/tools/discreteWavelet/execute_dwt_IvC_all.pl
https://bitbucket.org/cistrome/cistrome-harvard/ · Perl · 210 lines · 188 code · 15 blank · 7 comment · 5 complexity · d6f92d43152dc5ebe684296c4b3065cc MD5 · raw file
- #!/usr/bin/perl -w
- use warnings;
- use IO::Handle;
- $usage = "execute_dwt_IvC_all.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 = "IvC";
- # construct an R script to implement the IvC test
- print "\n";
- $r_script = "get_dwt_IvC_test.r";
- print "$r_script \n";
- # R script
- open(Rcmd, ">", "$r_script") or die "Cannot open $r_script \n\n";
- print Rcmd "
- ###########################################################################################
- # code to do wavelet Indel vs. Control
- # signal is the difference I-C; function is second moment i.e. variance from zero not mean
- # to perform wavelet transf. of signal, scale-by-scale analysis of the function
- # 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\", 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, \"moment2\", sep = \"_\"), paste(i, \"pval\", sep = \"_\"), paste(i, \"test\", sep = \"_\"));
- }
- print(title);
-
- # loop to compare a vs a
- for(i in 1:length(names.short)){
- wave1.dwt = NULL;
- m2.dwt = diff = var.dwt = NULL;
- out = NULL;
- out <- vector(length = length(title));
-
- 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 {
- # signal is the difference I-C data sets
- diff<-data.short[,i]-data.long[,i];
-
- # normalize the signal
- diff<-norm(diff);
-
- # function is 2nd moment
- # 2nd moment m_j = 1/N[sum_N(W_j + V_J)^2] = 1/N sum_N(W_j)^2 + (X_bar)^2
- wave1.dwt <- dwt(diff, wf = wf, short.levels, boundary = boundary);
- var.dwt <- wave.variance(wave1.dwt);
- m2.dwt <- vector(length = short.levels)
- for(level in 1:short.levels){
- m2.dwt[level] <- var.dwt[level, 1] + (mean(diff)^2);
- }
-
- # CI bands by permutation of time series
- feature1 = feature2 = NULL;
- feature1 = data.short[, i];
- feature2 = data.long[, i];
- null = results = med = NULL;
- m2_25 = m2_975 = NULL;
-
- for (k in 1:1000) {
- nk_1 = nk_2 = NULL;
- m2_null = var_null = NULL;
- null.levels = null_wave1 = null_diff = 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;
- null_diff <- nk_1-nk_2;
- null_diff <- norm(null_diff);
- null_wave1 <- dwt(null_diff, wf = wf, short.levels, boundary = boundary);
- var_null <- wave.variance(null_wave1);
- m2_null <- vector(length = null.levels);
- for(level in 1:null.levels){
- m2_null[level] <- var_null[level, 1] + (mean(null_diff)^2);
- }
- null= rbind(null, m2_null);
- }
-
- null <- apply(null, 2, sort, na.last = TRUE);
- m2_25 <- null[25,];
- m2_975 <- null[975,];
- med <- apply(null, 2, median, na.rm = TRUE);
- # plot
- results <- cbind(m2.dwt, m2_25, m2_975);
- matplot(results, type = \"b\", pch = \"*\", lty = 1, col = c(1, 2, 2), xlab = \"Wavelet Scale\", ylab = c(\"Wavelet 2nd Moment\", test), main = (names.short[i]), cex.main = 0.75);
- abline(h = 1);
- # get pvalues by comparison to null distribution
- out <- c(names.short[i]);
- for (m in 1:length(m2.dwt)){
- print(paste(\"scale\", m, sep = \" \"));
- print(paste(\"m2\", m2.dwt[m], sep = \" \"));
- print(paste(\"median\", med[m], sep = \" \"));
- out <- c(out, format(m2.dwt[m], digits = 4));
- pv = NULL;
- if(is.na(m2.dwt[m])){
- pv <- \"NA\";
- }
- else {
- if (m2.dwt[m] >= med[m]){
- # R tail test
- tail <- \"R\";
- pv <- (length(which(null[, m] >= m2.dwt[m])))/(length(na.exclude(null[, m])));
- }
- else{
- if (m2.dwt[m] < med[m]){
- # L tail test
- tail <- \"L\";
- pv <- (length(which(null[, m] <= m2.dwt[m])))/(length(na.exclude(null[, m])));
- }
- }
- }
- out <- c(out, pv);
- print(pv);
- out <- c(out, tail);
- }
- 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
-
- inputData <- read.delim(\"$firstInputFile\");
- inputDataNames <- colnames(inputData);
-
- controlData <- read.delim(\"$secondInputFile\");
- controlDataNames <- colnames(controlData);
-
- # call the test function to implement IvC test
- dwt_cor(inputData, inputDataNames, controlData, controlDataNames, test = \"$test\", pdf = \"$secondOutputFile\", table = \"$firstOutputFile\");
- print (\"done with the correlation test\");
- \n";
- print Rcmd "#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);