Mercurial > repos > devteam > dwt_ivc_all
changeset 0:91fad0f30fd3 draft
Imported from capsule None
| author | devteam |
|---|---|
| date | Thu, 23 Jan 2014 12:31:01 -0500 |
| parents | |
| children | 509993d9fdca |
| files | execute_dwt_IvC_all.pl execute_dwt_IvC_all.xml |
| diffstat | 2 files changed, 322 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/execute_dwt_IvC_all.pl Thu Jan 23 12:31:01 2014 -0500 @@ -0,0 +1,210 @@ +#!/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); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/execute_dwt_IvC_all.xml Thu Jan 23 12:31:01 2014 -0500 @@ -0,0 +1,112 @@ +<tool id="compute_p-values_second_moments_feature_occurrences_between_two_datasets_using_discrete_wavelet_transfom" name="Compute P-values and Second Moments for Feature Occurrences" version="1.0.0"> + <description>between two datasets using Discrete Wavelet Transfoms</description> + + <command interpreter="perl"> + execute_dwt_IvC_all.pl $inputFile1 $inputFile2 $outputFile1 $outputFile2 + </command> + + <inputs> + <param format="tabular" name="inputFile1" type="data" label="Select the first input file"/> + <param format="tabular" name="inputFile2" type="data" label="Select the second input file"/> + </inputs> + + <outputs> + <data format="tabular" name="outputFile1"/> + <data format="pdf" name="outputFile2"/> + </outputs> + + <help> + +.. class:: infomark + +**What it does** + +This program generates plots and computes table matrix of second moments, p-values, and test orientations at multiple scales for the correlation between the occurrences of features in one dataset and their occurrences in another using multiscale wavelet analysis technique. + +The program assumes that the user has two sets of DNA sequences, S1 and S1, each of which consists of one or more sequences of equal length. Each sequence in each set is divided into the same number of multiple intervals n such that n = 2^k, where k is a positive integer and k >= 1. Thus, n could be any value of the set {2, 4, 8, 16, 32, 64, 128, ...}. k represents the number of scales. + +The program has two input files obtained as follows: + +For a given set of features, say motifs, the user counts the number of occurrences of each feature in each interval of each sequence in S1 and S1, and builds two tabular files representing the count results in each interval of S1 and S1. These are the input files of the program. + +The program gives two output files: + +- The first output file is a TABULAR format file representing the second moments, p-values, and test orientations for each feature at each scale. +- The second output file is a PDF file consisting of as many figures as the number of features, such that each figure represents the values of the second moment for that feature at every scale. + +----- + +.. class:: warningmark + +**Note** + +In order to obtain empirical p-values, a random perumtation test is implemented by the program, which results in the fact that the program gives slightly different results each time it is run on the same input file. + +----- + +**Example** + +Counting the occurrences of 5 features (motifs) in 16 intervals (one line per interval) of the DNA sequences in S1 gives the following tabular file:: + + deletionHoptspot insertionHoptspot dnaPolPauseFrameshift topoisomeraseCleavageSite translinTarget + 226 403 416 221 1165 + 236 444 380 241 1223 + 242 496 391 195 1116 + 243 429 364 191 1118 + 244 410 371 236 1063 + 230 386 370 217 1087 + 275 404 402 214 1044 + 265 443 365 231 1086 + 255 390 354 246 1114 + 281 384 406 232 1102 + 263 459 369 251 1135 + 280 433 400 251 1159 + 278 385 382 231 1147 + 248 393 389 211 1162 + 251 403 385 246 1114 + 239 383 347 227 1172 + +And counting the occurrences of 5 features (motifs) in 16 intervals (one line per interval) of the DNA sequences in S2 gives the following tabular file:: + + deletionHoptspot insertionHoptspot dnaPolPauseFrameshift topoisomeraseCleavageSite translinTarget + 235 374 407 257 1159 + 244 356 353 212 1128 + 233 343 322 204 1110 + 222 329 398 253 1054 + 216 325 328 253 1129 + 257 368 352 221 1115 + 238 360 346 224 1102 + 225 350 377 248 1107 + 230 330 365 236 1132 + 241 389 357 220 1120 + 274 354 392 235 1120 + 250 379 354 210 1102 + 254 329 320 251 1080 + 221 355 406 279 1127 + 224 330 390 249 1129 + 246 366 364 218 1176 + + +We notice that the number of scales here is 4 because 16 = 2^4. Runnig the program on the above input files gives the following output: + +The first output file:: + + motif 1_moment2 1_pval 1_test 2_moment2 2_pval 2_test 3_moment2 3_pval 3_test 4_moment2 4_pval 4_test + + deletionHoptspot 0.8751 0.376 L 1.549 0.168 R 0.6152 0.434 L 0.5735 0.488 R + insertionHoptspot 0.902 0.396 L 1.172 0.332 R 0.6843 0.456 L 1.728 0.213 R + dnaPolPauseFrameshift 1.65 0.013 R 0.267 0.055 L 0.1387 0.124 L 0.4516 0.498 L + topoisomeraseCleavageSite 0.7443 0.233 L 1.023 0.432 R 1.933 0.155 R 1.09 0.3 R + translinTarget 0.5084 0.057 L 0.8219 0.446 L 3.604 0.019 R 0.4377 0.492 L + +The second output file: + +.. image:: ${static_path}/operation_icons/dwt_IvC_1.png +.. image:: ${static_path}/operation_icons/dwt_IvC_2.png +.. image:: ${static_path}/operation_icons/dwt_IvC_3.png +.. image:: ${static_path}/operation_icons/dwt_IvC_4.png +.. image:: ${static_path}/operation_icons/dwt_IvC_5.png + + </help> + +</tool>