Mercurial > repos > devteam > dwt_var_perclass

changeset 1:0c1388b563a8 draft default tip

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"planemo upload for repository https://github.com/galaxyproject/tools-devteam/tree/master/tools/dwt_var_perclass commit f929353ffb0623f2218d7dec459c7da62f3b0d24"
author devteam
date Mon, 06 Jul 2020 18:12:51 +0000
parents 27c5c2979e32
children
files execute_dwt_var_perClass.R execute_dwt_var_perClass.pl execute_dwt_var_perClass.xml test-data/in.tsv test-data/out.pdf
diffstat 5 files changed, 258 insertions(+), 331 deletions(-) [+]
line wrap: on
line diff
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/execute_dwt_var_perClass.R	Mon Jul 06 18:12:51 2020 +0000
@@ -0,0 +1,212 @@
+######################################################################
+## plot power spectra, i.e. wavelet variance by class
+## add code to create null bands by permuting the original data series
+## get class of maximum significant variance per feature
+## generate plots and table matrix of variance including p-values
+######################################################################
+library("wavethresh");
+library("waveslim");
+
+options(echo = FALSE)
+
+## normalize data
+norm <- function(data) {
+    v <- (data - mean(data)) / sd(data);
+    if (sum(is.na(v)) >= 1) {
+        v <- data;
+    }
+    return(v);
+}
+
+dwt_var_permut_get_max <- function(data, names, outfile, filter = 4, bc = "symmetric", method = "kendall", wf = "haar", boundary = "reflection") {
+    max_var <- NULL;
+    matrix <- NULL;
+    title <- NULL;
+    final_pvalue <- NULL;
+    short_levels <- NULL;
+    scale <- NULL;
+
+    print(names);
+
+    par(mfcol = c(length(names), length(names)), mar = c(0, 0, 0, 0), oma = c(4, 3, 3, 2), xaxt = "s", cex = 1, las = 1);
+
+    short_levels <- wavethresh::wd(data[, 1], filter.number = filter, bc = bc)$nlevels;
+
+    title <- c("motif");
+    for (i in seq_len(short_levels)) {
+        title <- c(title, paste(i, "var", sep = "_"), paste(i, "pval", sep = "_"), paste(i, "test", sep = "_"));
+    }
+    print(title);
+
+    ## normalize the raw data
+    data <- apply(data, 2, norm);
+
+    for (i in seq_len(length(names))) {
+        for (j in seq_len(length(names))) {
+            temp <- NULL;
+            results <- NULL;
+            wave1_dwt <- NULL;
+            out <- NULL;
+
+            out <- vector(length = length(title));
+            temp <- vector(length = short_levels);
+
+            if (i != j) {
+                plot(temp, type = "n", axes = FALSE, xlab = NA, ylab = NA);
+                box(col = "grey");
+                grid(ny = 0, nx = NULL);
+            } else {
+
+                wave1_dwt <- waveslim::dwt(data[, i], wf = wf, short_levels, boundary = boundary);
+
+                temp_row <- (short_levels + 1) * -1;
+                temp_col <- 1;
+                temp <- waveslim::wave.variance(wave1_dwt)[temp_row, temp_col];
+
+                ##permutations code :
+                feature1 <- NULL;
+                null <- NULL;
+                var_25 <- NULL;
+                var_975 <- NULL;
+                med <- NULL;
+
+                feature1 <- data[, i];
+                for (k in seq_len(1000)) {
+                    nk_1 <- NULL;
+                    null_levels <- NULL;
+                    var <- NULL;
+                    null_wave1 <- NULL;
+
+                    nk_1 <- sample(feature1, length(feature1), replace = FALSE);
+                    null_levels <- wavethresh::wd(nk_1, filter.number = filter, bc = bc)$nlevels;
+                    var <- vector(length = length(null_levels));
+                    null_wave1 <- waveslim::dwt(nk_1, wf = wf, short_levels, boundary = boundary);
+                    var <- waveslim::wave.variance(null_wave1)[-8, 1];
+                    null <- rbind(null, var);
+                }
+                null <- apply(null, 2, sort, na.last = TRUE);
+                var_25 <- null[25, ];
+                var_975 <- null[975, ];
+                med <- (apply(null, 2, median, na.rm = TRUE));
+
+                ## plot
+                results <- cbind(temp, var_25, var_975);
+                matplot(results, type = "b", pch = "*", lty = 1, col = c(1, 2, 2), axes = F);
+
+                ## get pvalues by comparison to null distribution
+                out <- (names[i]);
+                for (m in seq_len(length(temp))) {
+                    print(paste("scale", m, sep = " "));
+                    print(paste("var", temp[m], sep = " "));
+                    print(paste("med", med[m], sep = " "));
+                    pv <- NULL;
+                    tail <- NULL;
+                    out <- c(out, format(temp[m], digits = 3));
+                    if (temp[m] >= med[m]) {
+                        ## R tail test
+                        print("R");
+                        tail <- "R";
+                        pv <- (length(which(null[, m] >= temp[m]))) / (length(na.exclude(null[, m])));
+
+                    } else {
+                        ## L tail test
+                        print("L");
+                        tail <- "L";
+                        pv <- (length(which(null[, m] <= temp[m]))) / (length(na.exclude(null[, m])));
+                    }
+                    out <- c(out, pv);
+                    print(pv);
+                    out <- c(out, tail);
+                    ## get variances outside null bands by comparing temp to null
+                    ### temp stores variance for each scale, and null stores permuted variances for null bands
+                    if (temp[m] <= var_975[m]) {
+                        temp[m] <- NA;
+                    }
+                }
+                final_pvalue <- rbind(final_pvalue, out);
+                matrix <- rbind(matrix, temp)
+            }
+            ## labels
+            if (i == 1) {
+                mtext(names[j], side = 2, line = 0.5, las = 3, cex = 0.25);
+            }
+            if (j == 1) {
+                mtext(names[i], side = 3, line = 0.5, cex = 0.25);
+            }
+            if (j == length(names)) {
+                axis(1, at = (1:short_levels), las = 3, cex.axis = 0.5);
+            }
+        }
+    }
+    colnames(final_pvalue) <- title;
+
+    ## get maximum variance larger than expectation by comparison to null bands
+    varnames <- vector();
+    for (i in seq_len(length(names))) {
+        name1 <- paste(names[i], "var", sep = "_")
+        varnames <- c(varnames, name1)
+    }
+    rownames(matrix) <- varnames;
+    colnames(matrix) <- (1:short_levels);
+    max_var <- names;
+    scale <- vector(length = length(names));
+    for (x in seq_len(nrow(matrix))) {
+        if (length(which.max(matrix[x, ])) == 0) {
+            scale[x] <- NA;
+        } else{
+            scale[x] <- colnames(matrix)[which.max(matrix[x, ])];
+        }
+    }
+    max_var <- cbind(max_var, scale);
+    write.table(max_var, file = outfile, sep = "\t", quote = FALSE, row.names = FALSE, append = TRUE);
+    return(final_pvalue);
+}
+
+## execute
+## read in data
+args <- commandArgs(trailingOnly = TRUE)
+
+data_test <- NULL;
+data_test <- read.delim(args[1]);
+
+count <- ncol(data_test)
+print(paste("The number of columns in the input file is: ", count));
+
+# check if the number of motifs is not a multiple of 12, and round up is so
+if (count %% 12 != 0) {
+    print("the number of motifs is not a multiple of 12")
+    count2 <- ceiling(count / 12);
+}else{
+    print("the number of motifs is a multiple of 12")
+    count2 <- count / 12
+}
+print(paste("There will be", count2, "subfiles"))
+
+pdf(file = args[4], width = 11, height = 8);
+
+## loop to read and execute on all count2 subfiles
+final <- NULL;
+for (x in seq_len(count2)) {
+    sub <- NULL;
+    sub_names <- NULL;
+    a <- NULL;
+    b <- NULL;
+
+    a <- ((x - 1) * 12 + 1);
+    b <- x * 12;
+
+    if (x < count2) {
+        sub <- data_test[, +c(a:b)];
+        sub_names <- colnames(data_test)[a:b];
+        final <- rbind(final, dwt_var_permut_get_max(sub, sub_names, args[2]));
+    }
+    else{
+        sub <- data_test[, +c(a:ncol(data_test))];
+        sub_names <- colnames(data_test)[a:ncol(data_test)];
+        final <- rbind(final, dwt_var_permut_get_max(sub, sub_names, args[2]));
+    }
+}
+
+dev.off();
+
+write.table(final, file = args[3], sep = "\t", quote = FALSE, row.names = FALSE);
--- a/execute_dwt_var_perClass.pl	Thu Jan 23 12:31:07 2014 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,320 +0,0 @@
-#!/usr/bin/perl -w
-
-use warnings;
-use IO::Handle;
-use POSIX qw(floor ceil);
-
-# example: perl execute_dwt_var_perClass.pl hg18_NCNR_10bp_3flanks_deletionHotspot_data_del.txt deletionHotspot 3flanks del
-
-$usage = "execute_dwt_var_perClass.pl [TABULAR.in] [TABULAR.out] [TABULAR.out] [PDF.out] \n";
-die $usage unless @ARGV == 4;
-
-#get the input arguments
-my $inputFile = $ARGV[0];
-my $firstOutputFile = $ARGV[1];
-my $secondOutputFile = $ARGV[2];
-my $thirdOutputFile = $ARGV[3];
-
-open (INPUT, "<", $inputFile) || die("Could not open file $inputFile \n");
-open (OUTPUT1, ">", $firstOutputFile) || die("Could not open file $firstOutputFile \n");
-open (OUTPUT2, ">", $secondOutputFile) || die("Could not open file $secondOutputFile \n");
-open (OUTPUT3, ">", $thirdOutputFile) || die("Could not open file $thirdOutputFile \n");
-open (ERROR,  ">", "error.txt")  or die ("Could not open file error.txt \n");
-
-#save all error messages into the error file $errorFile using the error file handle ERROR
-STDERR -> fdopen( \*ERROR,  "w" ) or die ("Could not direct errors to the error file error.txt \n");
-
-# choosing meaningful names for the output files
-$max_dwt = $firstOutputFile; 
-$pvalue = $secondOutputFile; 
-$pdf = $thirdOutputFile; 
-
-# count the number of columns in the input file
-while($buffer = <INPUT>){
-	#if ($buffer =~ m/interval/){
-		chomp($buffer);
-		$buffer =~ s/^#\s*//;
-		@contrl = split(/\t/, $buffer);
-		last;
-	#}
-}
-print "The number of columns in the input file is: " . (@contrl) . "\n";
-print "\n";
-
-# count the number of motifs in the input file
-$count = 0;
-for ($i = 0; $i < @contrl; $i++){
-	$count++;
-	print "# $contrl[$i]\n";
-}
-print "The number of motifs in the input file is:  $count \n";
-
-# check if the number of motifs is not a multiple of 12, and round up is so
-$count2 = ($count/12);
-if ($count2 =~ m/(\D)/){
-	print "the number of motifs is not a multiple of 12 \n";
-	$count2 = ceil($count2);
-}
-else {
-	print "the number of motifs is a multiple of 12 \n";
-}
-print "There will be $count2 subfiles\n\n";
-
-# split infile into subfiles only 12 motif per file for R plotting
-for ($x = 1; $x <= $count2; $x++){
-	$a = (($x - 1) * 12 + 1);
-	$b = $x * 12;
-	
-	if ($x < $count2){
-		print "# data.short $x <- data_test[, +c($a:$b)]; \n"; 
-	}
-	else{
-		print "# data.short $x <- data_test[, +c($a:ncol(data_test)]; \n";
-	}
-}
-
-print "\n";
-print "There are 4 output files: \n";
-print "The first output file is a pdf file\n";
-print "The second output file is a max_dwt file\n";
-print "The third output file is a pvalues file\n";
-print "The fourth output file is a test_final_pvalues file\n";
-
-# write R script
-$r_script = "get_dwt_varPermut_getMax.r"; 
-print "The R file name is: $r_script \n";
-
-open(Rcmd, ">", "$r_script") or die "Cannot open $r_script \n\n";
-
-print Rcmd "
-	######################################################################
-	# plot power spectra, i.e. wavelet variance by class
-	# add code to create null bands by permuting the original data series
-	# get class of maximum significant variance per feature
-	# generate plots and table matrix of variance including p-values
-	######################################################################
-	library(\"Rwave\");
-	library(\"wavethresh\");
-	library(\"waveslim\");
-
-	options(echo = FALSE)
-
-	# normalize data
-	norm <- function(data){
-		v <- (data-mean(data))/sd(data);
-    	if(sum(is.na(v)) >= 1){
-    		v<-data;
-    	}
-    	return(v);
-	}
-
-	dwt_var_permut_getMax <- function(data, names, filter = 4, bc = \"symmetric\", method = \"kendall\", wf = \"haar\", boundary = \"reflection\") {
-		max_var = NULL;
-    	matrix = NULL;
-		title = NULL;
-    	final_pvalue = NULL;
-		short.levels = NULL;
-		scale = NULL;
-	
-    	print(names);
-    	
-   	 	par(mfcol = c(length(names), length(names)), mar = c(0, 0, 0, 0), oma = c(4, 3, 3, 2), xaxt = \"s\", cex = 1, las = 1);
-   	 	
-    	short.levels <- wd(data[, 1], filter.number = filter, bc = bc)\$nlevels;
-    	
-    	title <- c(\"motif\");
-    	for (i in 1:short.levels){
-    		title <- c(title, paste(i, \"var\", sep = \"_\"), paste(i, \"pval\", sep = \"_\"), paste(i, \"test\", sep = \"_\"));
-    	}
-    	print(title);
-        
-		# normalize the raw data
-    	data<-apply(data,2,norm);
-
-    	for(i in 1:length(names)){
-    		for(j in 1:length(names)){
-				temp = NULL;
-				results = NULL;
-				wave1.dwt = NULL;
-				out = NULL;
-				
-				out <- vector(length = length(title));
-            	temp <- vector(length = short.levels);
-            	
-            	if(i < j) {
-            		plot(temp, type = \"n\", axes = FALSE, xlab = NA, ylab = NA);
-                	box(col = \"grey\"); 
-                	grid(ny = 0, nx = NULL);
-            	} else {
-            		if (i > j){
-                		plot(temp, type = \"n\", axes = FALSE, xlab = NA, ylab = NA);
-                    	box(col = \"grey\"); 
-                    	grid(ny = 0, nx = NULL);
-                 	} else {
-                 	
-                 		wave1.dwt <- dwt(data[, i], wf = wf, short.levels, boundary = boundary); 
-                		
-                		temp_row = (short.levels + 1 ) * -1;
-                		temp_col = 1;
-                    	temp <- wave.variance(wave1.dwt)[temp_row, temp_col];
-
-                    	#permutations code :
-                    	feature1 = NULL;
-						null = NULL;
-						var_25 = NULL;
-						var_975 = NULL;
-						med = NULL;
-
-                    	feature1 = data[, i];
-                    	for (k in 1:1000) {
-							nk_1 = NULL;
-							null.levels = NULL;
-							var = NULL;
-							null_wave1 = NULL;
-
-                        	nk_1 = sample(feature1, length(feature1), replace = FALSE);
-                        	null.levels <- wd(nk_1, filter.number = filter, bc = bc)\$nlevels;
-                        	var <- vector(length = length(null.levels));
-                        	null_wave1 <- dwt(nk_1, wf = wf, short.levels, boundary = boundary);
-                        	var<- wave.variance(null_wave1)[-8, 1];
-                        	null= rbind(null, var);
-                    	}
-                    	null <- apply(null, 2, sort, na.last = TRUE);
-                    	var_25 <- null[25, ];
-                    	var_975 <- null[975, ];
-                    	med <- (apply(null, 2, median, na.rm = TRUE));
-
-                    	# plot
-                    	results <- cbind(temp, var_25, var_975);
-                    	matplot(results, type = \"b\", pch = \"*\", lty = 1, col = c(1, 2, 2), axes = F);
-
-                    	# get pvalues by comparison to null distribution
-                    	out <- (names[i]);
-                    	for (m in 1:length(temp)){
-                    		print(paste(\"scale\", m, sep = \" \"));
-                        	print(paste(\"var\", temp[m], sep = \" \"));
-                        	print(paste(\"med\", med[m], sep = \" \"));
-                        	pv = tail = NULL;
-							out <- c(out, format(temp[m], digits = 3));	
-                        	if (temp[m] >= med[m]){
-                        		# R tail test
-                            	print(\"R\");
-	                        	tail <- \"R\";
-                            	pv <- (length(which(null[, m] >= temp[m])))/(length(na.exclude(null[, m])));
-
-                        	} else {
-                        		if (temp[m] < med[m]){
-                                	# L tail test
-                                	print(\"L\");
-	                            	tail <- \"L\";
-                                	pv <- (length(which(null[, m] <= temp[m])))/(length(na.exclude(null[, m])));
-                        		}
-							}
-							out <- c(out, pv);
-							print(pv);
-							out <- c(out, tail);
-                    	}
-                    	final_pvalue <-rbind(final_pvalue, out);
-                 	
-                 
-                    	# get variances outside null bands by comparing temp to null
-                    	## temp stores variance for each scale, and null stores permuted variances for null bands
-                    	for (n in 1:length(temp)){
-                    		if (temp[n] <= var_975[n]){
-                        		temp[n] <- NA;
-                        	} else {
-                        		temp[n] <- temp[n];
-                        	}
-                    	}
-                    	matrix <- rbind(matrix, temp)
-            		}
-            	}
-	        	# labels
-	        	if (i == 1){
-	        		mtext(names[j], side = 2, line = 0.5, las = 3, cex = 0.25);
-	        	}
-	        	if (j == 1){
-	        		mtext(names[i], side = 3, line = 0.5, cex = 0.25);
-	        	}
-	        	if (j == length(names)){
-	        		axis(1, at = (1:short.levels), las = 3, cex.axis = 0.5);
-	        	}
-    		}
-    	}
-		colnames(final_pvalue) <- title;
-    	#write.table(final_pvalue, file = \"test_final_pvalue.txt\", sep = \"\\t\", quote = FALSE, row.names = FALSE, append = TRUE);
-
-		# get maximum variance larger than expectation by comparison to null bands
-    	varnames <- vector();
-    	for(i in 1:length(names)){
-    		name1 = paste(names[i], \"var\", sep = \"_\")
-        	varnames <- c(varnames, name1)
-    	}
-   		rownames(matrix) <- varnames;
-    	colnames(matrix) <- (1:short.levels);
-    	max_var <- names;
-    	scale <- vector(length = length(names));
-    	for (x in 1:nrow(matrix)){
-        	if (length(which.max(matrix[x, ])) == 0){
-            	scale[x] <- NA;
-        	}
-        	else{
-        		scale[x] <- colnames(matrix)[which.max(matrix[x, ])];
-        	}
-    	}
-    	max_var <- cbind(max_var, scale);
-    	write.table(max_var, file = \"$max_dwt\", sep = \"\\t\", quote = FALSE, row.names = FALSE, append = TRUE);
-    	return(final_pvalue);
-	}\n";
-
-print Rcmd "
-	# execute
-	# read in data 
-	
-	data_test = NULL;
-	data_test <- read.delim(\"$inputFile\");
-	
-	pdf(file = \"$pdf\", width = 11, height = 8);
-	
-	# loop to read and execute on all $count2 subfiles
-	final = NULL;
-	for (x in 1:$count2){
-		sub = NULL;
-		sub_names = NULL;
-		a = NULL;
-		b = NULL;
-		
-    	a = ((x - 1) * 12 + 1);
-    	b = x * 12;
-    
-    	if (x < $count2){
-    		sub <- data_test[, +c(a:b)];
-			sub_names <- colnames(data_test)[a:b];
-			final <- rbind(final, dwt_var_permut_getMax(sub, sub_names));
-    	}
-    	else{
-    		sub <- data_test[, +c(a:ncol(data_test))];
-			sub_names <- colnames(data_test)[a:ncol(data_test)];
-			final <- rbind(final, dwt_var_permut_getMax(sub, sub_names));
-			
-    	}
-	}
-
-	dev.off();
-
-	write.table(final, file = \"$pvalue\", sep = \"\\t\", quote = FALSE, row.names = FALSE);
-
-	#eof\n";
-
-close Rcmd;
-
-system("echo \"wavelet ANOVA started on \`hostname\` at \`date\`\"\n");
-system("R --no-restore --no-save --no-readline < $r_script > $r_script.out");
-system("echo \"wavelet ANOVA ended on \`hostname\` at \`date\`\"\n");
-
-#close the input and output and error files
-close(ERROR);
-close(OUTPUT3);
-close(OUTPUT2);
-close(OUTPUT1);
-close(INPUT);
\ No newline at end of file
--- a/execute_dwt_var_perClass.xml	Thu Jan 23 12:31:07 2014 -0500
+++ b/execute_dwt_var_perClass.xml	Mon Jul 06 18:12:51 2020 +0000
@@ -1,20 +1,38 @@
-<tool id="compute_p-values_max_variances_feature_occurrences_in_one_dataset_using_discrete_wavelet_transfom" name="Compute P-values and Max Variances for Feature Occurrences" version="1.0.0">
+<tool id="compute_p-values_max_variances_feature_occurrences_in_one_dataset_using_discrete_wavelet_transfom" name="Compute P-values and Max Variances for Feature Occurrences" version="1.0.1">
   <description>in one dataset using Discrete Wavelet Transfoms</description>
-  
-  <command interpreter="perl">
-  	execute_dwt_var_perClass.pl $inputFile $outputFile1 $outputFile2 $outputFile3
+  <requirements>
+    <requirement type="package" version="1.7.5">r-waveslim</requirement>
+    <requirement type="package" version="4.6.8">r-wavethresh</requirement>
+  </requirements>
+  <command detect_errors="exit_code">
+      Rscript --vanilla '$__tool_directory__/execute_dwt_var_perClass.R'
+      '$inputFile'
+      '$outputFile1'
+      '$outputFile2'
+      '$outputFile3'
   </command>
-  
   <inputs>
-  	<param format="tabular" name="inputFile" type="data" label="Select the input file"/>	
+    <param format="tabular" name="inputFile" type="data" label="Select the input file"/>
   </inputs>
-  
   <outputs>
-    <data format="tabular" name="outputFile1"/> 
-    <data format="tabular" name="outputFile2"/>
-    <data format="pdf" name="outputFile3"/>
+    <data format="tabular" name="outputFile1" label="${tool.name} on ${on_string}: scales"/> 
+    <data format="tabular" name="outputFile2" label="${tool.name} on ${on_string}: statistics"/>
+    <data format="pdf" name="outputFile3" label="${tool.name} on ${on_string}: pdf"/>
   </outputs>
-  	
+  <tests>
+    <test>
+      <param ftype="tabular" name="inputFile" value="in.tsv"/>
+      <output name="outputFile1" ftype="tabular">
+        <assert_contents><has_line_matching expression="^max_var\tscale.*"/></assert_contents>
+        <assert_contents><has_line_matching expression="^translinTarget.*" /></assert_contents>
+      </output>
+      <output name="outputFile2" ftype="tabular">
+        <assert_contents><has_line_matching expression="^motif\t1_var.*"/></assert_contents>
+        <assert_contents><has_line_matching expression="^translinTarget.*" /></assert_contents>
+      </output>
+      <output name="outputFile3" ftype="pdf" file="out.pdf" compare="sim_size"/>
+    </test>
+  </tests>
   <help> 
 
 .. class:: infomark
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/in.tsv	Mon Jul 06 18:12:51 2020 +0000
@@ -0,0 +1,17 @@
+deletionHoptspot	insertionHoptspot	dnaPolPauseFrameshift	indelHotspot	topoisomeraseCleavageSite	translinTarget	vDjRecombinationSignal	x-likeSite
+226	403	416	221	1165	832	749	1056
+236	444	380	241	1223	746	782	1207
+242	496	391	195	1116	643	770	1219
+243	429	364	191	1118	694	783	1223
+244	410	371	236	1063	692	805	1233
+230	386	370	217	1087	657	787	1215
+275	404	402	214	1044	697	831	1188
+265	443	365	231	1086	694	782	1184
+255	390	354	246	1114	642	773	1176
+281	384	406	232	1102	719	787	1191
+263	459	369	251	1135	643	810	1215
+280	433	400	251	1159	701	777	1151
+278	385	382	231	1147	697	707	1161
+248	393	389	211	1162	723	759	1183
+251	403	385	246	1114	752	776	1153
+239	383	347	227	1172	759	789	1141
Binary file test-data/out.pdf has changed