diff execute_dwt_cor_aVa_perClass.pl @ 0:6708501767b6 draft

Imported from capsule None

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/execute_dwt_cor_aVa_perClass.pl	Mon Jan 27 09:29:25 2014 -0500
@@ -0,0 +1,221 @@
+#!/usr/bin/perl -w
+
+use warnings;
+use IO::Handle;
+
+$usage = "execute_dwt_cor_aVa_perClass.pl [TABULAR.in] [TABULAR.in] [TABULAR.out] [PDF.out]  \n";
+die $usage unless @ARGV == 4;
+
+#get the input arguments
+my $firstInputFile = $ARGV[0];
+my $secondInputFile = $ARGV[1];
+my $firstOutputFile = $ARGV[2];
+my $secondOutputFile = $ARGV[3];
+
+open (INPUT1, "<", $firstInputFile) || die("Could not open file $firstInputFile \n");
+open (INPUT2, "<", $secondInputFile) || die("Could not open file $secondInputFile \n");
+open (OUTPUT1, ">", $firstOutputFile) || die("Could not open file $firstOutputFile \n");
+open (OUTPUT2, ">", $secondOutputFile) || die("Could not open file $secondOutputFile \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");
+
+print "There are two input data files: \n";
+print "The input data file is: $firstInputFile \n";
+print "The control data file is: $secondInputFile \n";
+
+# IvC test
+$test = "cor_aVa";
+
+# construct an R script to implement the IvC test
+print "\n";
+
+$r_script = "get_dwt_cor_aVa_test.r"; 
+print "$r_script \n";
+
+open(Rcmd, ">", "$r_script") or die "Cannot open $r_script \n\n";
+print Rcmd "
+	##################################################################################
+	# code to do all correlation tests of form: motif(a) vs. motif(a)
+	# add code to create null bands by permuting the original data series
+	# generate plots and table matrix of correlation coefficients 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_cor <- function(data.short, names.short, data.long, names.long, test, pdf, table, filter = 4, bc = \"symmetric\", method = \"kendall\", wf = \"haar\", boundary = \"reflection\") {
+		print(test);
+	    print(pdf);
+		print(table);
+		
+	    pdf(file = pdf);   
+	    final_pvalue = NULL;
+		title = NULL;
+		
+	    short.levels <- wd(data.short[, 1], filter.number = filter, bc = bc)\$nlevels;
+		title <- c(\"motif\");
+        for (i in 1:short.levels){
+	        title <- c(title, paste(i, \"cor\", sep = \"_\"), paste(i, \"pval\", sep = \"_\"));
+        }
+        print(title);
+	
+        # normalize the raw data
+        data.short <- apply(data.short, 2, norm);
+        data.long <- apply(data.long, 2, norm);
+        
+        for(i in 1:length(names.short)){
+        	# Kendall Tau
+            # DWT wavelet correlation function
+            # include significance to compare
+            wave1.dwt = wave2.dwt = NULL;
+            tau.dwt = NULL;
+            out = NULL;
+
+            print(names.short[i]);
+            print(names.long[i]);
+            
+            # need exit if not comparing motif(a) vs motif(a)
+            if (names.short[i] != names.long[i]){
+            	stop(paste(\"motif\", names.short[i], \"is not the same as\", names.long[i], sep = \" \"));
+            }
+            else {
+            	wave1.dwt <- dwt(data.short[, i], wf = wf, short.levels, boundary = boundary);
+                wave2.dwt <- dwt(data.long[, i], wf = wf, short.levels, boundary = boundary);
+                tau.dwt <- vector(length=short.levels)
+                       
+				#perform cor test on wavelet coefficients per scale 
+				for(level in 1:short.levels){
+                	w1_level = w2_level = NULL;
+                    w1_level <- (wave1.dwt[[level]]);
+                    w2_level <- (wave2.dwt[[level]]);
+                    tau.dwt[level] <- cor.test(w1_level, w2_level, method = method)\$estimate;
+                }
+                
+                # CI bands by permutation of time series
+                feature1 = feature2 = NULL;
+                feature1 = data.short[, i];
+                feature2 = data.long[, i];
+                null = results = med = NULL; 
+                cor_25 = cor_975 = NULL;
+                
+                for (k in 1:1000) {
+                	nk_1 = nk_2 = NULL;
+                    null.levels = NULL;
+                    cor = NULL;
+                    null_wave1 = null_wave2 = NULL;
+                    
+                    nk_1 <- sample(feature1, length(feature1), replace = FALSE);
+                    nk_2 <- sample(feature2, length(feature2), replace = FALSE);
+                    null.levels <- wd(nk_1, filter.number = filter, bc = bc)\$nlevels;
+                    cor <- vector(length = null.levels);
+                    null_wave1 <- dwt(nk_1, wf = wf, short.levels, boundary = boundary);
+                    null_wave2 <- dwt(nk_2, wf = wf, short.levels, boundary = boundary);
+
+                    for(level in 1:null.levels){
+                    	null_level1 = null_level2 = NULL;
+                        null_level1 <- (null_wave1[[level]]);
+                        null_level2 <- (null_wave2[[level]]);
+                        cor[level] <- cor.test(null_level1, null_level2, method = method)\$estimate;
+                    }
+                    null = rbind(null, cor);
+                }
+                
+                null <- apply(null, 2, sort, na.last = TRUE);
+                print(paste(\"NAs\", length(which(is.na(null))), sep = \" \"));
+                cor_25 <- null[25,];
+                cor_975 <- null[975,];
+                med <- (apply(null, 2, median, na.rm = TRUE));
+
+				# plot
+                results <- cbind(tau.dwt, cor_25, cor_975);
+                matplot(results, type = \"b\", pch = \"*\" , lty = 1, col = c(1, 2, 2), ylim = c(-1, 1), xlab = \"Wavelet Scale\", ylab = \"Wavelet Correlation Kendall's Tau\", main = (paste(test, names.short[i], sep = \" \")), cex.main = 0.75);
+                abline(h = 0);
+
+                # get pvalues by comparison to null distribution
+ 			    ### modify pval calculation for error type II of T test ####
+                out <- (names.short[i]);
+                for (m in 1:length(tau.dwt)){
+                	print(paste(\"scale\", m, sep = \" \"));
+                    print(paste(\"tau\", tau.dwt[m], sep = \" \"));
+                    print(paste(\"med\", med[m], sep = \" \"));
+					out <- c(out, format(tau.dwt[m], digits = 3));	
+                    pv = NULL;
+                    if(is.na(tau.dwt[m])){
+                    	pv <- \"NA\"; 
+                    } 
+                    else {
+                    	if (tau.dwt[m] >= med[m]){
+                        	# R tail test
+                            print(paste(\"R\"));
+                            ### per sv ok to use inequality not strict
+                            pv <- (length(which(null[, m] >= tau.dwt[m])))/(length(na.exclude(null[, m])));
+                            if (tau.dwt[m] == med[m]){
+								print(\"tau == med\");
+                                print(summary(null[, m]));
+                            }
+                    	}
+                        else if (tau.dwt[m] < med[m]){
+                        	# L tail test
+                            print(paste(\"L\"));
+                            pv <- (length(which(null[, m] <= tau.dwt[m])))/(length(na.exclude(null[, m])));
+                        }
+					}
+					out <- c(out, pv);
+                    print(paste(\"pval\", pv, sep = \" \"));
+                }
+                final_pvalue <- rbind(final_pvalue, out);
+				print(out);
+        	}
+        }
+        colnames(final_pvalue) <- title;
+        write.table(final_pvalue, file = table, sep = \"\\t\", quote = FALSE, row.names = FALSE)
+        dev.off();
+	}\n";
+
+print Rcmd "
+	# execute
+	# read in data 
+		
+	inputData1 = inputData2 = NULL;
+	inputData.short1 = inputData.short2 = NULL;
+	inputDataNames.short1 = inputDataNames.short2 = NULL;
+		
+	inputData1 <- read.delim(\"$firstInputFile\");
+	inputData.short1 <- inputData1[, +c(1:ncol(inputData1))];
+	inputDataNames.short1 <- colnames(inputData.short1);
+		
+	inputData2 <- read.delim(\"$secondInputFile\");
+	inputData.short2 <- inputData2[, +c(1:ncol(inputData2))];
+	inputDataNames.short2 <- colnames(inputData.short2);
+	
+	# cor test for motif(a) in inputData1 vs motif(a) in inputData2
+	dwt_cor(inputData.short1, inputDataNames.short1, inputData.short2, inputDataNames.short2, test = \"$test\", pdf = \"$secondOutputFile\", table = \"$firstOutputFile\");
+	print (\"done with the correlation test\");
+	
+	#eof\n";
+close Rcmd;
+
+system("echo \"wavelet IvC test started on \`hostname\` at \`date\`\"\n");
+system("R --no-restore --no-save --no-readline < $r_script > $r_script.out\n");
+system("echo \"wavelet IvC test ended on \`hostname\` at \`date\`\"\n");
+
+#close the input and output and error files
+close(ERROR);
+close(OUTPUT2);
+close(OUTPUT1);
+close(INPUT2);
+close(INPUT1);
+