# HG changeset patch # User devteam # Date 1406562088 14400 # Node ID 423bcc3a3785475706a1053923c95a59973af29c # Parent 2a8638f0c47e640bc472a3034b8fd8ce910ef8ba Uploaded correct tarball. diff -r 2a8638f0c47e -r 423bcc3a3785 MINE.xml --- a/MINE.xml Mon Jul 28 11:30:31 2014 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,82 +0,0 @@ - - - Maximal Information-based Nonparametric Exploration - - MINE - - mine_wrapper.py - --jar "\${JAVA_JAR_PATH}/MINE.jar" - - --infile "${input_file}" - - #if str( $master_variable_type.master_variable_type_selector ) in [ 'allPairs', 'adjacentPairs' ]: - --master_variable "${master_variable_type.master_variable_type_selector}" - #else: - --master_variable "${master_variable_type.master_variable}" - #end if - - --cv "${cv}" - - --exp "${exp}" - - --c "${c}" - - ##--gc ##skip - - - #if str( $master_variable_type.master_variable_type_selector ) != 'allPairs' and $master_variable_type.permute: - --permute - #end if - - --output_results "${output_results}" - - --output_log "${output_log}" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -**What it does** - -Applies the Maximal Information-based Nonparametric Exploration strategy to an input dataset. - -See http://www.exploredata.net/ for more information. - ------- - -**Citation** - -For the underlying tool, please cite `David N. Reshef, Yakir A. Reshef, Hilary K. Finucane5, Sharon R. Grossman, Gilean McVean, Peter J. Turnbaugh, Eric S. Lander, Michael Mitzenmacher, Pardis C. Sabeti Detecting Novel Associations in Large Data Sets. Science. 2011 Dec. <http://www.sciencemag.org/content/334/6062/1518>`_ - -If you use this tool in Galaxy, please cite Blankenberg D, et al. *In preparation.* - - - diff -r 2a8638f0c47e -r 423bcc3a3785 lda_analy.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/lda_analy.xml Mon Jul 28 11:41:28 2014 -0400 @@ -0,0 +1,288 @@ + + Linear Discriminant Analysis + + R + + r_wrapper.sh $script_file + + + + + + + + + + + + + + + + + + + + + + + + rm(list = objects() ) + + ############# FORMAT X DATA ######################### + format<-function(data) { + ind=NULL + for(i in 1 : ncol(data)){ + if (is.na(data[nrow(data),i])) { + ind<-c(ind,i) + } + } + #print(is.null(ind)) + if (!is.null(ind)) { + data<-data[,-c(ind)] + } + + data + } + + ########GET RESPONSES ############################### + get_resp<- function(data) { + resp1<-as.vector(data[,ncol(data)]) + resp=numeric(length(resp1)) + for (i in 1:length(resp1)) { + if (resp1[i]=="Y ") { + resp[i] = 0 + } + if (resp1[i]=="X ") { + resp[i] = 1 + } + } + return(resp) + } + + ######## CHARS TO NUMBERS ########################### + f_to_numbers<- function(F) { + ind<-NULL + G<-matrix(0,nrow(F), ncol(F)) + for (i in 1:nrow(F)) { + for (j in 1:ncol(F)) { + G[i,j]<-as.integer(F[i,j]) + } + } + return(G) + } + + ###################NORMALIZING######################### + norm <- function(M, a=NULL, b=NULL) { + C<-NULL + ind<-NULL + + for (i in 1: ncol(M)) { + if (sd(M[,i])!=0) { + M[,i]<-(M[,i]-mean(M[,i]))/sd(M[,i]) + } + # else {print(mean(M[,i]))} + } + return(M) + } + + ##### LDA DIRECTIONS ################################# + lda_dec <- function(data, k){ + priors=numeric(k) + grandmean<-numeric(ncol(data)-1) + means=matrix(0,k,ncol(data)-1) + B = matrix(0, ncol(data)-1, ncol(data)-1) + N=nrow(data) + for (i in 1:k){ + priors[i]=sum(data[,1]==i)/N + grp=subset(data,data\$group==i) + means[i,]=mean(grp[,2:ncol(data)]) + #print(means[i,]) + #print(priors[i]) + #print(priors[i]*means[i,]) + grandmean = priors[i]*means[i,] + grandmean + } + + for (i in 1:k) { + B= B + priors[i]*((means[i,]-grandmean)%*%t(means[i,]-grandmean)) + } + + W = var(data[,2:ncol(data)]) + svdW = svd(W) + inv_sqrtW =solve(svdW\$v %*% diag(sqrt(svdW\$d)) %*% t(svdW\$v)) + B_star= t(inv_sqrtW)%*%B%*%inv_sqrtW + B_star_decomp = svd(B_star) + directions = inv_sqrtW%*%B_star_decomp\$v + return( list(directions, B_star_decomp\$d) ) + } + + ################ NAIVE BAYES FOR 1D SIR OR LDA ############## + naive_bayes_classifier <- function(resp, tr_data, test_data, k=2, tau) { + tr_data=data.frame(resp=resp, dir=tr_data) + means=numeric(k) + #print(k) + cl=numeric(k) + predclass=numeric(length(test_data)) + for (i in 1:k) { + grp = subset(tr_data, resp==i) + means[i] = mean(grp\$dir) + #print(i, means[i]) + } + cutoff = tau*means[1]+(1-tau)*means[2] + #print(tau) + #print(means) + #print(cutoff) + if (cutoff>means[1]) { + cl[1]=1 + cl[2]=2 + } + else { + cl[1]=2 + cl[2]=1 + } + + for (i in 1:length(test_data)) { + + if (test_data[i] <= cutoff) { + predclass[i] = cl[1] + } + else { + predclass[i] = cl[2] + } + } + #print(means) + #print(mean(means)) + #X11() + #plot(test_data,pch=predclass, col=resp) + predclass + } + + ################# EXTENDED ERROR RATES ################# + ext_error_rate <- function(predclass, actualclass,msg=c("you forgot the message"), pr=1) { + er=sum(predclass != actualclass)/length(predclass) + + matr<-data.frame(predclass=predclass,actualclass=actualclass) + escapes = subset(matr, actualclass==1) + subjects = subset(matr, actualclass==2) + er_esc=sum(escapes\$predclass != escapes\$actualclass)/length(escapes\$predclass) + er_subj=sum(subjects\$predclass != subjects\$actualclass)/length(subjects\$predclass) + + if (pr==1) { + # print(paste(c(msg, 'overall : ', (1-er)*100, "%."),collapse=" ")) + # print(paste(c(msg, 'within escapes : ', (1-er_esc)*100, "%."),collapse=" ")) + # print(paste(c(msg, 'within subjects: ', (1-er_subj)*100, "%."),collapse=" ")) + } + return(c((1-er)*100, (1-er_esc)*100, (1-er_subj)*100)) + } + + ## Main Function ## + + files<-matrix("${input}", 1,1, byrow=T) + + d<-"${cond}" # Number of PC + + tau<-seq(0,1, by=0.005) + #tau<-seq(0,1, by=0.1) + for_curve=matrix(-10, 3,length(tau)) + + ############################################################## + + test_data_whole_X <-read.delim(files[1,1], row.names=1) + + #### FORMAT TRAINING DATA #################################### + # get only necessary columns + + test_data_whole_X<-format(test_data_whole_X) + oligo_labels<-test_data_whole_X[1:(nrow(test_data_whole_X)-1),ncol(test_data_whole_X)] + test_data_whole_X<-test_data_whole_X[,1:(ncol(test_data_whole_X)-1)] + + X_names<-colnames(test_data_whole_X)[1:ncol(test_data_whole_X)] + test_data_whole_X<-t(test_data_whole_X) + resp<-get_resp(test_data_whole_X) + ldaqda_resp = resp + 1 + a<-sum(resp) # Number of Subject + b<-length(resp) - a # Number of Escape + ## FREQUENCIES ################################################# + F<-test_data_whole_X[,1:(ncol(test_data_whole_X)-1)] + F<-f_to_numbers(F) + FN<-norm(F, a, b) + ss<-svd(FN) + eigvar<-NULL + eig<-ss\$d^2 + + for ( i in 1:length(ss\$d)) { + eigvar[i]<-sum(eig[1:i])/sum(eig) + } + + #print(paste(c("Variance explained : ", eigvar[d]*100, "%"), collapse="")) + + Z<-F%*%ss\$v + + ldaqda_data <- data.frame(group=ldaqda_resp,Z[,1:d]) + lda_dir<-lda_dec(ldaqda_data,2) + train_lda_pred <-Z[,1:d]%*%lda_dir[[1]] + + ############# NAIVE BAYES CROSS-VALIDATION ############# + ### LDA ##### + + y<-ldaqda_resp + X<-F + cv<-matrix(c(rep('NA',nrow(test_data_whole_X))), nrow(test_data_whole_X), length(tau)) + for (i in 1:nrow(test_data_whole_X)) { + # print(i) + resp<-y[-i] + p<-matrix(X[-i,], dim(X)[1]-1, dim(X)[2]) + testdata<-matrix(X[i,],1,dim(X)[2]) + p1<-norm(p) + sss<-svd(p1) + pred<-(p%*%sss\$v)[,1:d] + test<- (testdata%*%sss\$v)[,1:d] + lda <- lda_dec(data.frame(group=resp,pred),2) + pred <- pred[,1:d]%*%lda[[1]][,1] + test <- test%*%lda[[1]][,1] + test<-matrix(test, 1, length(test)) + for (t in 1:length(tau)) { + cv[i, t] <- naive_bayes_classifier (resp, pred, test,k=2, tau[t]) + } + } + + for (t in 1:length(tau)) { + tr_err<-ext_error_rate(cv[,t], ldaqda_resp , c("CV"), 1) + for_curve[1:3,t]<-tr_err + } + + dput(for_curve, file="${output}") + + + + + + + +.. class:: infomark + +**TIP:** If you want to perform Principal Component Analysis (PCA) on the give numeric input data (which corresponds to the "Source file First in "Generate A Matrix" tool), please use *Multivariate Analysis/Principal Component Analysis* + +----- + +.. class:: infomark + +**What it does** + +This tool consists of the module to perform the Linear Discriminant Analysis as described in Carrel et al., 2006 (PMID: 17009873) + +*Carrel L, Park C, Tyekucheva S, Dunn J, Chiaromonte F, et al. (2006) Genomic Environment Predicts Expression Patterns on the Human Inactive X Chromosome. PLoS Genet 2(9): e151. doi:10.1371/journal.pgen.0020151* + +----- + +.. class:: warningmark + +**Note** + +- Output from "Generate A Matrix" tool is used as input file for this tool +- Output of this tool contains LDA classification success rates for different values of the turning parameter tau (from 0 to 1 with 0.005 interval). This output file will be used to establish the ROC plot, and you can obtain more detail information from this plot. + + + + + diff -r 2a8638f0c47e -r 423bcc3a3785 mine_wrapper.py --- a/mine_wrapper.py Mon Jul 28 11:30:31 2014 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,85 +0,0 @@ -#!/usr/bin/env python -#Dan Blankenberg - -""" -A wrapper script for running the MINE.jar commands. -""" - -import sys, optparse, os, tempfile, subprocess, shutil - -CHUNK_SIZE = 2**20 #1mb - -BASE_NAME = "galaxy_mime_file.txt" -JOB_ID = "galaxy_mine" - -def cleanup_before_exit( tmp_dir ): - if tmp_dir and os.path.exists( tmp_dir ): - print os.listdir( tmp_dir ) - shutil.rmtree( tmp_dir ) - -def open_file_from_option( filename, mode = 'rb' ): - if filename: - return open( filename, mode = mode ) - return None - - -def __main__(): - #Parse Command Line - parser = optparse.OptionParser() - parser.add_option( '-j', '--jar', dest='jar', action='store', type="string", help='Location of JAR file' ) - parser.add_option( '-i', '--infile', dest='infile', action='store', type="string", help='infile' ) - parser.add_option( '-m', '--master_variable', dest='master_variable', action='store', type="string", help='master_variable' ) - parser.add_option( '-v', '--cv', dest='cv', action='store', type="string", help='cv' ) - parser.add_option( '-e', '--exp', dest='exp', action='store', type="string", help='exp' ) - parser.add_option( '-c', '--c', dest='c', action='store', type="string", help='c' ) - parser.add_option( '-p', '--permute', dest='permute', action='store_true', default=False, help='permute' ) - parser.add_option( '-o', '--output_results', dest='output_results', action='store', type="string", help='output_results' ) - parser.add_option( '-l', '--output_log', dest='output_log', action='store', type="string", help='output_log' ) - parser.add_option( '', '--stdout', dest='stdout', action='store', type="string", default=None, help='If specified, the output of stdout will be written to this file.' ) - parser.add_option( '', '--stderr', dest='stderr', action='store', type="string", default=None, help='If specified, the output of stderr will be written to this file.' ) - (options, args) = parser.parse_args() - - tmp_dir = tempfile.mkdtemp( prefix='tmp-MINE-' ) - tmp_input_name = os.path.join( tmp_dir, BASE_NAME ) - if options.permute: - permute = "-permute" - else: - permute = "" - - os.symlink( options.infile, tmp_input_name ) - - cmd = 'java -jar "%s" "%s" %s -cv%s -exp%s -c%s %s "%s"' % ( options.jar, tmp_input_name, options.master_variable, options.cv, options.exp, options.c, permute, JOB_ID ) - print cmd - - #set up stdout and stderr output options - stdout = open_file_from_option( options.stdout, mode = 'wb' ) - stderr = open_file_from_option( options.stderr, mode = 'wb' ) - #if no stderr file is specified, we'll use our own - if stderr is None: - stderr = tempfile.NamedTemporaryFile( prefix="MINE-stderr-", dir=tmp_dir ) - - proc = subprocess.Popen( args=cmd, stdout=stdout, stderr=stderr, shell=True, cwd=tmp_dir ) - return_code = proc.wait() - - if return_code: - stderr_target = sys.stderr - else: - stderr_target = sys.stdout - stderr.flush() - stderr.seek(0) - while True: - chunk = stderr.read( CHUNK_SIZE ) - if chunk: - stderr_target.write( chunk ) - else: - break - stderr.close() - - print os.listdir( tmp_dir ) - - shutil.move( '%s,%s,Results.csv' % ( tmp_input_name, JOB_ID ), options.output_results ) - shutil.move( '%s,%s,Status.csv' % ( tmp_input_name, JOB_ID ), options.output_log ) - - cleanup_before_exit( tmp_dir ) - -if __name__=="__main__": __main__() diff -r 2a8638f0c47e -r 423bcc3a3785 r_wrapper.sh --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/r_wrapper.sh Mon Jul 28 11:41:28 2014 -0400 @@ -0,0 +1,23 @@ +#!/bin/sh + +### Run R providing the R script in $1 as standard input and passing +### the remaining arguments on the command line + +# Function that writes a message to stderr and exits +fail() +{ + echo "$@" >&2 + exit 1 +} + +# Ensure R executable is found +which R > /dev/null || fail "'R' is required by this tool but was not found on path" + +# Extract first argument +infile=$1; shift + +# Ensure the file exists +test -f $infile || fail "R input file '$infile' does not exist" + +# Invoke R passing file named by first argument to stdin +R --vanilla --slave $* < $infile diff -r 2a8638f0c47e -r 423bcc3a3785 test-data/lda_analy_output.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/lda_analy_output.txt Mon Jul 28 11:41:28 2014 -0400 @@ -0,0 +1,134 @@ +structure(c(37.9310344827586, 23.6363636363636, 62.5, 37.9310344827586, +23.6363636363636, 62.5, 37.9310344827586, 23.6363636363636, 62.5, +37.9310344827586, 23.6363636363636, 62.5, 37.9310344827586, 23.6363636363636, +62.5, 37.9310344827586, 23.6363636363636, 62.5, 37.9310344827586, +23.6363636363636, 62.5, 37.9310344827586, 23.6363636363636, 62.5, +37.9310344827586, 23.6363636363636, 62.5, 37.9310344827586, 23.6363636363636, +62.5, 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