| Previous changeset 0:75faf9a89f5b (2018-09-14) Next changeset 2:ed0bb50d7ffe (2019-01-09) |
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Commit message:
planemo upload commit 1887dff812162880d66b003a927867cd5000c98f |
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modified:
quantp.r quantp.xml |
| b |
| 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 |
| [ |
| b'@@ -16,6 +16,7 @@\n png(outfile, width = 6, height = 6, units = \'in\', res=300);\n # bitmap(outfile, "png16m");\n g = autoplot(prcomp(select(tempdf, -Group)), data = tempdf, colour = \'Group\', size=3);\n+ saveWidget(ggplotly(g), file.path(gsub("\\\\.png", "\\\\.html", outplot)))\n plot(g);\n dev.off();\n }\n@@ -30,20 +31,20 @@\n regmodel_summary = summary(regmodel);\n \n cat("<font><h3>Linear Regression model fit between Proteome and Transcriptome data</h3></font>\\n",\n- "<p>Assuming a linear relationship between Proteome and Transcriptome data, we here fit a linear regression model.</p>\\n",\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\',\n- file = htmloutfile, append = TRUE);\n+ "<p>Assuming a linear relationship between Proteome and Transcriptome data, we here fit a linear regression model.</p>\\n",\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\',\n+ file = htmloutfile, append = TRUE);\n \n cat("<tr><td>Formula</td><td>","PE_abundance~TE_abundance","</td></tr>\\n",\n- "<tr><td colspan=\'2\' align=\'center\'> <b>Coefficients</b></td>","</tr>\\n",\n- "<tr><td>",names(regmodel$coefficients[1]),"</td><td>",regmodel$coefficients[1]," (Pvalue:", regmodel_summary$coefficients[1,4],")","</td></tr>\\n",\n- "<tr><td>",names(regmodel$coefficients[2]),"</td><td>",regmodel$coefficients[2]," (Pvalue:", regmodel_summary$coefficients[2,4],")","</td></tr>\\n",\n- "<tr><td colspan=\'2\' align=\'center\'> <b>Model parameters</b></td>","</tr>\\n",\n- "<tr><td>Residual standard error</td><td>",regmodel_summary$sigma," (",regmodel_summary$df[2]," degree of freedom)</td></tr>\\n",\n- "<tr><td>F-statistic</td><td>",regmodel_summary$fstatistic[1]," ( on ",regmodel_summary$fstatistic[2]," and ",regmodel_summary$fstatistic[3]," degree of freedom)</td></tr>\\n",\n- "<tr><td>R-squared</td><td>",regmodel_summary$r.squared,"</td></tr>\\n",\n- "<tr><td>Adjusted R-squared</td><td>",regmodel_summary$adj.r.squared,"</td></tr>\\n",\n- file = htmloutfile, append = TRUE);\n+ "<tr><td colspan=\'2\' align=\'center\'> <b>Coefficients</b></td>","</tr>\\n",\n+ "<tr><td>",names(regmodel$coefficients[1]),"</td><td>",regmodel$coefficients[1]," (Pvalue:", regmodel_summary$coefficients[1,4],")","</td></tr>\\n",\n+ "<tr><td>",names(regmodel$coefficients[2]),"</td><td>",regmodel$coefficients[2]," (Pvalue:", regmodel_summary$coefficients[2,4],")","</td></tr>\\n",\n+ "<tr><td colspan=\'2\' align=\'center\'> <b>Model parameters</b></td>","</tr>\\n",\n+ "<tr><td>Residual standard error</td><td>",regmodel_summary$sigma," (",regmodel_summary$df[2]," degree of freedom)</td></tr>\\n",\n+ "<tr><td>F-statistic</td><td>",regmodel_summary$fstatistic[1]," ( on ",regmodel_summary$fstatistic[2]," and ",regmodel_summary$fstatistic[3]," degree of freedom)</td></tr>\\n",\n+ "<tr><td>R-squared</td><td>",regmodel_summary$r.squared,"</td></tr>\\n",\n+ "<tr><td>Adjusted R-squared</td><td>",regmodel_summary$adj.r.squared,"</td></tr>\\n",\n+ file = htmloutfile, append = TRUE);\n \n cat("</table>\\n", file = htmloutfile, append = TRUE);\n \n@@ -58,13 +59,27 @@\n plot(regmodel, 1, cex.lab=1.5);\n dev.off();\n \n+ suppressWarnings(g <- autoplot(regmodel, label = FALSE)[[1]] +\n+ geom_point(aes(text=sprintf("Residual: %.2f<br>Fitted value: %.2f<br>Gene: %s", .fitted, .resid, PE_TE_data$PE_ID)),\n+ shape = 1, size = .1, stroke = .2) +\n+ theme_light())\n+ saveWidget(ggplotly(g, tooltip= c("text")), file.path(gsub("\\\\.png", "\\\\.html", outplot)))\n+ \n outplot = paste(outdir,"/PE_TE_lm_2.png",sep="",collapse="");\n png(outplot,width = 10, height = 10, units = \'in\', res=300);\n # bitmap(outplot, "png'..b'"#7a0019"><th><font color=#ffcc33>Scatter plot between Proteome and Transcriptome Abundance</font></th></tr>\\n\', file = htmloutfile, append = TRUE);\n- cat("<tr><td align=center>", \'<img src="TE_PE_scatter.png" width=800 height=800></td>\\n\', file = htmloutfile, append = TRUE);\n- PE_TE_data = data.frame(PE_df, TE_df);\n- colnames(PE_TE_data) = c("PE_ID","PE_abundance","TE_ID","TE_abundance");\n singlesample_scatter(PE_TE_data, outplot); \n-\n+ lines <- extractWidgetCode(outplot);\n+ postscripts <- c(postscripts, lines$postscripts); \n+ 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$widget_div),\n+ \'</td>\\n\', file = htmloutfile, append = TRUE);\n+ \n # TE PE Cor\n cat("<tr><td align=center>\\n", file = htmloutfile, append = TRUE);\n singlesample_cor(PE_TE_data, htmloutfile, append=TRUE);\n cat(\'<font color="red">*Note that <u>correlation</u> is <u>sensitive to outliers</u> in the data. So it is important to analyze outliers/influential observations in the data.<br> Below we use <u>Cook\\\'s distance based approach</u> to identify such influential observations.</font>\\n\',\n- file = htmloutfile, append = TRUE);\n+ file = htmloutfile, append = TRUE);\n cat(\'</td></table>\',\n- file = htmloutfile, append = TRUE);\n+ file = htmloutfile, append = TRUE);\n \n cat(\'<hr/><h2 id="regression_data"><font color=#ff0000>REGRESSION ANALYSIS</font></h2>\\n\',\n- file = htmloutfile, append = TRUE);\n+ file = htmloutfile, append = TRUE);\n \n # TE PE Regression\n singlesample_regression(PE_TE_data,htmloutfile, append=TRUE);\n+ postscripts <- c(postscripts, c(extractWidgetCode(paste(outdir,"/PE_TE_lm_1.png",sep="",collapse=""))$postscripts,\n+ extractWidgetCode(paste(outdir,"/PE_TE_lm_2.png",sep="",collapse=""))$postscripts,\n+ extractWidgetCode(paste(outdir,"/PE_TE_lm_5.png",sep="",collapse=""))$postscripts,\n+ extractWidgetCode(paste(outdir,"/PE_TE_lm_cooksd.png",sep="",collapse=""))$postscripts,\n+ extractWidgetCode(paste(outdir,"/AbundancePlot_scatter_without_outliers.png",sep="",collapse=""))$postscripts,\n+ gsub(\'data-for="html\', \'data-for="secondhtml"\', \n+ extractWidgetCode(paste(outdir,"/TE_PE_scatter.png",sep="",collapse=""))$postscripts)));\n \n cat(\'<hr/><h2 id="cluster_data"><font color=#ff0000>CLUSTER ANALYSIS</font></h2>\\n\',\n- file = htmloutfile, append = TRUE);\n+ file = htmloutfile, append = TRUE);\n \n #TE PE Heatmap\n singlesample_heatmap(PE_TE_data, htmloutfile, hm_nclust);\n+ lines <- extractWidgetCode(paste(outdir,"/PE_TE_heatmap.png",sep="",collapse=""))\n+ postscripts <- c(postscripts, lines$postscripts)\n+ prescripts <- c(prescripts, lines$prescripts)\n \n #TE PE Clustering (kmeans)\n singlesample_kmeans(PE_TE_data, htmloutfile, nclust=as.numeric(numCluster))\n- \n+ postscripts <- c(postscripts, extractWidgetCode(paste(outdir,"/PE_TE_kmeans.png",sep="",collapse=""))$postscripts);\n }\n }\n cat("<h3>Go To:</h3>\\n",\n@@ -1002,3 +1219,14 @@\n "<br><a href=#>TOP</a>",\n file = htmloutfile, append = TRUE);\n cat("</body></html>\\n", file = htmloutfile, append = TRUE);\n+\n+\n+#===============================================================================\n+# Add masked-javascripts tags to HTML file in the head and end\n+#===============================================================================\n+\n+htmllines <- readLines(htmloutfile)\n+htmllines[1] <- paste(\'<html>\\n<head>\\n\', paste(prescripts, collapse=\'\\n\'), \'\\n</head>\\n<body>\')\n+cat(paste(htmllines, collapse=\'\\n\'), file = htmloutfile)\n+cat(\'\\n\', paste(postscripts, collapse=\'\\n\'), "\\n",\n+ "</body>\\n</html>\\n", file = htmloutfile, append = TRUE);\n' |
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| 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> |