Mercurial > repos > devteam > lda_analysis
changeset 1:423bcc3a3785 draft
Uploaded correct tarball.
author | devteam |
---|---|
date | Mon, 28 Jul 2014 11:41:28 -0400 |
parents | 2a8638f0c47e |
children | cf85ea165ce0 |
files | MINE.xml lda_analy.xml mine_wrapper.py r_wrapper.sh test-data/lda_analy_output.txt test-data/matrix_generator_for_pc_and_lda_output.tabular tool_dependencies.xml |
diffstat | 7 files changed, 535 insertions(+), 169 deletions(-) [+] |
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--- 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 @@ -<tool id="maximal_information_based_nonparametric_exploration" name="MINE" version="0.0.1"> - <description>- Maximal Information-based Nonparametric Exploration</description> - <requirements> - <requirement type="package" version="1.0.1">MINE</requirement> - </requirements> - <command interpreter="python">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 "${output_log}" - </command> - <inputs> - <param name="input_file" type="data" format="csv" label="CSV file" /> - - <conditional name="master_variable_type"> - <param name="master_variable_type_selector" type="select" label="Choose the master variable type"> - <option value="allPairs">allPairs</option> - <option value="adjacentPairs">adjacentPairs</option> - <option value="compare_against_ith" selected="True">compare against i-th</option> - </param> - <when value="compare_against_ith"> - <param type="integer" value="0" name="master_variable" /> - <param type="boolean" truevalue="--permute" false_value="" name="permute" checked="False" /> - </when> - <when value="adjacentPairs"> - <param type="boolean" truevalue="--permute" false_value="" name="permute" checked="False" /> - </when> - </conditional> - - <param type="float" value="0" name="cv" /> - - <param type="float" value="0.6" name="exp" /> - - <param type="float" value="15" name="c" /> - - </inputs> - <outputs> - <data format="csv" name="output_results" label="${tool.name} on ${on_string} (Results)" /> - <data format="txt" name="output_log" label="${tool.name} on ${on_string} (log)" /> - </outputs> - <tests> - <!-- TODO --> - </tests> - <help> -**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.* - - </help> -</tool>
--- /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 @@ +<tool id="lda_analy1" name="Perform LDA" version="1.0.1"> + <description>Linear Discriminant Analysis</description> + <requirements> + <requirement type="package" version="2.11.0">R</requirement> + </requirements> + <command interpreter="sh">r_wrapper.sh $script_file</command> + <inputs> + <param format="tabular" name="input" type="data" label="Source file"/> + <param name="cond" size="30" type="integer" value="3" label="Number of principal components" help="See TIP below"> + <validator type="empty_field" message="Enter a valid number of principal components, see syntax below for examples"/> + </param> + + </inputs> + <outputs> + <data format="txt" name="output" /> + </outputs> + + <tests> + <test> + <param name="input" value="matrix_generator_for_pc_and_lda_output.tabular"/> + <output name="output" file="lda_analy_output.txt"/> + <param name="cond" value="2"/> + + </test> + </tests> + + <configfiles> + <configfile name="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}") + + + </configfile> + </configfiles> + + <help> + +.. 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. + + +</help> + +</tool>
--- 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__()
--- /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
--- /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, 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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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/matrix_generator_for_pc_and_lda_output.tabular Mon Jul 28 11:41:28 2014 -0400 @@ -0,0 +1,88 @@ + AGTR2 ARHGEF9 BRWD3 CLCN5 FMR1 GPC3 GPR173 GRIA3 GSPT2 HUWE1 KIAA2022 KLF8 MAGEH1 MTM1 NUDT10 NUDT11 PAGE1 PAGE4 PAK3 PCSK1N PGK1 PHF6 RRAGB SHROOM4 SOX3 TMEM29 TRO TSR2 USP51 UXT ZDHHC15 ZDHHC9 X(SUM) APOOL AR BEX4 BEX5 CHIC1 CHM CPXCR1 CSTF2 CXCR3 CXorf26 CYSLTR1 DACH2 DIAPH2 DRP2 EDA ESX1 FGF16 FXYD8 GPR23 HDX HEPH IL1RAPL2 ITM2A KIAA1166 KLHL4 LAS1L MAGEE1 MAGEE2 MSN MTMR8 NAP1L2 NRK NSBP1 NXF3 P2RY10 PABPC5 POF1B RNF12 RPS6KA6 SLC7A3 SPIN2A SPIN3 SPIN4 STARD8 SYTL4 TAF9B TBX22 TCEAL2 TCEAL4 TMEM28 UBQLN2 UPRT ZCCHC5 ZNF711 ZXDA Y(SUM) +CTCAGAAAAAAA 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 EOL +CCATCCATCCATCCATCC 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 3 0 0 0 0 0 0 0 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