root/galaxy-central/tools/discreteWavelet/execute_dwt_var_perClass.pl @ 2

#!/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);