRUE) + cat("</table></td></tr></table>\n", file = htmloutfile, append = TRUE) + + + + if(dim(PE_TE_data_outlier)[1]<10) + { + tab_n_row = dim(PE_TE_data_outlier)[1]; + }else{ + tab_n_row = 10; + } + + cat("<br><br><font size=5><b><a href='PE_TE_influential_observation.txt' target='_blank'>Download the complete list of influential observations</a></b></font> ", + "<font size=5><b><a href='PE_TE_non_influential_observation.txt' target='_blank'>Download the complete list (After removing influential points)</a></b></font><br>\n", + '<br><font color="brown"><h4>Top ',as.character(tab_n_row),' Influential observations (Cook\'s distance > ',cookdist_upper_cutoff,' * mean Cook\'s distance)</h4></font>\n', + file = htmloutfile, append = TRUE); + + cat('<table border=1 cellspacing=0 cellpadding=5> <tr bgcolor="#7a0019">\n', sep = "",file = htmloutfile, append = TRUE); + cat("<th><font color=#ffcc33>Gene</font></th><th><font color=#ffcc33>Protein Log Fold-Change</font></th><th><font color=#ffcc33>Transcript Log Fold-Change</font></th><th><font color=#ffcc33>Cook's Distance</font></th></tr>\n", + file = htmloutfile, append = TRUE); + + + for(i in 1:tab_n_row) + { + cat( + '<tr>','<td>',as.character(PE_TE_data_outlier_sorted[i,1]),'</td>\n', + '<td>',format(PE_TE_data_outlier_sorted[i,2], scientific=F),'</td>\n', + '<td>',PE_TE_data_outlier_sorted[i,4],'</td>\n', + '<td>',format(PE_TE_data_outlier_sorted[i,5], scientific=F),'</td></tr>\n', + file = htmloutfile, append = TRUE); + } + cat('</table><br><br>\n',file = htmloutfile, append = TRUE); + + +} + + + +#=============================================================================== +# Heatmap +#=============================================================================== +singlesample_heatmap=function(PE_TE_data, htmloutfile, hm_nclust){ + cat('<br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Heatmap of PE and TE abundance values (Hierarchical clustering)</font></th><th><font color=#ffcc33>Number of clusters to extract: ',hm_nclust,'</font></th></tr>\n', + file = htmloutfile, append = TRUE); + + hc=hclust(dist(as.matrix(PE_TE_data[,c("PE_abundance","TE_abundance")]))) + hm_cluster = cutree(hc,k=hm_nclust); + + outplot = paste(outdir,"/PE_TE_heatmap.png",sep="",collapse=""); + png(outplot, width = 10, height = 10, units = 'in', res=300); + # bitmap(outplot, "png16m"); + par(mfrow=c(1,1)); + hmap = heatmap.2(as.matrix(PE_TE_data[,c("PE_abundance","TE_abundance")]), trace="none", cexCol=1, col=greenred(100),Colv=F, labCol=c("Proteins","Transcripts"), scale="col", hclustfun = hclust, distfun = dist); + dev.off(); + + cat('<tr><td align=center colspan="2"><img src="PE_TE_heatmap.png" width=800 height=800></td></tr>\n', + file = htmloutfile, append = TRUE); + + + temp_PE_TE_data = data.frame(PE_TE_data$PE_ID, PE_TE_data$TE_abundance, PE_TE_data$PE_abundance, hm_cluster); + colnames(temp_PE_TE_data) = c("Gene", "Transcript abundance", "Protein abundance", "Cluster (Hierarchical clustering)") + tempoutfile = paste(outdir,"/PE_TE_hc_clusterpoints.txt",sep="",collapse=""); + write.table(temp_PE_TE_data, file=tempoutfile, row.names=F, quote=F, sep="\t", eol="\n") + + + cat('<tr><td colspan="2" align=center><font size=5><a href="PE_TE_hc_clusterpoints.txt" target="_blank"><b>Download the hierarchical cluster list</b></a></font></td></tr></table>\n', + file = htmloutfile, append = TRUE); +} + + +#=============================================================================== +# K-means clustering +#=============================================================================== +singlesample_kmeans=function(PE_TE_data, htmloutfile, nclust){ + PE_TE_data_kdata = PE_TE_data; + k1 = kmeans(PE_TE_data_kdata[,c("PE_abundance","TE_abundance")], nclust); + outplot = paste(outdir,"/PE_TE_kmeans.png",sep="",collapse=""); + png(outplot, width = 10, height = 10, units = 'in', res=300); + # bitmap(outplot, "png16m"); + par(mfrow=c(1,1)); + scatter.smooth(PE_TE_data_kdata[,"TE_abundance"], PE_TE_data_kdata[,"PE_abundance"], xlab="Transcript Abundance", ylab="Protein Abundance", cex.lab=1.5); + legend(1, 95, legend=c("Cluster 1", "Line 2"), col="red", lty=1:1, cex=0.8) + legend(1, 95, legend="Cluster 2", col="green", lty=1:1, cex=0.8) + + + ind=which(k1$cluster==1); + points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="red", pch=16); + ind=which(k1$cluster==2); + points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="green", pch=16); + ind=which(k1$cluster==3); + points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="blue", pch=16); + ind=which(k1$cluster==4); + points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="cyan", pch=16); + ind=which(k1$cluster==5); + points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="black", pch=16); + ind=which(k1$cluster==6); + points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="brown", pch=16); + ind=which(k1$cluster==7); + points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="gold", pch=16); + ind=which(k1$cluster==8); + points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="thistle", pch=16); + ind=which(k1$cluster==9); + points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="yellow", pch=16); + ind=which(k1$cluster==10); + points(PE_TE_data_kdata[ind,"TE_abundance"], PE_TE_data_kdata[ind,"PE_abundance"], col="orange", pch=16); + dev.off(); + + cat('<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>K-mean clustering</font></th><th><font color=#ffcc33>Number of clusters: ',nclust,'</font></th></tr>\n', + file = htmloutfile, append = TRUE); + + tempind = order(k1$cluster); + tempoutfile = paste(outdir,"/PE_TE_kmeans_clusterpoints.txt",sep="",collapse=""); + write.table(data.frame(PE_TE_data_kdata[tempind, ], Cluster=k1$cluster[tempind]), file=tempoutfile, row.names=F, quote=F, sep="\t", eol="\n") + + + cat('<tr><td colspan="2" align=center><img src="PE_TE_kmeans.png" width=800 height=800></td></tr>\n', + file = htmloutfile, append = TRUE); + cat('<tr><td colspan="2" align=center><font size=5><a href="PE_TE_kmeans_clusterpoints.txt" target="_blank"><b>Download the cluster list</b></a></font></td></tr></table><br><hr/>\n', + file = htmloutfile, append = TRUE); + +} + +#=============================================================================== +# scatter plot +#=============================================================================== +singlesample_scatter = function(PE_TE_data, outfile) +{ + min_lim = min(c(PE_TE_data$PE_abundance,PE_TE_data$TE_abundance)); + max_lim = max(c(PE_TE_data$PE_abundance,PE_TE_data$TE_abundance)); + png(outfile, width = 10, height = 10, units = 'in', res=300); + # bitmap(outfile, "png16m"); + g = ggplot(PE_TE_data, aes(x=TE_abundance, y=PE_abundance))+geom_point() + geom_smooth() + xlab("Transcript abundance log fold-change") + ylab("Protein abundance log fold-change") + xlim(min_lim,max_lim) + ylim(min_lim,max_lim); + suppressMessages(plot(g)); + # plot(g); + dev.off(); +} + +#=============================================================================== +# Correlation table +#=============================================================================== +singlesample_cor = function(PE_TE_data, htmloutfile, append=TRUE) +{ + cor_result_pearson = cor.test(PE_TE_data$TE_abundance, PE_TE_data$PE_abundance, method = "pearson"); + cor_result_spearman = cor.test(PE_TE_data$TE_abundance, PE_TE_data$PE_abundance, method = "spearman"); + cor_result_kendall = cor.test(PE_TE_data$TE_abundance, PE_TE_data$PE_abundance, method = "kendall"); + + cat( + '<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Method 1</font></th><th><font color=#ffcc33>Method 2</font></th><th><font color=#ffcc33>Method 3</font></th></tr>\n', + file = htmloutfile, append = TRUE); + + cat( + "<tr><td>Correlation method</td><td>",cor_result_pearson$method,"</td><td>",cor_result_spearman$method,"</td><td>",cor_result_kendall$method,"</td></tr>\n", + "<tr><td>Correlation coefficient</td><td>",cor_result_pearson$estimate,"</td><td>",cor_result_spearman$estimate,"</td><td>",cor_result_kendall$estimate,"</td></tr>\n", + file = htmloutfile, append = TRUE) + cat("</table>\n", file = htmloutfile, append = TRUE); + +} + +#=============================================================================== +# Boxplot +#=============================================================================== +multisample_boxplot = function(df, sampleinfo_df, outfile, fill_leg, user_xlab, user_ylab) +{ + tempdf = df[,-1, drop=FALSE]; + tempdf = t(tempdf) %>% as.data.frame(); + tempdf[is.na(tempdf)] = 0; + tempdf$Sample = rownames(tempdf); + tempdf1 = melt(tempdf, id.vars = "Sample"); + tempdf1$Group = sampleinfo_df[tempdf1$Sample,2]; + png(outplot, width = 6, height = 6, units = 'in', res=300); + # bitmap(outplot, "png16m"); + if(fill_leg=="Yes") + { + g = ggplot(tempdf1, aes(x=Sample, y=value, fill=Group)) + geom_boxplot() + labs(x=user_xlab) + labs(y=user_ylab) + }else{ + if(fill_leg=="No") + { + tempdf1$Group = c("case", "control") + g = ggplot(tempdf1, aes(x=Sample, y=value, fill=Group)) + geom_boxplot() + labs(x=user_xlab) + labs(y=user_ylab) + } + } + plot(g); + dev.off(); +} + + +#=============================================================================== +# Mean or Median of Replicates +#=============================================================================== + +mergeReplicates = function(TE_df,PE_df, sampleinfo_df, method) +{ +grps = unique(sampleinfo_df[,2]); + +TE_df_merged <<- sapply(grps, function(x){ + tempsample = sampleinfo_df[which(sampleinfo_df$Group==x),1] + if(length(tempsample)!=1){ + apply(TE_df[,tempsample],1,method); + }else{ + return(TE_df[,tempsample]); + } +}); +TE_df_merged <<- data.frame(as.character(TE_df[,1]), TE_df_merged); +colnames(TE_df_merged) = c(colnames(TE_df)[1], grps); + +PE_df_merged <<- sapply(grps, function(x){ + tempsample = sampleinfo_df[which(sampleinfo_df$Group==x),1] + if(length(tempsample)!=1){ + apply(PE_df[,tempsample],1,method); + }else{ + return(PE_df[,tempsample]); + } +}); + +PE_df_merged <<- data.frame(as.character(PE_df[,1]), PE_df_merged); +colnames(PE_df_merged) = c(colnames(PE_df)[1], grps); + +#sampleinfo_df_merged = data.frame(Sample = grps, Group = grps, stringsAsFactors = F); +sampleinfo_df_merged = data.frame(Sample = grps, Group = "Group", stringsAsFactors = F); + +return(list(TE_df_merged = TE_df_merged, PE_df_merged = PE_df_merged, sampleinfo_df_merged = sampleinfo_df_merged)); +} + +#=============================================================================== +# (T-Test or Wilcoxon ranksum test) and Volcano Plot +#=============================================================================== + +perform_Test_Volcano = function(TE_df_data,PE_df_data,TE_df_logfold, PE_df_logfold,sampleinfo_df, method, correction_method,volc_with) +{ + +PE_colnames = colnames(PE_df_data); +control_sample = sampleinfo_df[which(sampleinfo_df$Group=="control"),1]; +control_ind <<- sapply(control_sample, function(x){temp_ind = which(PE_colnames==x); as.numeric(temp_ind)}); +condition_sample = sampleinfo_df[which(sampleinfo_df$Group=="case"),1]; +condition_ind <<- sapply(condition_sample, function(x){temp_ind = which(PE_colnames==x); as.numeric(temp_ind)}); + +if(method=="mean"){ + #PE_pval = apply(PE_df_data[2:length(colnames(PE_df_data))],1,function(x) t.test(x[condition_ind-1], x[control_ind-1])$p.value); + PE_pval = apply(PE_df_data[2:length(colnames(PE_df_data))],1,function(x) {obj<-try(t.test(x[condition_ind-1], x[control_ind-1]),silent=TRUE); if(is(obj, "try-error")){return(NA)}else{return(obj$p.value)}}) +}else{ +if(method=="median"){ + PE_pval = apply(PE_df_data[2:length(colnames(PE_df_data))],1,function(x) {obj<-try(wilcox.test(x[condition_ind-1], x[control_ind-1]),silent=TRUE); if(is(obj, "try-error")){return(NA)}else{return(obj$p.value)}}) + # PE_pval = apply(PE_df_data[2:length(colnames(PE_df_data))],1,function(x) wilcox.test(x[condition_ind-1], x[control_ind-1])$p.value); + } +} +PE_adj_pval = p.adjust(PE_pval, method = correction_method, n = length(PE_pval)) + + +TE_colnames = colnames(TE_df_data); +control_sample = sampleinfo_df[which(sampleinfo_df$Group=="control"),1]; +control_ind <<- sapply(control_sample, function(x){temp_ind = which(TE_colnames==x); as.numeric(temp_ind)}); +condition_sample = sampleinfo_df[which(sampleinfo_df$Group=="case"),1]; +condition_ind <<- sapply(condition_sample, function(x){temp_ind = which(TE_colnames==x); as.numeric(temp_ind)}); + +if(method=="mean"){ + # TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) t.test(x[condition_ind-1], x[control_ind-1])$p.value); + TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) {obj<-try(t.test(x[condition_ind-1], x[control_ind-1]),silent=TRUE); if(is(obj, "try-error")){return(NA)}else{return(obj$p.value)}}) +}else{ + if(method=="median"){ + TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) {obj<-try(wilcox.test(x[condition_ind-1], x[control_ind-1]),silent=TRUE); if(is(obj, "try-error")){return(NA)}else{return(obj$p.value)}}) + # TE_pval = apply(TE_df_data[2:length(colnames(TE_df_data))],1,function(x) wilcox.test(x[condition_ind-1], x[control_ind-1])$p.value); + } +} +TE_adj_pval = p.adjust(TE_pval, method = correction_method, n = length(TE_pval)) + + +PE_TE_logfold_pval = data.frame(TE_df_logfold$Gene, TE_df_logfold$LogFold, TE_pval, TE_adj_pval, PE_df_logfold$LogFold, PE_pval, PE_adj_pval); +colnames(PE_TE_logfold_pval) = c("Gene", "Transcript log fold-change", "p-value (transcript)", "adj p-value (transcript)", "Protein log fold-change", "p-value (protein)", "adj p-value (protein)"); +outdatafile = paste(outdir,"/PE_TE_logfold_pval.txt", sep="", collapse=""); +write.table(PE_TE_logfold_pval, file=outdatafile, row.names=F, sep="\t", quote=F); +cat("<br><br><font size=5><b><a href='PE_TE_logfold_pval.txt' target='_blank'>Download the complete fold change data here</a></b></font><br>\n", + file = htmloutfile, append = TRUE); + + if(length(condition_ind)!=1) + { + # Volcano Plot + + if(volc_with=="adj_pval") + { + PE_pval = PE_adj_pval + TE_pval = TE_adj_pval + volc_ylab = "-log10 Adjusted p-value"; + }else{ + if(volc_with=="pval") + { + volc_ylab = "-log10 p-value"; + } + } + outplot = paste(outdir,"/PE_volcano.png",sep="",collapse=""); + png(outplot, width = 10, height = 10, units = 'in', res=300); + # bitmap(outplot, "png16m"); + par(mfrow=c(1,1)); + + plot(PE_df_logfold$LogFold, -log10(PE_pval), + xlab="log2 fold change", ylab=volc_ylab, + type="n") + sel <- which((PE_df_logfold$LogFold<=log(2,base=2))&(PE_df_logfold$LogFold>=log(0.5, base=2))) # or whatever you want to use + points(PE_df_logfold[sel,"LogFold"], -log10(PE_pval[sel]),col="black") + #sel <- which((PE_df_logfold$LogFold>log(2,base=2))&(PE_df_logfold$LogFold<log(0.5,base=2))) # or whatever you want to use + sel <- which((PE_df_logfold$LogFold>log(2,base=2))|(PE_df_logfold$LogFold<log(0.5, base=2))) + sel1 <- which(PE_pval<=0.05) + sel=intersect(sel,sel1) + points(PE_df_logfold[sel,"LogFold"], -log10(PE_pval[sel]),col="red") + sel <- which((PE_df_logfold$LogFold>log(2,base=2))|(PE_df_logfold$LogFold<log(0.5, base=2))) + sel1 <- which(PE_pval>0.05) + sel=intersect(sel,sel1) + points(PE_df_logfold[sel,"LogFold"], -log10(PE_pval[sel]),col="blue") + abline(h = -log(0.05,base=10), col="red", lty=2) + abline(v = log(2,base=2), col="red", lty=2) + abline(v = log(0.5,base=2), col="red", lty=2) + dev.off(); + + + + outplot = paste(outdir,"/TE_volcano.png",sep="",collapse=""); + png(outplot, width = 10, height = 10, units = 'in', res=300); + # bitmap(outplot, "png16m"); + par(mfrow=c(1,1)); + + plot(TE_df_logfold$LogFold, -log10(TE_pval), + xlab="log2 fold change", ylab=volc_ylab, + type="n") + sel <- which((TE_df_logfold$LogFold<=log(2,base=2))&(TE_df_logfold$LogFold>=log(0.5, base=2))) # or whatever you want to use + points(TE_df_logfold[sel,"LogFold"], -log10(TE_pval[sel]),col="black") + #sel <- which((TE_df_logfold$LogFold>log(2,base=2))&(TE_df_logfold$LogFold<log(0.5,base=2))) # or whatever you want to use + sel <- which((TE_df_logfold$LogFold>log(2,base=2))|(TE_df_logfold$LogFold<log(0.5, base=2))) + sel1 <- which(TE_pval<=0.05) + sel=intersect(sel,sel1) + points(TE_df_logfold[sel,"LogFold"], -log10(TE_pval[sel]),col="red") + sel <- which((TE_df_logfold$LogFold>log(2,base=2))|(TE_df_logfold$LogFold<log(0.5, base=2))) + sel1 <- which(TE_pval>0.05) + sel=intersect(sel,sel1) + points(TE_df_logfold[sel,"LogFold"], -log10(TE_pval[sel]),col="blue") + abline(h = -log(0.05,base=10), col="red", lty=2) + abline(v = log(2,base=2), col="red", lty=2) + abline(v = log(0.5,base=2), col="red", lty=2) + dev.off(); + + + + cat('<br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Transcript Fold-Change</font></th><th><font color=#ffcc33>Protein Fold-Change</font></th></tr>\n', file = htmloutfile, append = TRUE); + cat("<tr><td align=center>", '<img src="TE_volcano.png" width=600 height=600></td>\n', file = htmloutfile, append = TRUE); + cat("<td align=center>", + '<img src="PE_volcano.png" width=600 height=600></td></tr></table><br>\n', + file = htmloutfile, append = TRUE); + }else{ + cat('<br><br><b><font color=red>!!! No replicates found. Cannot perform test to check significance of differential expression. Thus, no Volcano plot generated !!!</font></b><br><br>', + file = htmloutfile, append = TRUE); + } + +} + + +#*************************************************************************************************************************************** +# Functions: End +#*************************************************************************************************************************************** + + + + +#=============================================================================== +# Arguments +#=============================================================================== +noargs = 12; +args = commandArgs(trailingOnly = TRUE); +if(length(args) != noargs) +{ + stop(paste("Please check usage. Number of arguments is not equal to ",noargs,sep="",collapse="")); +} + +mode = args[1]; # "multiple" or "logfold" +method = args[2]; # "mean" or "median" +sampleinfo_file = args[3]; +proteome_file = args[4]; +transcriptome_file = args[5]; +correction_method = args[6]; +cookdist_upper_cutoff = args[7]; +numCluster = args[8]; +hm_nclust = args[9]; +volc_with = args[10]; + +htmloutfile = args[11]; # html output file +outdir = args[12]; # html supporting files + + +#=============================================================================== +# Check for file existance +#=============================================================================== +if(! file.exists(proteome_file)) +{ + stop(paste("Proteome Data file does not exists. Path given: ",proteome_file,sep="",collapse="")); +} +if(! file.exists(transcriptome_file)) +{ + stop(paste("Transcriptome Data file does not exists. Path given: ",transcriptome_file,sep="",collapse="")); +} + +#=============================================================================== +# Load library +#=============================================================================== +options(warn=-1); + +suppressPackageStartupMessages(library(dplyr)); +suppressPackageStartupMessages(library(data.table)); +suppressPackageStartupMessages(library(gplots)); +suppressPackageStartupMessages(library(ggplot2)); +suppressPackageStartupMessages(library(ggfortify)); + +#=============================================================================== +# Select mode and parse experiment design file +#=============================================================================== +if(mode=="multiple") +{ + expDesign = fread(sampleinfo_file, header = FALSE, stringsAsFactors = FALSE, sep="\t") %>% data.frame(); + expDesign_cc = expDesign[1:2,]; + + sampleinfo_df = expDesign[3:nrow(expDesign),]; + rownames(sampleinfo_df)=1:nrow(sampleinfo_df); + colnames(sampleinfo_df) = c("Sample","Group"); + + condition_cols = sampleinfo_df[which(sampleinfo_df[,2]==expDesign_cc[which(expDesign_cc[,1]=="case"),2]),1]; + condition_g_name = "case"; + control_cols = sampleinfo_df[which(sampleinfo_df[,2]==expDesign_cc[which(expDesign_cc[,1]=="control"),2]),1]; + control_g_name = "control"; + sampleinfo_df[which(sampleinfo_df[,2]==expDesign_cc[which(expDesign_cc[,1]=="case"),2]),2] = "case"; + sampleinfo_df[which(sampleinfo_df[,2]==expDesign_cc[which(expDesign_cc[,1]=="control"),2]),2] = "control"; + sampleinfo_df_orig = sampleinfo_df; +} + +if(mode=="logfold") +{ + sampleinfo_df = data.frame("Sample"= c("LogFold"), "Group"=c("Fold_Change")) +} + +#=============================================================================== +# Parse Transcriptome data +#=============================================================================== +TE_df_orig = fread(transcriptome_file, sep="\t", stringsAsFactor=F, header=T) %>% data.frame(); +if(mode=="multiple") +{ + TE_df = TE_df_orig[,c(colnames(TE_df_orig)[1],condition_cols,control_cols)]; +} +if(mode=="logfold") +{ + TE_df = TE_df_orig; + colnames(TE_df) = c("Genes", "LogFold"); +} +#=============================================================================== +# Parse Proteome data +#=============================================================================== +PE_df_orig = fread(proteome_file, sep="\t", stringsAsFactor=F, header=T) %>% data.frame(); +if(mode=="multiple") +{ + PE_df = PE_df_orig[,c(colnames(PE_df_orig)[1],condition_cols,control_cols)]; +} +if(mode=="logfold") +{ + PE_df = PE_df_orig; + colnames(PE_df) = c("Genes", "LogFold"); +} + +#============================================================================================================= +# Create directory structures and then set the working directory to output directory +#============================================================================================================= +if(! file.exists(outdir)) +{ + dir.create(outdir); +} +#=============================================================================== +# Write initial data summary in html outfile +#=============================================================================== + cat("<html><head></head><body>\n", file = htmloutfile); + + cat("<h1><u>QuanTP: Association between abundance ratios of transcript and protein</u></h1><hr/>\n", + "<font><h3>Input data summary</h3></font>\n", + "<ul>\n", + "<li>Abbreviations used: PE (Proteome data) and TE (Transcriptome data)","</li><br>\n", + "<li>Input Proteome data dimension (Row Column): ", dim(PE_df)[1]," x ", dim(PE_df)[2],"</li>\n", + "<li>Input Transcriptome data dimension (Row Column): ", dim(TE_df)[1]," x ", dim(TE_df)[2],"</li></ul><hr/>\n", + file = htmloutfile, append = TRUE); + + cat("<h3 id=table_of_content>Table of Contents:</h3>\n", + "<ul>\n", + "<li><a href=#sample_dist>Sample distribution</a></li>\n", + "<li><a href=#corr_data>Correlation</a></li>\n", + "<li><a href=#regression_data>Regression analysis</a></li>\n", + "<li><a href=#inf_obs>Influential observations</a></li>\n", + "<li><a href=#cluster_data>Cluster analysis</a></li></ul><hr/>\n", + file = htmloutfile, append = TRUE); +#=============================================================================== +# Find common samples +#=============================================================================== +common_samples = intersect(sampleinfo_df[,1], colnames(TE_df)[-1]) %>% intersect(., colnames(PE_df)[-1]); + +if(length(common_samples)==0) +{ + stop("No common samples found "); + cat("<b>Please check your experiment design file. Sample names (column names) in the Transcriptome and the Proteome data do not match. </b>\n",file = htmloutfile, append = TRUE); +} + +#=============================================================================== +# Create subsets based on common samples +#=============================================================================== +TE_df = select(TE_df, 1, common_samples); +PE_df = select(PE_df, 1, common_samples); +sampleinfo_df = filter(sampleinfo_df, Sample %in% common_samples); +rownames(sampleinfo_df) = sampleinfo_df[,1]; + +#=============================================================================== +# Check for number of rows similarity +#=============================================================================== +if(nrow(TE_df) != nrow(PE_df)) +{ + stop("Number of rows in Transcriptome and Proteome data are not same i.e. they are not paired"); + cat("<b>The correlation analysis expects paired TE and PE data i.e. (i)th gene/transcript of TE file should correspond to (i)th protein of PE file. In the current input provided there is mismatch in terms of number of rows of TE and PE file. Please make sure you provide paired data.</b>\n",file = htmloutfile, append = TRUE); +} + +#=============================================================================== +# Number of groups +#=============================================================================== +ngrps = unique(sampleinfo_df[,2]) %>% length(); +grps = unique(sampleinfo_df[,2]); +names(grps) = grps; + +#=============================================================================== +# Change column1 name +#=============================================================================== +colnames(TE_df)[1] = "Gene"; +colnames(PE_df)[1] = "Protein"; + +#=============================================================================== +# Treat missing values +#=============================================================================== +TE_nacount = sum(is.na(TE_df)); +PE_nacount = sum(is.na(PE_df)); + +TE_df[is.na(TE_df)] = 0; +PE_df[is.na(PE_df)] = 0; + + +#=============================================================================== +# Decide based on analysis mode +#=============================================================================== +if(mode=="logfold") +{ + cat('<h2 id="sample_dist"><font color=#ff0000>SAMPLE DISTRIBUTION</font></h2>\n', + file = htmloutfile, append = TRUE); + + # TE Boxplot + outplot = paste(outdir,"/Box_TE.png",sep="",collape=""); + cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">\n', + '<tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n', + "<tr><td align=center>", '<img src="Box_TE.png" width=500 height=500></td>\n', file = htmloutfile, append = TRUE); + multisample_boxplot(TE_df, sampleinfo_df, outplot, "Yes", "Samples", "Transcript Abundance data"); + + # PE Boxplot + outplot = paste(outdir,"/Box_PE.png",sep="",collape=""); + cat("<td align=center>", '<img src="Box_PE.png" width=500 height=500></td></tr></table>\n', file = htmloutfile, append = TRUE); + multisample_boxplot(PE_df, sampleinfo_df, outplot, "Yes", "Samples", "Protein Abundance data"); + + cat('<hr/><h2 id="corr_data"><font color=#ff0000>CORRELATION</font></h2>\n', + file = htmloutfile, append = TRUE); + + # TE PE scatter + outplot = paste(outdir,"/TE_PE_scatter.png",sep="",collape=""); + cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatter plot between Proteome and Transcriptome Abundance</font></th></tr>\n', file = htmloutfile, append = TRUE); + cat("<tr><td align=center>", '<img src="TE_PE_scatter.png" width=800 height=800></td></tr>\n', file = htmloutfile, append = TRUE); + PE_TE_data = data.frame(PE_df, TE_df); + colnames(PE_TE_data) = c("PE_ID","PE_abundance","TE_ID","TE_abundance"); + singlesample_scatter(PE_TE_data, outplot); + + # TE PE Cor + cat("<tr><td align=center>", file = htmloutfile, append = TRUE); + singlesample_cor(PE_TE_data, htmloutfile, append=TRUE); + cat('<font color="red">*Note that <u>correlation</u> is <u>sensitive to outliers</u> in the data. So it is important to analyze outliers/influential observations in the data.<br> Below we use <u>Cook\'s distance based approach</u> to identify such influential observations.</font>\n', + file = htmloutfile, append = TRUE); + cat('</td></table>', + file = htmloutfile, append = TRUE); + + cat('<hr/><h2 id="regression_data"><font color=#ff0000>REGRESSION ANALYSIS</font></h2>\n', + file = htmloutfile, append = TRUE); + + # TE PE Regression + singlesample_regression(PE_TE_data,htmloutfile, append=TRUE); + + cat('<hr/><h2 id="cluster_data"><font color=#ff0000>CLUSTER ANALYSIS</font></h2>\n', + file = htmloutfile, append = TRUE); + + # TE PE Heatmap + singlesample_heatmap(PE_TE_data, htmloutfile, hm_nclust); + + + # TE PE Clustering (kmeans) + singlesample_kmeans(PE_TE_data, htmloutfile, nclust=as.numeric(numCluster)) + +}else{ + if(mode=="multiple") + { + cat('<h2 id="sample_dist"><font color=#ff0000>SAMPLE DISTRIBUTION</font></h2>\n', + file = htmloutfile, append = TRUE); + + # TE Boxplot + outplot = paste(outdir,"/Box_TE_all_rep.png",sep="",collape=""); + cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; ">\n', + '<tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n', + "<tr><td align=center>", '<img src="Box_TE_all_rep.png" width=500 height=500></td>\n', file = htmloutfile, append = TRUE); + temp_df_te_data = data.frame(TE_df[,1], log(TE_df[,2:length(TE_df)])); + colnames(temp_df_te_data) = colnames(TE_df); + multisample_boxplot(temp_df_te_data, sampleinfo_df, outplot, "Yes", "Samples", "Transcript Abundance (log)"); + + # PE Boxplot + outplot = paste(outdir,"/Box_PE_all_rep.png",sep="",collape=""); + cat("<td align=center>", '<img src="Box_PE_all_rep.png" width=500 height=500></td></tr></table>\n', file = htmloutfile, append = TRUE); + temp_df_pe_data = data.frame(PE_df[,1], log(PE_df[,2:length(PE_df)])); + colnames(temp_df_pe_data) = colnames(PE_df); + multisample_boxplot(temp_df_pe_data, sampleinfo_df, outplot, "Yes", "Samples", "Protein Abundance (log)"); + + # Calc TE PCA + outplot = paste(outdir,"/PCA_TE_all_rep.png",sep="",collape=""); + multisample_PCA(TE_df, sampleinfo_df, outplot); + + # Calc PE PCA + outplot = paste(outdir,"/PCA_PE_all_rep.png",sep="",collape=""); + multisample_PCA(PE_df, sampleinfo_df, outplot); + + + # Replicate mode + templist = mergeReplicates(TE_df,PE_df, sampleinfo_df, method); + TE_df = templist$TE_df_merged; + PE_df = templist$PE_df_merged; + sampleinfo_df = templist$sampleinfo_df_merged; + rownames(sampleinfo_df) = sampleinfo_df[,1]; + + # TE Boxplot + outplot = paste(outdir,"/Box_TE_rep.png",sep="",collape=""); + cat('<br><font color="#ff0000"><h3>Sample wise distribution (Box plot) after using ',method,' on replicates </h3></font><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n', + "<tr><td align=center>", '<img src="Box_TE_rep.png" width=500 height=500></td>\n', file = htmloutfile, append = TRUE); + temp_df_te_data = data.frame(TE_df[,1], log(TE_df[,2:length(TE_df)])); + colnames(temp_df_te_data) = colnames(TE_df); + multisample_boxplot(temp_df_te_data, sampleinfo_df, outplot, "No", "Sample Groups", "Mean Transcript Abundance (log)"); + + # PE Boxplot + outplot = paste(outdir,"/Box_PE_rep.png",sep="",collape=""); + cat("<td align=center>", '<img src="Box_PE_rep.png" width=500 height=500></td></tr></table>\n', file = htmloutfile, append = TRUE); + temp_df_pe_data = data.frame(PE_df[,1], log(PE_df[,2:length(PE_df)])); + colnames(temp_df_pe_data) = colnames(PE_df); + multisample_boxplot(temp_df_pe_data, sampleinfo_df, outplot, "No", "Sample Groups", "Mean Protein Abundance (log)"); + + #=============================================================================== + # Calculating log fold change and running the "single" code part + #=============================================================================== + + TE_df = data.frame("Genes"=TE_df[,1], "LogFold"=apply(TE_df[,c(which(colnames(TE_df)==condition_g_name),which(colnames(TE_df)==control_g_name))],1,function(x) log(x[1]/x[2],base=2))); + PE_df = data.frame("Genes"=PE_df[,1], "LogFold"=apply(PE_df[,c(which(colnames(PE_df)==condition_g_name),which(colnames(PE_df)==control_g_name))],1,function(x) log(x[1]/x[2],base=2))); + + #=============================================================================== + # Treat missing values + #=============================================================================== + + TE_df[is.infinite(TE_df[,2]),2] = NA; + PE_df[is.infinite(PE_df[,2]),2] = NA; + TE_df[is.na(TE_df)] = 0; + PE_df[is.na(PE_df)] = 0; + + sampleinfo_df = data.frame("Sample"= c("LogFold"), "Group"=c("Fold_Change")) + #=============================================================================== + # Find common samples + #=============================================================================== + + common_samples = intersect(sampleinfo_df[,1], colnames(TE_df)[-1]) %>% intersect(., colnames(PE_df)[-1]); + TE_df = select(TE_df, 1, common_samples); + PE_df = select(PE_df, 1, common_samples); + sampleinfo_df = filter(sampleinfo_df, Sample %in% common_samples); + rownames(sampleinfo_df) = sampleinfo_df[,1]; + + # TE Boxplot + outplot = paste(outdir,"/Box_TE.png",sep="",collape=""); + cat('<br><font color="#ff0000"><h3>Distribution (Box plot) of log fold change </h3></font>', file = htmloutfile, append = TRUE); + cat('<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr>\n', + "<tr><td align=center>", '<img src="Box_TE.png" width=500 height=500></td>\n', file = htmloutfile, append = TRUE); + multisample_boxplot(TE_df, sampleinfo_df, outplot, "Yes", "Sample (log2(case/control))", "Transcript Abundance fold-change (log2)"); + + # PE Boxplot + outplot = paste(outdir,"/Box_PE.png",sep="",collape=""); + cat("<td align=center>", '<img src="Box_PE.png" width=500 height=500></td></tr></table>\n', file = htmloutfile, append = TRUE); + multisample_boxplot(PE_df, sampleinfo_df, outplot, "Yes", "Sample (log2(case/control))", "Protein Abundance fold-change(log2)"); + + + # Log Fold Data + perform_Test_Volcano(TE_df_orig,PE_df_orig,TE_df, PE_df,sampleinfo_df_orig,method,correction_method,volc_with) + + + + # Print PCA + + cat('<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>PCA plot: Transcriptome data</font></th><th><font color=#ffcc33>PCA plot: Proteome data</font></th></tr>\n', + "<tr><td align=center>", '<img src="PCA_TE_all_rep.png" width=500 height=500></td>\n', + "<td align=center>", '<img src="PCA_PE_all_rep.png" width=500 height=500></td></tr></table>\n', + file = htmloutfile, append = TRUE); + + + + cat('<hr/><h2 id="corr_data"><font color=#ff0000>CORRELATION</font></h2>\n', + file = htmloutfile, append = TRUE); + + # TE PE scatter + outplot = paste(outdir,"/TE_PE_scatter.png",sep="",collape=""); + cat('<br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatter plot between Proteome and Transcriptome Abundance</font></th></tr>\n', file = htmloutfile, append = TRUE); + cat("<tr><td align=center>", '<img src="TE_PE_scatter.png" width=800 height=800></td>\n', file = htmloutfile, append = TRUE); + PE_TE_data = data.frame(PE_df, TE_df); + colnames(PE_TE_data) = c("PE_ID","PE_abundance","TE_ID","TE_abundance"); + singlesample_scatter(PE_TE_data, outplot); + + # TE PE Cor + cat("<tr><td align=center>\n", file = htmloutfile, append = TRUE); + singlesample_cor(PE_TE_data, htmloutfile, append=TRUE); + cat('<font color="red">*Note that <u>correlation</u> is <u>sensitive to outliers</u> in the data. So it is important to analyze outliers/influential observations in the data.<br> Below we use <u>Cook\'s distance based approach</u> to identify such influential observations.</font>\n', + file = htmloutfile, append = TRUE); + cat('</td></table>', + file = htmloutfile, append = TRUE); + + cat('<hr/><h2 id="regression_data"><font color=#ff0000>REGRESSION ANALYSIS</font></h2>\n', + file = htmloutfile, append = TRUE); + + # TE PE Regression + singlesample_regression(PE_TE_data,htmloutfile, append=TRUE); + + cat('<hr/><h2 id="cluster_data"><font color=#ff0000>CLUSTER ANALYSIS</font></h2>\n', + file = htmloutfile, append = TRUE); + + #TE PE Heatmap + singlesample_heatmap(PE_TE_data, htmloutfile, hm_nclust); + + #TE PE Clustering (kmeans) + singlesample_kmeans(PE_TE_data, htmloutfile, nclust=as.numeric(numCluster)) + + } +} +cat("<h3>Go To:</h3>\n", + "<ul>\n", + "<li><a href=#sample_dist>Sample distribution</a></li>\n", + "<li><a href=#corr_data>Correlation</a></li>\n", + "<li><a href=#regression_data>Regression analysis</a></li>\n", + "<li><a href=#inf_obs>Influential observations</a></li>\n", + "<li><a href=#cluster_data>Cluster analysis</a></li></ul>\n", + "<br><a href=#>TOP</a>", + file = htmloutfile, append = TRUE); +cat("</body></html>\n", file = htmloutfile, append = TRUE);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/quantp.xml Fri Sep 14 12:22:31 2018 -0400 @@ -0,0 +1,195 @@ +<tool id="quantp" name="QuanTP" version="1.0.0"> + <description>Correlation between protein and transcript abundances</description> + <requirements> + <requirement type="package" version="1.10.4">r-data.table</requirement> + <requirement type="package" version="3.0.1">r-gplots</requirement> + <requirement type="package" version="0.7.6">r-dplyr</requirement> + <requirement type="package" version="3.0.0">r-ggplot2</requirement> + <requirement type="package" version="0.4.5">r-ggfortify</requirement> + </requirements> + <command detect_errors="exit_code"><![CDATA[ +Rscript '$__tool_directory__/quantp.r' + $experiment_design_option.sample_type + $experiment_design_option.method_type + $experiment_design_option.exp_design + '$pe_exp' + '$te_exp' + $experiment_design_option.correction_method + $cooksd_upper + $nclust + $hm_nclust + $experiment_design_option.volcano_with + '$html_file' + '$html_file.files_path' +]]></command> + <inputs> + <param name="pe_exp" type="data" format="tabular" label="Input Protein Abundance File" help="Protein abundance input file"/> + <param name="te_exp" type="data" format="tabular" label="Input RNA Abundance File" help="Transcript abundance input file"/> + <conditional name="experiment_design_option"> + <param name="sample_type" type="select" label="Select data input type" help="If the input files already have fold-change values, select Log fold-change. Else, select abundances and the tool will perform the fold-change analysis"> + <option value="multiple" selected="True">Abundances from different conditions with or without replicates (in multiple columns)</option> + <option value="logfold">Log fold-change values (or single condition abundance without replicates in single column) data</option> + </param> + <when value="logfold"> + <param name="exp_design" type="hidden" value="none" /> + <param name="method_type" type="hidden" value="none" /> + <param name="correction_method" type="hidden" value="none" /> + <param name="volcano_with" type="hidden" value="pval" /> + </when> + <when value="multiple"> + <param name="exp_design" type="data" format="tabular" help="Please check the format of the experiment design file"> + <label>Experiment design File (Please see the format below)</label> + </param> + <param name="method_type" type="select" label="Data summarization method" help="Perform T-Test on selecting Mean; Wilcoxon Ranksum Test on selecting Median"> + <option value="mean" selected="True">Mean (Default)</option> + <option value="median">Median</option> + </param> + <param name="correction_method" type="select" label="Multiple testing correction method"> + <option value="BH" selected="True">Benjamini and Hochberg (BH) (Default)</option> + <option value="holm">Holm</option> + <option value="hochberg">Hochberg</option> + <option value="hommel">Hommel</option> + <option value="bonferroni">Bonferroni</option> + <option value="BY">Benjamini and Yekutieli (BY)</option> + <option value="none">None</option> + </param> + <param name="volcano_with" type="select" display="radio" label="Volcano plot with p-value or adjusted p-value"> + <option value="pval" selected="True">P-value (Default)</option> + <option value="adj_pval">Adjusted P-value</option> + </param> + </when> + </conditional> + <param name="cooksd_upper" type="integer" value="4" optional="false" > + <label>Influential Observation cutoff: Observations > value * mean of Cook's distances (Default: "4" * mean(Cook's Distance))</label> + </param> + <param name="nclust" type="select" label="K-mean clustering: Number of clusters"> + <option value="1">1</option> + <option value="2">2</option> + <option value="3">3</option> + <option value="4" selected="True">4</option> + <option value="5">5</option> + <option value="6">6</option> + <option value="7">7</option> + <option value="8">8</option> + <option value="9">9</option> + <option value="10">10</option> + </param> + + <param name="hm_nclust" type="select" label="Hierarchical clustering: Number of clusters (from Heatmap)"> + <option value="1">1</option> + <option value="2">2</option> + <option value="3">3</option> + <option value="4">4</option> + <option value="5" selected="True">5</option> + <option value="6">6</option> + <option value="7">7</option> + <option value="8">8</option> + <option value="9">9</option> + <option value="10">10</option> + </param> + </inputs> + <outputs> + <data format="html" name="html_file" label="protein transcript correlation on ${pe_exp.name} and ${te_exp.name}"/> + </outputs> + <tests> + <test> + <conditional name="experiment_design_option"> + <param name="sample_type" value="multiple"/> + <param name="method_type" value="mean"/> + <param name="exp_design" value="exp_design_file.tabular" ftype="tabular" /> + <param name="correction_method" value="BH"/> + <param name="volcano_with" value="pval"/> + </conditional> + <param name="pe_exp" value="protein_data.tabular" ftype="tabular" /> + <param name="te_exp" value="transcript_data.tabular" ftype="tabular" /> + <param name="cooksd_upper" value="4"/> + <param name="nclust" value="4"/> + <param name="hm_nclust" value="5"/> + <output name="html_file"> + <assert_contents> + <has_text text="SAMPLE DISTRIBUTION" /> + </assert_contents> + </output> + </test> + </tests> + <help><![CDATA[ + +**What it does** + +QuanTP correlates *transcript abundance* and *protein abundance* to examine the association between them. + +It either takes in the log fold-change of abundances as input or raw abundances from different conditions where it calculates the log ratios of abundances between two conditions. + +Transcript input file can be generated from the quantitative RNA-Seq study whereas Protein input file can be generated from quantitative analysis of mass-spectrometry-based protein data. + +----- + +**Input file formats** + +**Protein data file** + +First column - Gene + +Following columns - Abundance values (or log fold-change values) + +Example of Protein input file + +====== ========= ========= ========= ========= ========= ========= ========= ========= +Gene sample1 sample2 sample3 sample4 sample5 sample6 sample7 sample8 +------ --------- --------- --------- --------- --------- --------- --------- --------- +GeneX value value value value value value value value +GeneY value value value value value value value value +GeneZ value value value value value value value value +====== ========= ========= ========= ========= ========= ========= ========= ========= + + +**Transcript data file** + +First column - Gene + +Following columns - Abundance values (or log fold-change values) + +Example of Transcript input file + +====== ========= ========= ========= ========= ========= ========= ========= ========= +Gene sample1 sample2 sample3 sample4 sample5 sample6 sample7 sample8 +------ --------- --------- --------- --------- --------- --------- --------- --------- +GeneX value value value value value value value value +GeneY value value value value value value value value +GeneZ value value value value value value value value +====== ========= ========= ========= ========= ========= ========= ========= ========= + + +**Data input type** + +If data input type is abundance, experiment design file is required. + +Example of experiment design file + +======== ========= +case groupA +control groupB +sample1 groupA +sample2 groupA +sample3 groupA +sample4 groupA +sample5 groupB +sample6 groupB +sample7 groupB +sample8 groupB +======== ========= + +Note: No title/header in experiment design file and the first two lines of the experiment design must have keyword "case" and "control" + + ]]> + </help> + <citations> + <citation type="bibtex"> +@misc{QuanTP: A software resource for quantitative proteo-transcriptomic comparative data analysis and informatics, + author={Praveen Kumar, Priyabrata Panigrahi, James Johnson, Wanda Weber, Subina Mehta, Ray Sajulga, Caleb Easterly, Brian Crooker, Mohammad Heydarian, Krishanpal Anamika, Timothy Griffin, and Pratik Jagtap}, + year={2018}, + title={QuanTP} +} + </citation> + </citations> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/exp_design_file.tabular Fri Sep 14 12:22:31 2018 -0400 @@ -0,0 +1,6 @@ +case G1 +control G2 +D03_01 G1 +D03_02 G1 +M14_01 G2 +M14_02 G2
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.html Fri Sep 14 12:22:31 2018 -0400 @@ -0,0 +1,155 @@ +<html><head></head><body> +<h1><u>QuanTP: Association between abundance ratios of transcript and protein</u></h1><hr/> + <font><h3>Input data summary</h3></font> + <ul> + <li>Abbreviations used: PE (Proteome data) and TE (Transcriptome data) </li><br> + <li>Input Proteome data dimension (Row Column): 2817 x 5 </li> + <li>Input Transcriptome data dimension (Row Column): 2817 x 5 </li></ul><hr/> +<h3 id=table_of_content>Table of Contents:</h3> + <ul> + <li><a href=#sample_dist>Sample distribution</a></li> + <li><a href=#corr_data>Correlation</a></li> + <li><a href=#regression_data>Regression analysis</a></li> + <li><a href=#inf_obs>Influential observations</a></li> + <li><a href=#cluster_data>Cluster analysis</a></li></ul><hr/> +<h2 id="sample_dist"><font color=#ff0000>SAMPLE DISTRIBUTION</font></h2> +<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> + <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr> + <tr><td align=center> <img src="Box_TE_all_rep.png" width=500 height=500></td> +<td align=center> <img src="Box_PE_all_rep.png" width=500 height=500></td></tr></table> +<br><font color="#ff0000"><h3>Sample wise distribution (Box plot) after using mean on replicates </h3></font><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr> + <tr><td align=center> <img src="Box_TE_rep.png" width=500 height=500></td> +<td align=center> <img src="Box_PE_rep.png" width=500 height=500></td></tr></table> +<br><font color="#ff0000"><h3>Distribution (Box plot) of log fold change </h3></font><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Boxplot: Transcriptome data</font></th><th><font color=#ffcc33>Boxplot: Proteome data</font></th></tr> + <tr><td align=center> <img src="Box_TE.png" width=500 height=500></td> +<td align=center> <img src="Box_PE.png" width=500 height=500></td></tr></table> +<br><br><font size=5><b><a href='PE_TE_logfold_pval.txt' target='_blank'>Download the complete fold change data here</a></b></font><br> +<br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Transcript Fold-Change</font></th><th><font color=#ffcc33>Protein Fold-Change</font></th></tr> +<tr><td align=center> <img src="TE_volcano.png" width=600 height=600></td> +<td align=center> <img src="PE_volcano.png" width=600 height=600></td></tr></table><br> +<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>PCA plot: Transcriptome data</font></th><th><font color=#ffcc33>PCA plot: Proteome data</font></th></tr> + <tr><td align=center> <img src="PCA_TE_all_rep.png" width=500 height=500></td> + <td align=center> <img src="PCA_PE_all_rep.png" width=500 height=500></td></tr></table> +<hr/><h2 id="corr_data"><font color=#ff0000>CORRELATION</font></h2> +<br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatter plot between Proteome and Transcriptome Abundance</font></th></tr> +<tr><td align=center> <img src="TE_PE_scatter.png" width=800 height=800></td> +<tr><td align=center> +<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Method 1</font></th><th><font color=#ffcc33>Method 2</font></th><th><font color=#ffcc33>Method 3</font></th></tr> +<tr><td>Correlation method</td><td> Pearson's product-moment correlation </td><td> Spearman's rank correlation rho </td><td> Kendall's rank correlation tau </td></tr> + <tr><td>Correlation coefficient</td><td> 0.1173569 </td><td> 0.1608612 </td><td> 0.1093701 </td></tr> +</table> +<font color="red">*Note that <u>correlation</u> is <u>sensitive to outliers</u> in the data. So it is important to analyze outliers/influential observations in the data.<br> Below we use <u>Cook's distance based approach</u> to identify such influential observations.</font> +</td></table><hr/><h2 id="regression_data"><font color=#ff0000>REGRESSION ANALYSIS</font></h2> +<font><h3>Linear Regression model fit between Proteome and Transcriptome data</h3></font> + <p>Assuming a linear relationship between Proteome and Transcriptome data, we here fit a linear regression model.</p> + <table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr> +<tr><td>Formula</td><td> PE_abundance~TE_abundance </td></tr> + <tr><td colspan='2' align='center'> <b>Coefficients</b></td> </tr> + <tr><td> (Intercept) </td><td> -0.06910598 (Pvalue: 1.220723e-05 ) </td></tr> + <tr><td> TE_abundance </td><td> 0.1712395 (Pvalue: 4.168015e-10 ) </td></tr> + <tr><td colspan='2' align='center'> <b>Model parameters</b></td> </tr> + <tr><td>Residual standard error</td><td> 0.8363295 ( 2815 degree of freedom)</td></tr> + <tr><td>F-statistic</td><td> 39.31142 ( on 1 and 2815 degree of freedom)</td></tr> + <tr><td>R-squared</td><td> 0.01377265 </td></tr> + <tr><td>Adjusted R-squared</td><td> 0.0134223 </td></tr> +</table> +<font color='#ff0000'><h3>Regression and diagnostics plots</h3></font> +<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "><tr bgcolor="#7a0019"><th> <font color='#ffcc33'><h4>1) <u>Residuals vs Fitted plot</h4></font></u></th> + <th><font color=#ffcc33><h4>2) <u>Normal Q-Q plot of residuals</h4></font></u></th></tr> +<tr><td align=center><img src="PE_TE_lm_1.png" width=600 height=600></td><td align=center><img src="PE_TE_lm_2.png" width=600 height=600></td></tr> +<tr><td align=center>This plot checks for linear relationship assumptions.<br>If a horizontal line is observed without any distinct patterns, it indicates a linear relationship.</td> + <td align=center>This plot checks whether residuals are normally distributed or not.<br>It is good if the residuals points follow the straight dashed line i.e., do not deviate much from dashed line.</td></tr></table> +<br><h2 id="inf_obs"><font color=#ff0000>Outliers based on the residuals from regression analysis</font></h2> +<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> + <tr bgcolor="#7a0019"><th colspan=2><font color=#ffcc33>Residuals from Regression</font></th></tr> + <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr> +<tr><td>Mean Residual value</td><td> 1.942328e-17 </td></tr> + <tr><td>Standard deviation (Residuals)</td><td> 0.836181 </td></tr> + <tr><td>Total outliers (Residual value > 2 standard deviation from the mean)</td><td> 164 <font size=4>(<b><a href=PE_TE_outliers_residuals.txt target="_blank">Download these 164 data points with high residual values here</a></b>)</font></td> + <tr><td colspan=2 align=center><font size=4>(<b><a href=PE_TE_abundance_residuals.txt target="_blank">Download the complete residuals data here</a></b>)</font></td></td> + </table><br><br> +<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "><tr bgcolor="#7a0019"><th><font color=#ffcc33><h4>3) <u>Residuals vs Leverage plot</h4></font></u></th></tr> +<tr><td align=center><img src="PE_TE_lm_5.png" width=600 height=600></td></tr> +<tr><td align=center>This plot is useful to identify any influential cases, that is outliers or extreme values.<br>They might influence the regression results upon inclusion or exclusion from the analysis.</td></tr></table><br> +<hr/><h2 id="inf_obs"><font color=#ff0000>INFLUENTIAL OBSERVATIONS</font></h2> +<p><b>Cook's distance</b> computes the influence of each data point/observation on the predicted outcome. i.e. this measures how much the observation is influencing the fitted values.<br>In general use, those observations that have a <b>Cook's distance > than 4 times the mean</b> may be classified as <b>influential.</b></p> +<img src="PE_TE_lm_cooksd.png" width=800 height=800> <br>In the above plot, observations above red line ( 4 * mean Cook's distance) are influential. Genes that are outliers could be important. These observations influences the correlation values and regression coefficients<br><br><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Value</font></th></tr> +<tr><td>Mean Cook's distance</td><td> 0.0004875011 </td></tr> + <tr><td>Total influential observations (Cook's distance > 4 * mean Cook's distance)</td><td> 115 </td> + <tr><td>Observations with Cook's distance < 4 * mean Cook's distance</td><td> 2702 </td> + </table><br><br> +<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Scatterplot: Before removal</font></th><th><font color=#ffcc33>Scatterplot: After removal</font></th></tr> +<tr><td align=center><!--<font color='#ff0000'><h3>Scatter plot between Proteome and Transcriptome Abundance</h3></font> +--> <img src="TE_PE_scatter.png" width=600 height=600></td> +<td align=center> + <img src="AbundancePlot_scatter_without_outliers.png" width=600 height=600></td></tr> +<tr><td> +<table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Method 1</font></th><th><font color=#ffcc33>Method 2</font></th><th><font color=#ffcc33>Method 3</font></th></tr> +<tr><td>Correlation method</td><td> Pearson's product-moment correlation </td><td> Spearman's rank correlation rho </td><td> Kendall's rank correlation tau </td></tr> + <tr><td>Correlation coefficient</td><td> 0.1173569 </td><td> 0.1608612 </td><td> 0.1093701 </td></tr> +</table> +</td> +<td><table border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#7a0019"><th><font color=#ffcc33>Parameter</font></th><th><font color=#ffcc33>Method 1</font></th><th><font color=#ffcc33>Method 2</font></th><th><font color=#ffcc33>Method 3</font></th></tr> +<tr><td>Correlation method</td><td> Pearson's product-moment correlation </td><td> Spearman's rank correlation rho </td><td> Kendall's rank correlation tau </td></tr> + <tr><td>Correlation coefficient</td><td> 0.1334038 </td><td> 0.1611936 </td><td> 0.1082761 </td></tr> +</table></td></tr></table> +<br><br><font size=5><b><a href='PE_TE_influential_observation.txt' target='_blank'>Download the complete list of influential observations</a></b></font> <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>
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/transcript_data.tabular Fri Sep 14 12:22:31 2018 -0400 @@ -0,0 +1,2818 @@ +Gene D03_01 D03_02 M14_01 M14_02 +ELANE -0.08223053805901 0.128367421476871 0.168575604288361 0.179386061121957 +AGL 18.3503026568495 18.3971714903452 28.8550772364392 28.2897976837947 +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 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