# HG changeset patch -- Bitbucket.org # Project galaxy-dist # URL http://bitbucket.org/galaxy/galaxy-dist/overview # User Dan Blankenberg <dan(a)bx.psu.edu&gt; # Date 1279306372 14400 # Node ID a27244147802bd74be35ae24665cc3fc57580f23 # Parent 11fb3bb5b25058b55d927d4dfb39dab1112f290b Modify tools contributed by Chungoo Park (cxp440(a)psu.edu) for Linear Discriminant Analysis, and drawing Receiver Operating Characteristic plots; add a new tool to 'Generate A Matrix for using PC and LDA'; remove redundant 'Principal Component Analysis' tool. --- a/test-data/pca_for_lda_output.txt.re_match +++ /dev/null @@ -1,87 +0,0 @@ -\"21\.91439361\d*\" -\"28\.33335569\d*\" -\"33\.54383183\d*\" -\"38\.39279966\d*\" -\"42\.95792886\d*\" -\"47\.09815550\d*\" -\"50\.94487376\d*\" -\"54\.58512629\d*\" -\"58\.11752456\d*\" -\"61\.36666878\d*\" -\"64\.43815007\d*\" -\"67\.37442210\d*\" -\"70\.19430653\d*\" -\"72\.98844264\d*\" -\"75\.67923231\d*\" -\"78\.07658354\d*\" -\"80\.38195983\d*\" -\"82\.59937656\d*\" -\"84\.57494344\d*\" -\"86\.46924307\d*\" -\"88\.07774382\d*\" -\"89\.63410862\d*\" -\"91\.04016474\d*\" -\"92\.37409738\d*\" -\"93\.48214466\d*\" -\"94\.55735875\d*\" -\"95\.53454224\d*\" -\"96\.40615879\d*\" -\"97\.19612900\d*\" -\"97\.88859479\d*\" -\"98\.51598802\d*\" -\"99\.11847048\d*\" -\"99\.57772251\d*\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" -\"100\" --- a/test-data/pca_for_lda_output.txt +++ /dev/null @@ -1,87 +0,0 @@ -"21.9143936160079" -"28.3333556968471" -"33.5438318357343" -"38.3927996693607" -"42.9579288624902" -"47.0981555066747" -"50.9448737656701" -"54.5851262912127" -"58.117524563425" -"61.3666687874078" -"64.4381500783847" -"67.3744221025279" -"70.1943065338356" -"72.9884426407974" -"75.6792323125723" -"78.0765835478277" -"80.381959836786" -"82.5993765654159" -"84.5749434452634" -"86.4692430753353" -"88.0777438285547" -"89.6341086298525" -"91.0401647492736" -"92.3740973813576" -"93.4821446651001" -"94.5573587555456" -"95.534542246934" -"96.4061587978985" -"97.1961290046054" -"97.888594796593" -"98.5159880218074" -"99.11847048736" -"99.5777225170877" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" -"100" --- a/tools/stats/plot_from_lda.xml +++ b/tools/stats/plot_from_lda.xml @@ -1,24 +1,24 @@ -<tool id="plot_for_lda_output1" name="Draw ROC"> +<tool id="plot_for_lda_output1" name="Draw ROC" version="1.0.1"><description>Receiver Operating Characteristic plot</description><command interpreter="sh">r_wrapper.sh $script_file</command><inputs> - <param format="text" name="input" type="data" label="Source file"/> - <param name="my_title" size="30" type="text" value="ROC" label="Title of your plot" help="See syntax below"> - </param> - + <param format="text" name="input" type="data" label="Source file"></param> + <param name="my_title" size="30" type="text" value="My Figure" label="Title of your plot" help="See syntax below"></param> + <param name="X_axis" size="30" type="text" value="Text for X axis" label="Legend of X axis in your plot" help="See syntax below"></param> + <param name="Y_axis" size="30" type="text" value="Text for Y axis" label="Legend of Y axis in your plot" help="See syntax below"></param></inputs><outputs><data format="pdf" name="pdf_output" /> - <data format="txt" name="desc_output" /></outputs><tests><test><param name="input" value="lda_analy_output.txt"/> - <param name="my_title" value="Test Plot"/> + <param name="my_title" value="Test Plot1"/> + <param name="X_axis" value="Test Plot2"/> + <param name="Y_axis" value="Test Plot3"/><output name="pdf_output" file="plot_for_lda_output.pdf"/> - <output name="desc_output" file="empty_file.dat"/></test></tests> @@ -203,7 +203,7 @@ for (i in 1:nfiles) { r&lt;-rez_ext[[i]] #tr - rate&lt;-rbind(rate, c(files_alias[i]," "," "," ") ) + # rate&lt;-rbind(rate, c(files_alias[i]," "," "," ") ) mm&lt;-which((r[3,])==max(r[3,])) m_tr[i]&lt;-mm[1] @@ -213,9 +213,9 @@ pdf(file= paste("${pdf_output}")) - plot(rez_ext[[i]][2,]~rez_ext[[i]][3,], xlim=c(0,100), ylim=c(0,100), xlab="X", ylab="Y", type="l", lty=1, col="blue", xaxt='n', yaxt='n') + plot(rez_ext[[i]][2,]~rez_ext[[i]][3,], xlim=c(0,100), ylim=c(0,100), xlab="${X_axis} [1-FP(False Positive)]", ylab="${Y_axis} [1-FP(False Positive)]", type="l", lty=1, col="blue", xaxt='n', yaxt='n') for (i in 1:nfiles) { - lines(rez_ext[[i]][2,]~rez_ext[[i]][3,], xlab="X", ylab="Y", type="l", lty=1, col=i) + lines(rez_ext[[i]][2,]~rez_ext[[i]][3,], xlab="${X_axis} [1-FP(False Positive)]", ylab="${Y_axis} [1-FP(False Positive)]", type="l", lty=1, col=i) # pt=c(r,) points(x=rez_ext[[i]][3,m_tr[i]],y=rez_ext[[i]][2,m_tr[i]], pch=16, col=i) } @@ -236,27 +236,6 @@ <help> -.. 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* - ------ - -**Example** - -- Input file - - A output file from "LDA" tool is used to perform it. - -- Output file - - There are two files produced: - (1) A summary file for LDA classification success rates with optimal tau - (2) An image file for plot </help> Binary file static/images/tools/lda/second_matrix_generator_example_file.png has changed --- a/test-data/lda_analy_output.txt +++ b/test-data/lda_analy_output.txt @@ -1,119 +1,134 @@ -structure(c(56.1728395061728, 59.5890410958904, 25, 56.1728395061728, -59.5890410958904, 25, 56.1728395061728, 59.5890410958904, 25, -56.1728395061728, 59.5890410958904, 25, 56.1728395061728, 59.5890410958904, -25, 56.1728395061728, 59.5890410958904, 25, 56.1728395061728, -59.5890410958904, 25, 56.1728395061728, 59.5890410958904, 25, -56.1728395061728, 59.5890410958904, 25, 56.1728395061728, 59.5890410958904, -25, 56.1728395061728, 59.5890410958904, 25, 56.1728395061728, -59.5890410958904, 25, 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label="Source file First: a matrix (samples/observations in rows and variables/features in columns)"></param> + <param format="tabular" name="input_2" type="data" label="Source file Second: a table (samples/observations with response/class label)"></param> + </inputs> + + <outputs> + <data format="tabular" name="output" /> + </outputs> + + <tests> + <test> + <param name="input_1" value="matrix_generator_for_pc_and_lda_input_1.tabular"/> + <param name="input_2" value="matrix_generator_for_pc_and_lda_input_2.tabular"/> + <output name="output" file="matrix_generator_for_pc_and_lda_output.tabular"/> + </test> + </tests> + + <help> + +.. class:: infomark + +**What it does** + +This tool consists of a module to generate a matrix to be used for running 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* + +----- + +**Example** + +- Input file (Source file First) + +.. image:: ../static/images/tools/lda/first_matrix_generator_example_file.png + + +- Input file (Source file Second) + +.. image:: ../static/images/tools/lda/second_matrix_generator_example_file.png + + +</help> + +</tool> --- /dev/null +++ b/tools/stats/generate_matrix_for_pca_lda.pl @@ -0,0 +1,147 @@ +#!/usr/bin/perl -w + +use strict; +use warnings; + +my $Input_Matrix = $ARGV[0]; +my $Input_Label = $ARGV[1]; + +my %Hash_X = (); +my %Hash_Y = (); +my $My_Num_X = 0; +my $My_Num_Y = 0; + +open (OUT, "> $ARGV[2]"); + +open (LABEL, "< $Input_Label") || + die "Sorry, I couldn't open the escape.txt for clone: $!\n"; + +my $Label_Index = 0; +my $X_Label; +my $input_Label; +while (defined($input_Label = <LABEL>)){ + chomp($input_Label); + my @cArray_Label = $input_Label =~ /(\S+)\s*/g; + if ($input_Label =~ /\w/){ + if ($Label_Index == 0){ + $Hash_X{$cArray_Label[0]} = $cArray_Label[1]; + $X_Label = $cArray_Label[1]; + $Label_Index = 1; + }else{ + if ($cArray_Label[1] eq $X_Label){ + $Hash_X{$cArray_Label[0]} = $cArray_Label[1]; + }else{ + $Hash_Y{$cArray_Label[0]} = $cArray_Label[1]; + } + } + } +} +close(LABEL); + +open (MATRIX, "< $Input_Matrix") || + die "Sorry, I couldn't open the escape.txt for clone: $!\n"; + +my %Hash_Matrix = (); +my %Hash_Features = (); +my @cArray_Features = (); + +my %Hash_Sum = (); +my $Matrix_Index = 0; +my $input_Matrix; +while (defined($input_Matrix = <MATRIX>)){ + chomp($input_Matrix); + my @cArray_Matrix = $input_Matrix =~ /(\S+)\s*/g; + if ($input_Matrix =~ /\w/){ + if ($Matrix_Index == 0){ + @cArray_Features = @cArray_Matrix; + my $Temp_Num_Array = scalar(@cArray_Matrix); + my $Temp_Index = 0; + for(;$Temp_Index < $Temp_Num_Array; $Temp_Index++){ + $Hash_Features{$cArray_Matrix[$Temp_Index]} = "BOL"; + $Hash_Sum{$cArray_Matrix[$Temp_Index]} = 0; + } + $Matrix_Index = 1; + }else{ + $Hash_Matrix{$cArray_Matrix[0]} = $input_Matrix; + } + } +} +close(MATRIX); + +my $Trace_Key; + +foreach $Trace_Key (sort {$a cmp $b} keys %Hash_X){ + my @cArray_Trace_X = $Hash_Matrix{$Trace_Key} =~ /(\S+)\s*/g; + my $Num_Array_Feature_X = scalar(@cArray_Features); + my $Index_Feature_X = 0; + for(;$Index_Feature_X < $Num_Array_Feature_X; $Index_Feature_X++){ + if ($Hash_Features{$cArray_Features[$Index_Feature_X]} eq "BOL"){ + $Hash_Features{$cArray_Features[$Index_Feature_X]} = $cArray_Trace_X[$Index_Feature_X + 1]; + }else{ + $Hash_Features{$cArray_Features[$Index_Feature_X]} = $Hash_Features{$cArray_Features[$Index_Feature_X]} . "\t" . $cArray_Trace_X[$Index_Feature_X + 1]; + } + + $Hash_Sum{$cArray_Features[$Index_Feature_X]} += $cArray_Trace_X[$Index_Feature_X + 1]; + } + $My_Num_X ++; +} + +my $Append_Key; +foreach $Append_Key (keys %Hash_Features){ + $Hash_Features{$Append_Key} = $Hash_Features{$Append_Key} . "\t" . $Hash_Sum{$Append_Key}; + $Hash_Sum{$Append_Key} = 0; +} + +foreach $Trace_Key (sort {$a cmp $b} keys %Hash_Y){ + my @cArray_Trace_Y = $Hash_Matrix{$Trace_Key} =~ /(\S+)\s*/g; + my $Num_Array_Feature_Y = scalar(@cArray_Features); + my $Index_Feature_Y = 0; + for(;$Index_Feature_Y < $Num_Array_Feature_Y; $Index_Feature_Y++){ + if ($Hash_Features{$cArray_Features[$Index_Feature_Y]} eq "BOL"){ + $Hash_Features{$cArray_Features[$Index_Feature_Y]} = $cArray_Trace_Y[$Index_Feature_Y + 1]; + }else{ + $Hash_Features{$cArray_Features[$Index_Feature_Y]} = $Hash_Features{$cArray_Features[$Index_Feature_Y]} . "\t" . $cArray_Trace_Y[$Index_Feature_Y + 1]; + } + + $Hash_Sum{$cArray_Features[$Index_Feature_Y]} += $cArray_Trace_Y[$Index_Feature_Y + 1]; + } + $My_Num_Y ++; +} + +foreach $Append_Key (keys %Hash_Features){ + $Hash_Features{$Append_Key} = $Hash_Features{$Append_Key} . "\t" . $Hash_Sum{$Append_Key} . "\t" . "EOL"; +} + +my $Prt_Key; +print OUT " \t"; +foreach $Prt_Key (sort {$a cmp $b} keys %Hash_X){ + print OUT "$Prt_Key \t"; +} +print OUT "X(SUM) \t"; + +foreach $Prt_Key (sort {$a cmp $b} keys %Hash_Y){ + print OUT "$Prt_Key \t"; +} +print OUT "Y(SUM) \t"; +print OUT "\n"; + +my $Prt_Index = 0; +my $Prt_Array_Num = scalar (@cArray_Features); +for(;$Prt_Index < $Prt_Array_Num; $Prt_Index++){ + print OUT "$cArray_Features[$Prt_Index] \t$Hash_Features{$cArray_Features[$Prt_Index]}\n"; +} + +print OUT " \t"; +my $My_Label_Index = 0; +for(;$My_Label_Index < $My_Num_X; $My_Label_Index++){ + print OUT "X \t"; +} +print OUT " \t"; + +$My_Label_Index = 0; +for(;$My_Label_Index < $My_Num_Y; $My_Label_Index++){ + print OUT "Y \t"; +} +print OUT " \t\n"; + +close(OUT); --- a/tools/stats/pca_for_lda.xml +++ /dev/null @@ -1,245 +0,0 @@ -<tool id="pca_for_lda1" name="Calculate PC"> - <description>Principal Component Analysis</description> - <command interpreter="sh">r_wrapper.sh $script_file</command> - - <inputs> - <param format="tabular" name="input" type="data" label="Source file"> - </param> - </inputs> - - <outputs> - <data format="txt" name="output" /> - </outputs> - - <tests> - <test> - <param name="input" value="pca_and_lda_analy_for_lda_input.tabular"/> - <output name="output" file="pca_for_lda_output.txt.re_match" compare="re_match"/> - </test> - </tests> - - <configfiles> - <configfile name="script_file"> - - rm(list = objects() ) - - ############# FORMAT X DATA ######################### - format&lt;-function(data) { - ind=NULL - for(i in 1 : ncol(data)){ - if (is.na(data[nrow(data),i])) { - ind&lt;-c(ind,i) - } - } - #print(is.null(ind)) - if (!is.null(ind)) { - data&lt;-data[,-c(ind)] - } - - data - } - - ########GET RESPONSES ############################### - get_resp&lt;- function(data) { - resp1&lt;-as.vector(data[,ncol(data)]) - resp=numeric(length(resp1)) - for (i in 1:length(resp1)) { - if (resp1[i]=="Control ") { - resp[i] = 0 - } - if (resp1[i]=="XLMR ") { - resp[i] = 1 - } - } - return(resp) - } - - ######## CHARS TO NUMBERS ########################### - f_to_numbers&lt;- function(F) { - ind&lt;-NULL - G&lt;-matrix(0,nrow(F), ncol(F)) - for (i in 1:nrow(F)) { - for (j in 1:ncol(F)) { - G[i,j]&lt;-as.integer(F[i,j]) - } - } - return(G) - } - - ###################NORMALIZING######################### - norm &lt;- function(M, a=NULL, b=NULL) { - C&lt;-NULL - ind&lt;-NULL - - for (i in 1: ncol(M)) { - if (sd(M[,i])!=0) { - M[,i]&lt;-(M[,i]-mean(M[,i]))/sd(M[,i]) - } - # else {print(mean(M[,i]))} - } - return(M) - } - - ##### LDA DIRECTIONS ################################# - lda_dec &lt;- function(data, k){ - priors=numeric(k) - grandmean&lt;-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 &lt;- 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&gt;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] &lt;= 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 &lt;- function(predclass, actualclass,msg=c("you forgot the message"), pr=1) { - er=sum(predclass != actualclass)/length(predclass) - - matr&lt;-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&lt;-matrix("${input}", 1,1, byrow=T) - - tau&lt;-seq(0,1, by=0.005) - #tau&lt;-seq(0,1, by=0.1) - for_curve=matrix(-10, 3,length(tau)) - - ############################################################## - - test_data_whole_X &lt;-read.delim(files[1,1], row.names=1) - - #### FORMAT TRAINING DATA #################################### - # get only necessary columns - - test_data_whole_X&lt;-format(test_data_whole_X) - oligo_labels&lt;-test_data_whole_X[1:(nrow(test_data_whole_X)-1),ncol(test_data_whole_X)] - test_data_whole_X&lt;-test_data_whole_X[,1:(ncol(test_data_whole_X)-1)] - - X_names&lt;-colnames(test_data_whole_X)[1:ncol(test_data_whole_X)] - test_data_whole_X&lt;-t(test_data_whole_X) - resp&lt;-get_resp(test_data_whole_X) - ldaqda_resp = resp + 1 - a&lt;-sum(resp) # Number of Subject - b&lt;-length(resp) - a # Number of Escape - ## FREQUENCIES ################################################# - F&lt;-test_data_whole_X[,1:(ncol(test_data_whole_X)-1)] - F&lt;-f_to_numbers(F) - FN&lt;-norm(F, a, b) - ss&lt;-svd(FN) - eigvar&lt;-NULL - eig&lt;-ss\$d^2 - - print_out &lt;- c(rep('NA', length(ss\$d))) - for ( i in 1:length(ss\$d)) { - eigvar[i]&lt;-sum(eig[1:i])/sum(eig) - # print(paste(c("Variance explained : ", eigvar[i]*100, " %"), collapse="")) - print_out[i] &lt;- c(eigvar[i]*100) - } - - #My_Names &lt;- c("order of principal components", "accumulated variance explained", "") - #names(print_out)&lt;-My_Names - - write.table(print_out, file = "${output}", col.names = FALSE, row.names = FALSE) - - </configfile> - </configfiles> - - <help> - -.. class:: infomark - -**What it does** - -This tool consists of the first module to perform the Principal Component 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* - ------ - -**Example** - -- Input file - -.. image:: ../static/images/tools/lda/LDA_Input.png - -- Output file - - The values indicate "accumulated variance explained (%)" and were sorted by the order of principal components (i.e., 1st, 2nd, 3rd, 4th...). - - -</help> - -</tool> --- a/tool_conf.xml.sample +++ b/tool_conf.xml.sample @@ -129,7 +129,7 @@ <tool file="stats/gsummary.xml" /><tool file="filters/uniq.xml" /><tool file="stats/cor.xml" /> - <tool file="stats/pca_for_lda.xml" /> + <tool file="stats/generate_matrix_for_pca_lda.xml" /><tool file="stats/lda_analy.xml" /><tool file="stats/plot_from_lda.xml" /><tool file="regVariation/t_test_two_samples.xml" /> --- /dev/null +++ b/test-data/matrix_generator_for_pc_and_lda_input_1.tabular @@ -0,0 +1,88 @@ + CTCAGAAAAAAA CCATCCATCCATCCATCC AGAGGGAGAGGG ATCTGTTGATGG AGGGGTGACAGA ATCCCATTTACA AAGGGTATCAGT ATAATATATACA TATTATATGTATA CATCATCATCATCA CCCCATGATCCA TGATTCCTTAAAGAA AACACTTGGACA TATTAATATAATTTATAAA ATGTTTATTTTA TATCCAGGAGAA CAATTCTCCTGCC ACCATAAAAATGAGAAC CTCAGCCATAAAAA ATGAGTGAGAATAT ACTGTGAGTCAA TAAACTAAAGAGCTTCTGCACAGCA TCCTTTGGGTATAT ATGCTGCTATAAAGACACATGCACAC TGCTGGAGAGGATGTGGAGAAATAGGAACACTTTTACACTG TCCACAATGGTTGAA TTGGCTGCATAAAT CAATGGAACAGAACAGAGCCCTCAGAAATAA GAACTAGAAATACCATTTGACCCAGC TCAAAGATCAGAT GACCCATCTCACACCAGTTAGAATG CAAAGAGAATAAAATACCTAGGTATTTTATTCTTTTT AAAATGGCCATACTGCCCAAG AAATTTTTCCAATTCTGTGAAGAAA TTCACAATAGCAAAGAC TAGGAAGAATCAATA CTGGGTATATACCCAGTAATGG TTGGAAGTTCTGGCCAGGGCAA TGAAGGACCTCTTCAAGGAGAACTACAAACCACTGCTCAA CTCTCACCACTCCTATTCAACATA ACACACACATAT CCATGAGCATGGAATGTTCT AACCCAAATGTCC TTTACAAGAAAAAAACAAACAACCCCA AGTTCTAGATCC CTGGAAGCATTCCCTTT GATCCCATTTGTC GATATGAACAGACA TGGGTTTGTCATA AGTTTCTTTTGC A ATTACCCAGTCT TCATGTCATCTGCAAACA AAAACTCTCAAT CAACTTCAGCAA ATTGGCTGTGGGTTT AATTAGATCCCA GACCTAAAACCA AGAATCTACAAT AAACCAAACACC GGAGTTCCAGACCAGCCT CCTTGCCCATGCC ATCATACTGAAT AGGAAGTCAAATT AGCAAACCACCA AGCATGATTTATA GAGAGCCAAATCATGAGTG AAAGAGCCAAAT AGCAATTGTGAATGG CACTATTCACAATTGC TCAGGATACAAAATCAATG CAATGAACTCAAACAAATT TCCAACAAAGGACATGAACTCATC AATTGAACAATGAGAACATGTGGTAT AACCACAATGAGAACA GTGTATATATATACAT ACATATATATAT GTCAGATGGATA TCATCTGACAAAGGGCTAATATCCAG TTTGTCAGGTTTGTCAAAG TTCTTAATCCAGTCTATCATT TTTAATCCATCTTGA AAACTACCATCAGAGTGAACAGGCA GCCATCAGAGAAAT ACAGGCAACCTACA GCAACCTACTCA TAGAAAACCCCAT +ARHGEF9 0 0 0 0 0 0 0 0 1 0 0 0 2 0 1 0 0 3 1 0 0 0 0 1 2 2 1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 +KIAA2022 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 +ZDHHC15 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 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 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 0 0 0 0 0 +PGK1 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 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+++ b/tools/stats/lda_analy.xml @@ -1,9 +1,9 @@ -<tool id="lda_analy1" name="Perform LDA"> +<tool id="lda_analy1" name="Perform LDA" version="1.0.1"><description>Linear Discriminant Analysis</description><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 syntax below"> + <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> @@ -14,7 +14,7 @@ <tests><test> - <param name="input" value="pca_and_lda_analy_for_lda_input.tabular"/> + <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"/> @@ -47,10 +47,10 @@ resp1&lt;-as.vector(data[,ncol(data)]) resp=numeric(length(resp1)) for (i in 1:length(resp1)) { - if (resp1[i]=="Control ") { + if (resp1[i]=="Y ") { resp[i] = 0 } - if (resp1[i]=="XLMR ") { + if (resp1[i]=="X ") { resp[i] = 1 } } @@ -256,6 +256,12 @@ <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** @@ -266,16 +272,13 @@ This tool consists of the module to perf ----- -**Example** +.. class:: warningmark -- Input file +**Note** -.. image:: ../static/images/tools/lda/LDA_Input.png +- 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. -- Output file - - 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. </help> --- /dev/null +++ b/test-data/matrix_generator_for_pc_and_lda_output.tabular @@ -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 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