Mercurial > repos > mmaiensc > arts
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/COMMIT_EDITMSG Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,1 @@ +Update readme
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/HEAD Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,1 @@ +ref: refs/heads/master
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/config Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,12 @@ +[core] + bare = false + repositoryformatversion = 0 + filemode = true + logallrefupdates = true + ignorecase = true +[remote "origin"] + url = https://github.com/mmaiensc/ARTS.git + fetch = +refs/heads/*:refs/remotes/origin/* +[branch "master"] + remote = origin + merge = refs/heads/master
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/description Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,1 @@ +Tools for study randomization \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/hooks/README.sample Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,5 @@ +#!/bin/sh +# +# Place appropriately named executable hook scripts into this directory +# to intercept various actions that git takes. See `git help hooks` for +# more information.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/info/exclude Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,2 @@ +# File patterns to ignore; see `git help ignore` for more information. +# Lines that start with '#' are comments.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/logs/HEAD Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,2 @@ +0000000000000000000000000000000000000000 bd39087f25b3386a667a6744de4aa95642b0ad82 mmaiensc <mmaiensc@gmail.com> 1384376349 -0600 commit (initial): Initial push +bd39087f25b3386a667a6744de4aa95642b0ad82 54a4951e3c5c45f409e70a985b0cea2455ae957e mmaiensc <mmaiensc@gmail.com> 1384376504 -0600 commit: Update readme
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/logs/refs/heads/master Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,2 @@ +0000000000000000000000000000000000000000 bd39087f25b3386a667a6744de4aa95642b0ad82 mmaiensc <mmaiensc@gmail.com> 1384376349 -0600 commit (initial): Initial push +bd39087f25b3386a667a6744de4aa95642b0ad82 54a4951e3c5c45f409e70a985b0cea2455ae957e mmaiensc <mmaiensc@gmail.com> 1384376504 -0600 commit: Update readme
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/logs/refs/remotes/origin/master Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,1 @@ +0000000000000000000000000000000000000000 54a4951e3c5c45f409e70a985b0cea2455ae957e mmaiensc <mmaiensc@gmail.com> 1384376550 -0600 update by push
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/objects/bd/39087f25b3386a667a6744de4aa95642b0ad82 Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,2 @@ +xN0D9+Vq] U儸^%۱pysaT;.D@(ch5 ƌJ"48u.#zJM ? +vLR+9ah;vxM=KlX Uh_ ->3tϧs ^0 _sRk 09s S^ 2AޛWވ rY \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/refs/heads/master Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,1 @@ +54a4951e3c5c45f409e70a985b0cea2455ae957e
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/.git/refs/remotes/origin/master Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,1 @@ +54a4951e3c5c45f409e70a985b0cea2455ae957e
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/ARTS.pl Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,1244 @@ +#!/usr/bin/perl +# ARTS: Automated Randomization of multiple Traits for Study Design, using diploidly GA +# Mark Maienschein-Cline, last updated 8/19/2013 +# mmaiensc@uic.edu +# Center for Research Informatics, University of Illinois at Chicago +# +# Copyright (C) 2013 Mark Maienschein-Cline +# +# This program is free software; you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation; either version 2 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License along +# with this program; if not, write to the Free Software Foundation, Inc., +# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. + + + + + +use Getopt::Long qw(:config no_ignore_case); +#use Time::HiRes qw( clock_gettime ); +use Math::Trig; +$|++; + +# +# initialize random number parameters +# +&ran1_init(); + +# +# read command line +# +&read_command_line(); + +# +# read phenotype list: print the title lines of columns used for verbose +# +&read_data(); +if( $verb eq "y" || $verb eq "l" ){ + printf("Using traits:"); + for($i=0; $i<= $#allcols; $i++){ + print "\t$titlevals[$allcols[$i]]"; + } + print "\n"; + printf("Using trait combinations:"); + for($i=0; $i<= $#cols; $i++){ + printf("\t{%s", $titlevals[$cols[$i][0]]); + for($j=1; $j<= $#{$cols[$i]}; $j++){ + printf(",%s", $titlevals[$cols[$i][$j]]); + } + printf("}"); + } + print "\n"; +} + +# +# initialize GA parameters +# +&ga_init(); + +# +# if using the batchcolumn, fill in the batch +# +if( $bcolumn ne "" ){ + if( $verb eq "y" ){ + printf("Looking at column %i (%s) for batch assignments\n", $bcolumn+1, $titlevals[$bcolumn]); + } + # fill in batch from last column of @data + $numbatches = 0; + $foundbatchhash = {}; + @batchsizes = (); + for($i=0; $i<=$#data; $i++){ + if( $foundbatchhash->{$data[$i][$bcolumn]} eq "" ){ + $foundbatchhash->{$data[$i][$bcolumn]} = $numbatches; + $numbatches++; + push(@batchnames, $data[$i][$bcolumn]); + push(@batchsizes, 0); + } + $batchsizes[$foundbatchhash->{$data[$i][$bcolumn]}]++; + $data[$i][$#{$data[0]}] = $data[$i][$bcolumn]; + } + $mi = &mutual_info( $numbatches ); + $bestmi = $mi; +} + +# +# else do sampling: run GA +# +if( $bcolumn eq "" ){ + &initialize_population(); + + $oldavg = 1; + $err = 0.0001; + for($n=0; $n< $numgen; $n++){ + &add_immigrants(); + @population = &permute( \@population ); + $k = 0; + $k = &crossover( $k ); + $k = &mutate( $k ); + $k = &add_parents( $k ); + @pool = sort{$a->{score} <=> $b->{score}} @pool; + $average = &fill_population(); + + # check if we've done enough already, and print out status + if( $verb eq "y" ){printf(" Generation %i of %i, average fitness %0.4f\n", $n+1, $numgen, $average );} + if( $oldavg >= $average && $oldavg - $average < $err ){last;} + $oldavg = $average; + } + + # save the final best one + for($i=0; $i<= $#data; $i++){ + &fill_assignments( \@{$population[0]->{assignments}} ); + } +} + +# +# print final log to stdout +# +if( $verb eq "y" || $verb eq "l" ){&print_info;} + +# +# print result +# +if( $out ne "" ){ + open(OUT,">$out"); + print OUT "$title\t$bname\n"; + for($i=0; $i<= $#data; $i++){ + $orig[$i][1] = $data[$i][$#{$data[0]}]; + printf OUT ("%s\t%i\n", $orig[$i][0], $orig[$i][1]); + } + close(OUT); +} + +############### +# SUBROUTINES # +############### + +# read command line options +sub read_command_line{ + my $i; + + # + # option default values + # + $in = ""; + $out = ""; + $bcolumn = ""; + $batch = ""; + $bname = "batch"; + $phenocols = ""; + $contcols = ""; + $datecols = ""; + $bins = 5; + @blist = (); + $verb = "y"; + $mmi = 0; + + $options = " +Usage: ./ARTS.pl <OPTIONS> + REQUIRED: + -i input traits (rectangular, tab-delimited matrix, including title line with column names) + -c trait columns to randomize + comma- and semicolon delimited list, columns indexed from 1 + all traits indicated by commas are used in joint distributions + AND EITHER -b AND -o, OR -p: + -b batch sizes (a single number, or a comma-delimited list) + -o output file (formatted same as input file, with batch added as last column) + -or- + -p <batch column index>: print MI statistic for input traits using this column as batch designations + -p will not do any sampling + OTHER OPTIONS: + -cc continuously-valued columns (will be binned) + -cd date-valued columns (should be M/D/Y); should also list these as continuous (in -cc) + -cb number of bins to use for continuous or date columns (default: $bins for each) + can give 1 value, or a list of the same length as -cc; if a list, will be assigned in the same order as -cc + -bn batch name (title of added column, default $bname) + -s random number seed (large negative integer, default: $seed) +"; + +# +# Secret options: +# -v y or l (verbose: print all, or just print status from beginning or end) +# -mmi force use of MMI objective function on all columns indicated by -c, over-riding any other settings from -c +# + + GetOptions('i=s' => \$in, + 'o=s' => \$out, + 'p=i' => \$bcolumn, + 'b=s' => \$batch, + 'c=s' => \$phenocols, + 'cc=s' => \$contcols, + 'cd=s' => \$datecols, + 'cb=s' => \$bins, + 'bn=s' => \$bname, + 's=i' => \$seed, + 'mmi' => \$mmi, + 'v=s' => \$verb, + ) || die "$options\n"; + + # + # check that required inputs exist + # + if( $in eq "" ){&exit_required("i");} + if( ($out eq "" || $batch eq "") && $bcolumn eq "" ){&exit_required("b and -o, or -p,");} + if( $phenocols eq "" || $phenocols eq "None" ){&exit_required("c");} + + # + # check that inputs values are OK + # + if( $bcolumn ne "" ){ + if( $bcolumn < 1 ){&exit_err("p","at least 1");} + $bcolumn--; + } + if( $verb ne "y" && $verb ne "n" && $verb ne "l" ){&exit_err("v","y or n or l");} + if( $seed > 0 ){$seed*= -1;} + if( $seed == 0 ){&exit_err("s","non-zero");} + + # + # if mmi, reset phenocols value using all found columns + # + if( $mmi ){ + @initcs = split(/[,;]/, $phenocols); + # remove duplicates + @clist = (); + $cinds = {}; + for($i=0; $i<= $#initcs; $i++){ + if( $cinds->{$initcs[$i]} eq "" ){ + $cinds->{$initcs[$i]} = 1; + push(@clist, $initcs[$i]); + } + } + # add to new phenocols + $phenocols = "$clist[0]"; + for($i=1; $i<= $#clist; $i++){ + $phenocols = sprintf("%s,%s", $phenocols, $clist[$i]); + } + $phenocols = sprintf("%s;%s", $phenocols, $clist[0]); + for($i=1; $i<= $#clist; $i++){ + $phenocols = sprintf("%s;%s", $phenocols, $clist[$i]); + } + } + + # + # extract phenotype columns + # + @cols = (); + @allcols = (); + $alllist = {}; + @jointlist = split(';',$phenocols); + for($i=0; $i<= $#jointlist; $i++){ + @tmp = split(',',$jointlist[$i]); + @tmp = &fix_cols( \@tmp ); + push(@cols, [@tmp]); + for($j=0; $j<= $#tmp; $j++){ + if( $alllist->{$tmp[$j]} eq "" ){ + $alllist->{$tmp[$j]} = 1; + push(@allcols, $tmp[$j]); + } + } + } + + # + # extract continuous and date columns + # sort continuous columns so that bins correspond to them in order + # + if( $contcols ne "" && $contcols ne "None" ){ + @conts = split(',',$contcols); + @conts = &fix_cols( \@conts ); + $numconts = $#conts+1; + } + if( $datecols ne "" && $datecols ne "None" ){ + @dates = split(',',$datecols); + @dates = &fix_cols( \@dates ); + $numdates = $#dates+1; + # check that date columns are among continuous columns + for($i=0; $i<= $#dates; $i++){ + for($j=0; $j<= $#conts; $j++){ + if( $dates[$i] == $conts[$j] ){last;} + if( $j==$#conts ){ + printf("Error: please specify date column %i as continuous\n", $dates[$i]+1 ); + die; + } + } + } + } + if( $bins =~ /,/ ){ + @blist = split(',',$bins); + if( $#blist+1 != $#conts + 1 ){ + printf("Error: you input %i bins, but %i columns that need binning\n", $#blist+1, $#conts+1); + die; + } + } + else{ + for($i=0; $i<= $#conts; $i++){ + push(@blist, $bins); + } + } +} + +# print error message for flag $_[0], with correct values $_[1], and print usage +sub exit_err{ + printf("Error: set -%s to be %s\n%s\n", $_[0], $_[1], $options); + exit; +} +# print error message saying flag $_[0] is required +sub exit_required{ + printf("Error: option -%s is required\n%s\n", $_[0], $options); + exit; +} + +# fix all indices in array $_[0]: cast to integer, check at least 1, and subtract 1 +sub fix_cols{ + my @list; + my $i; + @list = @{$_[0]}; + for($i=0; $i<= $#list; $i++){ + $list[$i] = sprintf("%i", $list[$i]); + if( $list[$i] < 1 ){ + print "Error: column indices should be at least 1\n"; + die; + } + $list[$i]--; + } + return @list; +} + +# print info about best randomization +sub print_info{ + # + # get MI of each phenotype and average + # + $bestmi = &mutual_info(); + @bestmilist = &individual_mi( $numbatches ); + $bestavgmi = 0; + for($i=0; $i<= $#bestmilist; $i++){ + $bestavgmi+= $bestmilist[$i]/($#bestmilist+1); + } + + printf("Final MI %0.4f ; Individual trait MIs (mean %0.4f ): ", $bestmi, $bestavgmi); + for($i=0; $i<= $#bestmilist; $i++){ + printf("\t%0.4f", $bestmilist[$i]); + } + print "\n-----------------------------------------------------------------\n"; + # + # print the counts for each phenotype in each batch + # + # first title line: phenotype names + for($i=0; $i<= $#allcols; $i++){ + printf("\t%s values", $titlevals[$allcols[$i]]); + for($j=1; $j<= $#{$items->{$allcols[$i]}->{list}}; $j++){ + printf("\t"); + } + } + print "\nBatch (size)"; + # second title line: phenotype values + for($i=0; $i<= $#allcols; $i++){ + for($j=0; $j<= $#{$items->{$allcols[$i]}->{list}}; $j++){ + if( $items->{$allcols[$i]}->{list}[$j] ne "" ){printf("\t%s", &name($items->{$allcols[$i]}->{list}[$j], $allcols[$i]) );} + else{printf("\tempty");} + } + } + print "\n-------"; + # print a line of dashes to separate + for($i=0; $i<= $#allcols; $i++){ + for($j=0; $j<= $#{$items->{$allcols[$i]}->{list}}; $j++){ + printf("\t-------"); + } + } + print "\n"; + for($k=0; $k< $numbatches; $k++){ + printf("%s (%i)", $batchnames[$k], $batchsizes[$k] ); + for($i=0; $i<= $#allcols; $i++){ + for($j=0; $j<= $#{$items->{$allcols[$i]}->{list}}; $j++){ + printf("\t%i", &count( $#{$data[0]}, $batchnames[$k], $allcols[$i], $items->{$allcols[$i]}->{list}[$j] ) ); + } + } + print "\n"; + } + print "-------"; + # print a line of dashes to separate + for($i=0; $i<= $#allcols; $i++){ + for($j=0; $j<= $#{$items->{$allcols[$i]}->{list}}; $j++){ + printf("\t-------"); + } + } + # print totals for each type + print "\nTotal"; + for($i=0; $i<= $#allcols; $i++){ + for($j=0; $j<= $#{$items->{$allcols[$i]}->{list}}; $j++){ + printf("\t%i", $items->{$allcols[$i]}->{$items->{$allcols[$i]}->{list}[$j]}[1] ); + } + } + print "\n"; +} + +# for continuous valued columns, checked by $_[1], convert value $_[0] back to a range +sub name{ + my $i; + my $binw; + + # + # if there aren't any continuous columns, or $_[1] doesn't match one, just return $_[0] + # + if( $#conts < 0 ){return $_[0];} + for($i=0; $i<= $#conts; $i++){ + if( $_[1] == $conts[$i] ){last;} + if( $i==$#conts ){return $_[0];} + } + + # + # convert bin value back to continuous value + # + $binw = ($contstats[$i][2]-$contstats[$i][0])/$blist[$i]; + $val1 = $binw*$_[0]+$contstats[$i][0]; + $val2 = $binw*($_[0]+1)+$contstats[$i][0]; + + # + # if there aren't any date columns, or $_[1] doesn't match one, just return the range val1-val2 + # + if( $#dates < 0 ){return sprintf("%s-%s", $val1, $val2);} + for($i=0; $i<= $#dates; $i++){ + if( $_[1] == $dates[$i] ){last;} + if( $i==$#dates ){return sprintf("%s-%s", $val1, $val2);} + } + $val1 = sprintf("%i", $val1); + $val2 = sprintf("%i", $val2); + return sprintf("%s-%s", &convert_date( $val1 ), &convert_date( $val2 )); + +} + +# read in regular matrix from $in +# for continuous (including date-value) columns, make histograms +sub read_data{ + my @lines; + my $i; + @data = (); + $items = {}; + @orig = (); + @titlevals = (); + @batchsizes = (); + $numbatches = 0; + + # + # fix newline convention + # + + + open(IN,"$in") || die "Error: can't open $in\n"; + # + # read in all lines and check formatting + # + @lines = <IN>; + if( $lines[0] =~ /\r/ && $#lines == 0 ){ + # this happens with tab-delimited text saved from excel + @lines = split('\r', $lines[0]); + } + + # + # read title line + # + $title = $lines[0]; + chomp($title); + @titlevals = split('\t',$title); + for($k=1; $k<= $#lines; $k++){ + $line = $lines[$k]; + chomp($line); + if( $line ne "" ){ # ignore blank lines + @parts = split('\t',$line); + for($i=$#parts+1; $i<= $#titlevals; $i++){ + push(@parts, ""); + } + if( $#parts != $#titlevals ){ + printf("Error: not enough columns in line %i\n", $#data+2); + die; + } + # push 1 extra for the batch + push(@parts, 0); + push(@data, [(@parts)] ); + push(@orig, [($line, 0)] ); + } + } + close(IN); + + # + # exit if no data read + # + if( $#data < 0 ){ + printf("Error: no samples were read in\n"); + die; + } + + # + # if batch is not empty, check batches: + # if no commas, cast to integer and count how many are needed + # if there are commas, get batched on given sizes + # double check that we add up + # + @batchnames = (); + if( $batch ne "" ){ + if( $batch !~ /,/ ){ + $batch = sprintf("%i", $batch); + # fix batch size if too big + if( $batch > $#data + 1 ){ + printf("Warning: you have %i samples, but asked for a batch size of %i, so there is only 1 batch\n", $#data+1, $batch); + $batch = $#data+1; + } + $numbatches = ($#data+1)/$batch; + $exactbatches = sprintf("%i", $numbatches); + if( $exactbatches < $numbatches ){$exactbatches++;} + $numbatches = $exactbatches; + for($i=0; $i< $numbatches-1; $i++){ + push(@batchsizes, $batch); + } + push(@batchsizes, $batch - ($numbatches*$batch - ($#data+1)) ); + } + else{ + @batchsizes = split(',',$batch); + $numbatches = $#batchsizes+1; + } + $tot = 0; + for($i=0; $i< $numbatches; $i++){ + push(@batchnames, $i+1); + $tot+= $batchsizes[$i]; + } + if( $tot != $#data+1 ){ + printf("Error: have %i spaces in all batches, but %i samples\n", $tot, $#data+1); + die; + } + } + + # + # convert dates to numbers + # + for($i=0; $i<= $#data; $i++){ + for($j=0; $j<= $#dates; $j++){ + if( $data[$i][$dates[$j]] ne "" ){$data[$i][$dates[$j]] = &convert_date( $data[$i][$dates[$j]] );} + } + } + + # + # for all continuous columns, compute median and fill in missing values + # also record max and min for binning + # + @contstats = (); # records min, median, max for each continuous column + for($j=0; $j<= $#conts; $j++){ + @tmp = (); + for($i=0; $i<= $#data; $i++){ + if( $data[$i][$conts[$j]] ne "" ){push(@tmp, $data[$i][$conts[$j]]);} + } + @tmp = sort{ $a <=> $b } @tmp; + $median = $tmp[sprintf("%i", ($#tmp+1)/2)]; + push(@contstats, [($tmp[0], $median, $tmp[$#tmp])] ); + # for($i=0; $i<= $#data; $i++){ + # if( $data[$i][$conts[$j]] eq "" ){$data[$i][$conts[$j]] = $median;} + # } + } + + # + # for all continuous columns, bin data + # + for($j=0; $j<= $#conts; $j++){ + $binw = ($contstats[$j][2] - $contstats[$j][0])/($blist[$j]); + if( $binw == 0 ){ + printf("Error: max and min of column %i are equal (max/min are %f/%f)\n", $conts[$j]+1, $contstats[$j][2], $contstats[$j][0] ); + die; + } + for($i=0; $i<= $#data; $i++){ + if( $data[$i][$conts[$j]] ne "" ){ + $data[$i][$conts[$j]] = sprintf("%i", ($data[$i][$conts[$j]] - $contstats[$j][0])/$binw); + if( $data[$i][$conts[$j]] >= $blist[$j] ){$data[$i][$conts[$j]] = $blist[$j]-1;} + } + } + } + + # + # for each column we're using, count how many item types there are + # empty phenotypes are considered their own, distinct phenotype + # + $items = &itemize( \@allcols ); +} + +# count how many item types of @{$_[0]} there are in @data +sub itemize{ + my $i; + my $j; + my $info; + my @cols; + my $items; + @cols = @{$_[0]}; + + for($j=0; $j<= $#cols; $j++){ + $info = {}; + $info->{list} = (); + for($i=0; $i<= $#data; $i++){ + if( $info->{$data[$i][$cols[$j]]} eq "" ){ + $info->{$data[$i][$cols[$j]]} = [($#{$info->{list}}+1,0)]; + push(@{$info->{list}}, $data[$i][$cols[$j]]); + } + $info->{$data[$i][$cols[$j]]}[1]++; + $info->{count}++; + } + $info->{num} = $#{$info->{list}}+1; + $items->{$cols[$j]} = $info; + } + + for($j=0; $j<= $#cols; $j++){ + # this set prints the number of values and counts for each phenotype + #printf("%i,%s:", $cols[$j], $titlevals[$cols[$j]]); + #for($k=0; $k<= $#{$items->{$cols[$j]}->{list}}; $k++){ + # printf("\t%s,%i", $items->{$cols[$j]}->{list}[$k], $items->{$cols[$j]}->{$items->{$cols[$j]}->{list}[$k]}[1] ); + #} + #print "\n"; + if( $items->{$cols[$j]}->{num} > 20 ){ + printf("Warning: column %i (%s) has %i values; should you make it continuous?\n", $cols[$j], $titlevals[$cols[$j]], $items->{$cols[$j]}->{num} ); + } + } + return $items; +} + +# convert date in M/D/Y to integer, or integer to M/D/Y +sub convert_date{ + my $date; + my $month; + my $day; + my $year; + my $months; + my $i; + # cumulative days per month + $months->{0} = 0; + $months->{1} = 31; + $months->{2} = 59; + $months->{3} = 90; + $months->{4} = 120; + $months->{5} = 151; + $months->{6} = 181; + $months->{7} = 212; + $months->{8} = 243; + $months->{9} = 273; + $months->{10} = 304; + $months->{11} = 334; + $months->{12} = 365; + $date = $_[0]; + + # convert date to integer + if( $date =~ /\// ){ + ($month, $day, $year) = split('/',$date); + $month = sprintf("%i", $month); + $day = sprintf("%i", $day); + $year = sprintf("%i", $year); + if( $month < 1 || $month > 12 ){ + print "Error: found a month $month not between 1 and 12\n"; + die; + } + if( $day < 1 || $day > 31 ){ + print "Error: found a day $day not between 1 and 31\n"; + die; + } + + return $day + $months->{$month-1} + $year*$months->{12}; + } + # convert integer to date + elsif( $date == sprintf("%i", $date) ){ + $year = sprintf("%i", $date/($months->{12})); + $month = $date-$year*$months->{12}; + for($i=1; $i<=12; $i++){ + if( $month < $months->{$i} ){last;} + } + $day = $month - $months->{$i-1}; + $month = $i; + return sprintf("%s/%s/%s", $month, $day, $year); + } + else{ + printf("\nError: unrecognized format in convert_date(): %s\n", $date); + die; + } +} + +# set globals used by ran1 +sub ran1_init{ + # + # random number variables + # + $iset = 0; + $gset = 0; + #$iseed = clock_gettime(CLOCK_REALTIME); + #($first, $second) = split('\.', $iseed); + #$seed = sprintf("-%i%i", $second, $first); + $seed = -10854829; + $M1 = 259200; + $IA1 = 7141; + $IC1 = 54773; + $RM1 = 1.0/$M1; + $M2 = 134456; + $IA2 = 8121; + $IC2 = 28411; + $RM2 = 1.0/$M2; + $M3 = 243000; + $IA3 = 4561; + $IC3 = 51349; + $iff = 0; + $ix1 = 0; + $ix2 = 0; + $ix3 = 0; + @ranarray = (); + for($i=0; $i< 98; $i++){ + push(@ranarray, 0); + } +} + +# uniform random number generator, seed, iff, and various capital-letter variables set in beginning +sub ran1{ + my $j; + my $temp; + + if( $seed < 0 || $iff == 0 ){ + $iff = 1; + $ix1 = ($IC1 - $seed)%$M1; + $ix1 = ($IA1*$ix1 + $IC1)%$M1; + $ix2 = $ix1%$M2; + $ix1 = ($IA1*$ix1 + $IC1)%$M1; + $ix3 = $ix1%$M3; + for($j=1; $j<= 97; $j++){ + $ix1 = ($IA1*$ix1 + $IC1)%$M1; + $ix2 = ($IA2*$ix2 + $IC2)%$M2; + $ranarray[$j] = ($ix1 + $ix2*$RM2)*$RM1; + } + $seed = 1; + } + $ix1 = ($IA1*$ix1 + $IC1)%$M1; + $ix2 = ($IA2*$ix2 + $IC2)%$M2; + $ix3 = ($IA3*$ix3 + $IC3)%$M3; + + $j = sprintf("%i", 1 + ((97*$ix3)/$M3) ); + if( $j> 97 || $j< 1 ){ + printf("Error in ran1: $j outside of [1:97]\n"); + die; + } + $temp = $ranarray[$j]; + $ranarray[$j] = ($ix1 + $ix2*$RM2)*$RM1; + return $temp; +} + +# permute array $_[0] +sub permute{ + my @assignments; + my $i; + my $j; + my $tmp; + + @assignments = @{$_[0]}; + + # + # shuffle batches randomly + # + for($i=$#assignments; $i>= 0; $i--){ + $j = sprintf("%i", ($i+1)*&ran1() ); + $tmp = $assignments[$j]; + $assignments[$j] = $assignments[$i]; + $assignments[$i] = $tmp; + } + return @assignments; +} + +# fill data with assignments $_[0] +sub fill_assignments{ + my @list; + my $i; + @list = @{$_[0]}; + if( $#list != $#data ){ + print "Error in fill_assignments: mismatching list lengths\n"; + die; + } + for($i=0; $i<= $#list; $i++){ + $data[$i][$#{$data[0]}] = $list[$i]; + } +} + +# compute mutual information of a batch assignment +sub mutual_info{ + my $i; + my $s; + my $mi; + my $stot; + + $mi = 0; + for($i=0; $i<= $#cols; $i++){ + $mi += &this_mi( $_[0], $#{$data[0]}, \@{$cols[$i]} )/($#cols+1); + } + + return $mi; +} + +# compute all single-phenotype mutual information +sub individual_mi{ + my $i; + my @list; + my @milist; + + @milist = (); + for($i=0; $i<= $#allcols; $i++){ + @list = ($allcols[$i]); + push(@milist, &this_mi( $_[0], $#{$data[0]}, \@list ) ); + } + return @milist; +} + +# compute mutual information of columns $_[1] ($_[0] bins) and all of @{$_[2]} +sub this_mi{ + my $i; + my $j; + my $summand; + my @list; + my $jprob; + my $m1prob; + my $m2prob; + my $jbin; + my $m1bin; + my $m2bin; + my $jbinstot; + my $m1binstot; + my $m2binstot; + my @jbinlist; + my @m1binlist; + my @m2binlist; + my $mi; + my $s1; + my $s2; + my $s; + @list = @{$_[2]}; + + # initialize probabilities + $jprob = {}; # joint distribution + $m1prob = {}; # batch marginal dist + $m2prob = {}; # pheno marginal dist + @jbinlist = (); # phenotype combos found in joint distribution + @m1binlist = (); # batches found in batches distribution (1st marginal dist) + @m2binlist = (); # phenotype combos found in phenotypes distribution (2nd marginal dist) + + # + # read through data and add to distributions + # + $summand = 1.0/($#data+1); + for($i=0; $i<= $#data; $i++){ + # + # define bin names based on phenotype/batch + # for phenotypes p1, p2, etc., batch b: + # joint = p1_p2_..._pn_b + # 1st marginal = b + # 2nd marginal = p1_p2_..._pn + # + $jbin = sprintf("%s", $data[$i][$#{$data[0]}]); + $m1bin = sprintf("%s", $data[$i][$#{$data[0]}]); + $m2bin = ""; + for($j=0; $j<= $#list; $j++){ + # NOTE: + # $list[$j] is a phenotype column (e.g., gender) + # $data[$i][$list[$j]] is the value of that phenotype in sample $i (e.g., M or F) + # $items->{$list[$j]}->{$data[$i][$list[$j]]}[0] is the bin index (e.g., M->0, F->1) of that phenotype + + $jbin = sprintf("%s_%i", $jbin, $items->{$list[$j]}->{$data[$i][$list[$j]]}[0]); + if( $j>0 ){$m2bin = sprintf("%s_", $m2bin);} + $m2bin = sprintf("%s%i", $m2bin, $items->{$list[$j]}->{$data[$i][$list[$j]]}[0]); + } + + # + # check if we've already seen this bin, for each distribution + # initialize probabilities and add to list if it's the first time + # + if( $jprob->{$jbin} eq "" ){ + $jprob->{$jbin} = 0; + push(@jbinlist, [($jbin, $m1bin, $m2bin)] ); + } + if( $m1prob->{$m1bin} eq "" ){ + $m1prob->{$m1bin} = 0; + push(@m1binlist, $m1bin); + } + if( $m2prob->{$m2bin} eq "" ){ + $m2prob->{$m2bin} = 0; + push(@m2binlist, $m2bin); + } + + # + # add a count to each distribution + # + $jprob->{$jbin} += $summand; + $m1prob->{$m1bin} += $summand; + $m2prob->{$m2bin} += $summand; + } + + # + # compute mutual information, and entropy of m1prob and m2prob (for normalization) + # + $mi = 0; + $s1 = 0; + $s2 = 0; + for($i=0; $i<= $#jbinlist; $i++){ + $mi+= ($jprob->{$jbinlist[$i][0]}) * log( ($jprob->{$jbinlist[$i][0]})/($m1prob->{$jbinlist[$i][1]} * $m2prob->{$jbinlist[$i][2]}) ); + } + for($i=0; $i<= $#m1binlist; $i++){ + $s1-= $m1prob->{$m1binlist[$i]} * log( $m1prob->{$m1binlist[$i]} ); + } + for($i=0; $i<= $#m2binlist; $i++){ + $s2-= $m2prob->{$m2binlist[$i]} * log( $m2prob->{$m2binlist[$i]} ); + } + $s = sqrt($s1*$s2); + + # + # normalize mi + # + if( $s>0 ){$mi/= $s;} + + # + # return normalized mi (0=independent, 1=completely dependent) + # + return $mi; +} + +# count how many of @data have column $_[0] equal $_[1] and column $_[2] equal $_[3] +sub count{ + my $i; + my $tot; + + $tot = 0; + for($i=0; $i<= $#data; $i++){ + if( $data[$i][$_[0]] eq $_[1] && $data[$i][$_[2]] eq $_[3] ){$tot++;} + } + return $tot; +} + +# initialize GA parameters and large matrices +sub ga_init{ + my $i; + my $j; + my $info; + + $popsize = 100; + $numgen = 300; + $nchrmuts = 2; + $nnewimm = 10; + $nkeepparents = 2; + $nchrpool = ($nnewimm+$popsize) + ($nnewimm+$popsize)*$nchrmuts + $nkeepparents; + + # + # population to turn over each generation + # + @population = (); + for($i=0; $i< $popsize+$nnewimm; $i++){ + $info = {}; + $info->{score} = 0; + $info->{assignments} = [()]; + for($j=0; $j<= $#data; $j++){ + push(@{$info->{assignments}}, 0); + } + push(@population, $info); + } + + # + # new individuals to fill each generation + # + @pool = (); + for($i=0; $i< $nchrpool; $i++){ + $info = {}; + $info->{score} = 0; + $info->{assignments} = [()]; + for($j=0; $j<= $#data; $j++){ + push(@{$info->{assignments}}, 0); + } + push(@pool, $info); + } + + # + # array to randomize for batch assignments + # + @batched = (); + @bcounts = (); + for($i=0; $i<= $#batchsizes; $i++){ + push(@bcounts, 0); + for($j=0; $j< $batchsizes[$i]; $j++){ + push(@batched, $i+1); + } + } +} + +# initialize the population array: randomize $popsize batches and score each one +sub initialize_population{ + my $i; + + for($i=0; $i< $popsize; $i++){ + @{$population[$i]->{assignments}} = &permute( \@batched ); + &fill_assignments( \@{$population[$i]->{assignments}} ); + $population[$i]->{score} = &mutual_info( $numbatches ); + } +} + +# complete the crossover step, keeping track of our index with $_[0] +sub crossover{ + my $i; + my $j; + my $k; + + $k = $_[0]; + + for($i=0; $i< $popsize + $nnewimm; $i+=2){ + &do_cross($i, $k); + &fill_assignments( \@{$pool[$k]->{assignments}} ); + $pool[$k]->{score} = &mutual_info( $numbatches ); + $k++; + &fill_assignments( \@{$pool[$k]->{assignments}} ); + $pool[$k]->{score} = &mutual_info( $numbatches ); + $k++; + } + return $k; +} + +# do a crossover between population members $_[0] and $_[0]+1, fill to pool members $_[1] and $_[1]+1 +sub do_cross{ + my $i; + my $j; + my $popmem; + my $poolmem; + my $index; + my @swap; + my @subswap; + my $swapinds; + + $popmem = $_[0]; + $poolmem = $_[1]; + $index = sprintf("%i", $#data * &ran1() ); + + # + # count how many of each batch to switch + # + for($i=0; $i<= $#bcounts; $i++){ + $bcounts[$i] = 0; + } + for($i=0; $i<= $index; $i++){ + $bcounts[$population[$popmem]->{assignments}[$i] - 1]++; + } + + # + # for each batch i: + # record into subswap all indices with that batch from popmem+1 + # permute subswap + # add first bcounts[$i] into swap + # then sort swap + # + @swap = (); + $swapinds = {}; + for($i=0; $i<= $#bcounts; $i++){ + @subswap = (); + for($j=0; $j<= $#data; $j++){ + if( $population[$popmem+1]->{assignments}[$j] == $i+1 ){ + push(@subswap, $j); + } + } + @subswap = &permute( \@subswap ); + for($j=0; $j< $bcounts[$i]; $j++){ + push(@swap, $subswap[$j]); + $swapinds->{$subswap[$j]} = 1; + } + } + @swap = sort{$a <=> $b} @swap; + + # + # fill start of first new chr from swap indices of second chr, and end from end of first chr + # + for($i=0; $i<= $index; $i++){ + $pool[$poolmem]->{assignments}[$i] = $population[$popmem+1]->{assignments}[$swap[$i]]; + } + for($i=$index+1; $i<= $#data; $i++){ + $pool[$poolmem]->{assignments}[$i] = $population[$popmem]->{assignments}[$i]; + } + + # + # fill start of second chr from start of first chr, and end from remaining parts of second chr + # + for($i=0; $i<= $index; $i++){ + $pool[$poolmem+1]->{assignments}[$i] = $population[$popmem]->{assignments}[$i]; + } + $j = $index+1; + for($i=0; $i<= $#data; $i++){ + if( $swapinds->{$i} != 1 ){ + $pool[$poolmem+1]->{assignments}[$j] = $population[$popmem+1]->{assignments}[$i]; + $j++; + } + } + + + # + # check that batch counts are still ok + # + $checkbatch = 0; + if( $checkbatch ){ + for($i=0; $i<= $#bcounts; $i++){ + $bcounts[$i] = 0; + } + for($i=0; $i<= $#data; $i++){ + $bcounts[$pool[$poolmem]->{assignments}[$i] - 1]++; + } + for($i=0; $i<= $#bcounts; $i++){ + if( $bcounts[$i] != $batchsizes[$i] ){ + print "Error in do_cross: lost some batch counts in first daughter chr\n"; + exit; + } + } + for($i=0; $i<= $#bcounts; $i++){ + $bcounts[$i] = 0; + } + for($i=0; $i<= $#data; $i++){ + $bcounts[$pool[$poolmem+1]->{assignments}[$i] - 1]++; + } + for($i=0; $i<= $#bcounts; $i++){ + if( $bcounts[$i] != $batchsizes[$i] ){ + print "Error in do_cross: lost some batch counts in first daughter chr\n"; + exit; + } + } + } +} + +# complete the mutation step, keeping track of our index with $_[0] +sub mutate{ + my $i; + my $j; + my $k; + + $k = $_[0]; + + for($i=0; $i< $popsize+$nnewimm; $i++){ + for($j=0; $j< $nchrmuts; $j++){ + &do_mutation($i, $k); + &fill_assignments( \@{$pool[$k]->{assignments}} ); + $pool[$k]->{score} = &mutual_info( $numbatches ); + $k++; + } + } + return $k; +} + +# do a mutation for population member $_[0], fill to pool member $_[1] +sub do_mutation{ + my $i; + my $popmem; + my $poolmem; + my $index1; + my $index2; + + $popmem = $_[0]; + $poolmem = $_[1]; + + # + # fill all of poolmem + # + for($i=0; $i<= $#data; $i++){ + $pool[$poolmem]->{assignments}[$i] = $population[$popmem]->{assignments}[$i]; + } + + # + # switch two members + # + $index1 = sprintf("%i", ($#data+1) * &ran1() ); + $index2 = sprintf("%i", ($#data+1) * &ran1() ); + + $pool[$poolmem]->{assignments}[$index1] = $population[$popmem]->{assignments}[$index2]; + $pool[$poolmem]->{assignments}[$index2] = $population[$popmem]->{assignments}[$index1]; +} + +# add immigrants, keeping track of our index with $_[0] +sub add_immigrants{ + my $j; + + for($j=0; $j< $nnewimm; $j++){ + @{$population[$popsize+$j]->{assignments}} = &permute( \@batched ); + &fill_assignments( \@{$population[$popsize+$j]->{assignments}} ); + $population[$popsize+$j]->{score} = &mutual_info( $numbatches ); + $k++; + } +} + +# add top-scoring parents, keeping track of our index with $_[0] +sub add_parents{ + my $i; + my $j; + my $k; + + $k = $_[0]; + # sort population now + @population = sort{$a->{score} <=> $b->{score}} @population; + + for($j=0; $j< $nkeepparents; $j++){ + ©_parents( $j, $k ); + # will copy score also in copy_parents() + $k++; + } + return $k; +} + +# add population member $_[0] to pool member $_[1] +sub copy_parents{ + my $i; + my $popmem; + my $poolmem; + my $index1; + my $index2; + + $popmem = $_[0]; + $poolmem = $_[1]; + + # + # fill all of poolmem + # + for($i=0; $i<= $#data; $i++){ + $pool[$poolmem]->{assignments}[$i] = $population[$popmem]->{assignments}[$i]; + } + $pool[$poolmem]->{score} = $population[$popmem]->{score}; +} + +# copy top of pool to population (assumes pool is sorted) +sub fill_population{ + my $i; + my $j; + my $m; + + $m = 0; + for($i=0; $i< $popsize; $i++){ + $population[$i]->{score} = $pool[$i]->{score}; + $m+= $population[$i]->{score}; + for($j=0; $j<= $#data; $j++){ + $population[$i]->{assignments}[$j] = $pool[$i]->{assignments}[$j]; + } + } + return $m/$popsize; +} + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/LICENSE Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,280 @@ +GNU GENERAL PUBLIC LICENSE + Version 2, June 1991 + + Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/> + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. By contrast, the GNU General Public +License is intended to guarantee your freedom to share and change free +software--to make sure the software is free for all its users. This +General Public License applies to most of the Free Software +Foundation's software and to any other program whose authors commit to +using it. (Some other Free Software Foundation software is covered by +the GNU Lesser General Public License instead.) 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/README Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,227 @@ +ARTS: Automated Randomization of multiple Traits for Study design +Written by Mark Maienschein-Cline +mmaiensc@gmail.com +Center for Research Informatics +University of Illinois at Chicago + +ARTS uses a genetic algorithm to optimize (minimize) a mutual information-based objective function, obtaining +an optimal randomization for studies of arbitrary size and design. + +The publication for this code is in preparation; citation to be added soon (hopefully!). When it is published, +the section of the supplementary information will give more details about usage (in addition to what's below). + +Please contact me at the email above with questions. + + + +There are two ways of using this code: command-line (it's a perl script), or through Galaxy. + +You can learn about, and download, Galaxy at http://galaxyproject.org. + +################ +# INSTALLATION # +################ + +# +# Command line version: +# +No installation needed, as long as you have a perl interpreter. Should work fine on a Mac or Linux system; +probably fine on Windows, but I haven't tested it. + +# +# Galaxy version: +# +Two options: +1) You can download this tool from the Galaxy toolshed directly into your installation. +2) Move the ARTS.pl and .xml files into tools/ in your Galaxy distribution, and edit the tool_config file +appropriately. If you don't know how to do this, you should probably use strategy #1. + +########### +# RUNNING # +########### + +# +# Galaxy version +# +Once you get the tools installed in Galaxy, there are help sections in the tool descriptions you can refer to. +Also refer to the instructions for the command-line version below. + +# +# Command line version: +# + +Run ARTS.pl without any inputs to see the usage. All inputs are specified using the usual [-flag] [value] +syntax (i.e., -i input.txt). + +Sample command using the sample_data.txt file: +./ARTS.pl -i sample_data.txt -c "2,3,4,5;2;3;4;5" -b 10 -o batched_data.txt -cc 2,4 -cd 4 + + +More information about the inputs (*'ed remarks refer to the values in the sample command above): + +-i Input trait table: tab-delimited table, including 1 header line. See sample_data.txt for an example. + You can prepare this table in Excel and save as a tab-delimited text, or just write it in a text file, + or copy-paste from Excel to a text file. You can have more columns than you will actually care about + randomizing here. + * You can use the file sample_data.txt as an example input; there are 5 columns, Sample ID, Age, Sex, + Collection Date, and Disease. + +-c Trait columns to randomize. This is a comma- and semicolon-delimited list. Its syntax is important, + so pay attention. + Columns are numbered starting from 1. Traits that should be considered jointly should be listed together + separated by commas. Each set of jointly considered traits should be listed separated by semicolons. Hence, + * -c "2,3,4,5;2;3;4;5" says to consider all the traits (columns 2-5) jointly (that's the 2,3,4,5 part), AND + to consider each trait individually (that's the ;2;3;4;5 part). + You could opt to only consider traits individually (-c "2;3;4;5"), or only jointly (-c "2,3,4,5"), or only + pair-wise (-c "2,3;2,4;2,5;3,4;3,5;4,5"), or whatever you want. + OUR GENERAL-PURPOSE RECOMMENDATION is to consider all traits jointly, plus all individually, as in the sample + command. This corresponds to the MMI statistic discussed in the publication. + GALAXY USERS: you just get to select the columns to consider, and the script will use the MMI statistic + automatically (you don't get a choice). + FINAL NOTE: you should put quotes around the value here, since otherwise semicolons will be interpreted + as end-of-line characters. + +-b Batch size (number of samples that can be processed at the same time). You have two options: + 1) Enter a single number. This will fill as many complete batches as possible, and put the remainder into a smaller + batch. This is probably convenient, but you should do a quick count to make sure you don't end up with a really + small last batch (e.g., if you have 105 samples and do batch size of 25, your last batch will only have 5 samples). + 2) Enter a comma-delimited list that adds up to the number of samples, which allows for uneven batch sizes + For example, -b 10,10,9,9 for 38 samples. If your math doesn't add up, the program will exit and let you know. + * sample_data.txt has 30 samples, so "-b 10" makes 3 batches of 10 samples each. + +-o Output file. Self-explanatory. The batch assignments are added as an extra column on the end, otherwise looks + like the input. + * batched_data.txt is our output file. + +-p (sort-of optional: you MUST use both -b and -o, OR just -p) Print (to STDOUT) the statistics of a batched + run using this column. The result will look like the last part of the STDOUT from an ARTS run (see below), + but you can use this option for testing batch assignments from another algorithm, or if you did one by hand. + +-cc Indices of continuously-valued columns. ARTS uses discrete values for its statistics, so these columns must + be discretized (binned). If ARTS encounters a column with more than 20 values, it will generate a warning asking + if you want it to be continuous. Comma-delimited list. + * In sample_data.txt, columns 2 (age) and 4 (date) could be considered continuous (that is, it's worth treating + a 35 year-old similarly to a 36 year-old), so we set "-cc 2,4". + +-cd Date-valued columns. These columns should also be listed under -cc, but this lets ARTS know to expect a date + (format MUST be M/D/Y, where month is a number (1 instead of January)) and convert the date to a number before + binning. + * In sample_data.txt, column 4 is a date, so set "-cd 4". + +-cb Number of bins to use for discretizing the continuous columns. Again, you can set a single value, or give a comma- + delimited list, which will match the order of the list given in the -cc flag. + * For the sample run, we left the default value of 5, but we could do, for example, "-cb 5,7", which would bin + the ages into 5 bins and the dates into 7 bins (since we set "-cc 2,4", and column 2 was age, column 4 was date). + +-bn Name for the batch column added to the output. Default is "batch". + +-s Random number seed. Set as a large negative integer. The code always uses the same seed, but if you want to + rerun with a different seed you can use this option. + +---------------------------------------------- + +When you run the sample command, the STDOUT looks like this (I added the N) line numbers): + +""""""""""""""""""" +1) Using traits: Age Sex Collection date Disease +2) Using trait combinations: {Age,Sex,Collection date,Disease} {Age} {Sex} {Collection date} {Disease} +3) Generation 1 of 300, average fitness 0.1432 +4) Generation 2 of 300, average fitness 0.1342 +5) Generation 3 of 300, average fitness 0.1298 +6) Generation 4 of 300, average fitness 0.1279 +7) Generation 5 of 300, average fitness 0.1250 +8) Generation 6 of 300, average fitness 0.1227 +9) Generation 7 of 300, average fitness 0.1211 +10) Generation 8 of 300, average fitness 0.1194 +11) Generation 9 of 300, average fitness 0.1187 +12) Generation 10 of 300, average fitness 0.1181 +13) Generation 11 of 300, average fitness 0.1175 +14) Generation 12 of 300, average fitness 0.1165 +15) Generation 13 of 300, average fitness 0.1143 +16) Generation 14 of 300, average fitness 0.1133 +17) Generation 15 of 300, average fitness 0.1132 +18) Generation 16 of 300, average fitness 0.1127 +19) Generation 17 of 300, average fitness 0.1123 +20) Generation 18 of 300, average fitness 0.1116 +21) Generation 19 of 300, average fitness 0.1119 +22) Generation 20 of 300, average fitness 0.1113 +23) Generation 21 of 300, average fitness 0.1113 +24) Generation 22 of 300, average fitness 0.1110 +25) Generation 23 of 300, average fitness 0.1110 +26) Final MI 0.1045 ; Individual trait MIs (mean 0.0091 ): 0.0155 0.0000 0.0209 0.0000 +27) ----------------------------------------------------------------- +28) Age values Sex values Collection date values Disease values +29) Batch (size) 19-27.2 35.4-43.6 51.8-60 43.6-51.8 27.2-35.4 M F 2/26/2012-11/11/2012 11/11/2012-7/27/2013 6/14/2011-2/26/2012 9/29/2010-6/14/2011 1/15/2010-9/29/2010 Y N +30) ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- +31) 1 (10) 2 2 2 1 3 5 5 3 2 2 2 1 5 5 +32) 2 (10) 2 2 1 2 3 5 5 2 2 4 1 1 5 5 +33) 3 (10) 3 2 1 1 3 5 5 3 2 2 2 1 5 5 +34) ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- +35) Total 7 6 4 4 9 15 15 8 6 8 5 3 15 15 +""""""""""""""""""" + +Here's what the lines mean: +1) Tells you what traits you've selected. +2) Tells you what trait combinations you've selected. +3-25) Prints the progress for each generation of the GA. Converges when average fitness changes by less than 0.0001. +26) Final objective function value. Normalized between 0 and 1, ideal case is 0. Note that different choices of the + objective function ARE NOT COMPARABLE: if you select fewer traits, or simpler combinations of traits (fewer + joint traits) using different -c values, you will get lower MI values, but this does not necessarily indicate better + overall randomization, because your choices may be overly simplistic. This is why we recommend sticking with the + MMI definition (all joint + all individual) consistently. This line also gives the randomization values for all + individual traits. +27-24) Inividual trait counts per batch for different values. Continuously-valued columns are given as a range + (e.g., age 19-27.2). +35) Total number of traits in each bin over all samples. + +---------------------------------------------- + +The output, batched_data.txt, will look like this: + +""""""""""""""""""" +Sample ID Age Sex Collection date Disease batch +sample1 25 M 3/28/2012 Y 3 +sample2 37 F 4/27/2013 N 3 +sample3 36 F 3/10/2013 N 1 +sample4 52 M 7/1/2012 Y 1 +sample5 48 M 8/13/2011 Y 3 +sample6 60 M 9/21/2011 N 3 +sample7 31 F 10/22/2010 Y 3 +sample8 28 F 1/15/2010 N 2 +sample9 26 M 1/7/2012 N 1 +sample10 44 F 4/5/2012 Y 1 +sample11 33 M 5/18/2012 N 3 +sample12 25 F 7/27/2013 N 3 +sample13 28 M 1/20/2013 Y 2 +sample14 30 F 8/11/2012 Y 3 +sample15 51 M 11/23/2011 N 2 +sample16 22 M 12/21/2011 N 2 +sample17 28 M 9/26/2010 Y 1 +sample18 19 F 1/18/2010 Y 3 +sample19 35 M 2/10/2012 N 1 +sample20 38 F 2/17/2012 N 2 +sample21 25 F 4/28/2012 Y 1 +sample22 55 M 1/7/2013 Y 2 +sample23 33 F 6/30/2013 N 1 +sample24 24 M 7/1/2012 Y 2 +sample25 42 M 2/15/2011 N 3 +sample26 60 M 5/21/2011 N 1 +sample27 34 F 10/23/2010 Y 2 +sample28 37 F 12/18/2010 Y 1 +sample29 41 F 11/7/2012 N 2 +sample30 50 F 2/15/2012 Y 2 +""""""""""""""""""" + +Looks the same as the input file, with a sixth column titled "batch" added, saying which of the three +batches each sample should be processed in (of course, you can permute the order of batches if you want). + +Included file batched_data.txt is what the output should look like. + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/batched_data.txt Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,31 @@ +Sample ID Age Sex Collection date Disease batch +sample1 25 M 3/28/2012 Y 3 +sample2 37 F 4/27/2013 N 3 +sample3 36 F 3/10/2013 N 1 +sample4 52 M 7/1/2012 Y 1 +sample5 48 M 8/13/2011 Y 3 +sample6 60 M 9/21/2011 N 3 +sample7 31 F 10/22/2010 Y 3 +sample8 28 F 1/15/2010 N 2 +sample9 26 M 1/7/2012 N 1 +sample10 44 F 4/5/2012 Y 1 +sample11 33 M 5/18/2012 N 3 +sample12 25 F 7/27/2013 N 3 +sample13 28 M 1/20/2013 Y 2 +sample14 30 F 8/11/2012 Y 3 +sample15 51 M 11/23/2011 N 2 +sample16 22 M 12/21/2011 N 2 +sample17 28 M 9/26/2010 Y 1 +sample18 19 F 1/18/2010 Y 3 +sample19 35 M 2/10/2012 N 1 +sample20 38 F 2/17/2012 N 2 +sample21 25 F 4/28/2012 Y 1 +sample22 55 M 1/7/2013 Y 2 +sample23 33 F 6/30/2013 N 1 +sample24 24 M 7/1/2012 Y 2 +sample25 42 M 2/15/2011 N 3 +sample26 60 M 5/21/2011 N 1 +sample27 34 F 10/23/2010 Y 2 +sample28 37 F 12/18/2010 Y 1 +sample29 41 F 11/7/2012 N 2 +sample30 50 F 2/15/2012 Y 2
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/galaxy_arts.xml Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,110 @@ +<tool id="ARTS" name="ARTS"> + <description>automated study randomization</description> + <command interpreter="perl">ARTS.pl -i $input -o $out -b $batch -c "$column" -cc $conts -cd $dates -cb $bins -bn $bname -s $seed -mmi -v l </command> + <inputs> + <param name="input" type="data" format="tabular" label="Input traits per sample" help="Ensure input is formatted as tabular"/> + <param name="batch" type="text" size="40" label="Batch size" optional="False" help="Set to a single number, or a comma-delimited list"/> + <param name="column" type="data_column" data_ref="input" multiple="True" numerical="False" label="Trait columns to randomize" help="Multi-select list - hold the appropriate key while clicking to select multiple columns." /> + <param name="conts" type="data_column" data_ref="input" multiple="True" numerical="False" optional="True" label="Continuous- and date-valued columns for binning (if any)" help="Multi-select list. Values should be numbers." /> + <param name="dates" type="data_column" data_ref="input" multiple="True" numerical="False" optional="True" label="Date-valued columns for binning (if any)" help="Multi-select list. Dates should be M/D/Y, where M, D, and Y are all integers (e.g., 7/9/1985)." /> + <param name="bins" type="text" size="40" label="Bin sizes (for continuously-valued columns)" value="5" optional="False" help="Set to a single number, or a comma-delimited list. If given as a list, will be used in same order as continuous columns."/> + <param name="bname" type="text" size="40" label="Batch name" value="batch" optional="False" help="Name given to the batch column in the output."/> + <param name="seed" type="integer" size="40" label="Random number seed" optional="False" value="-123456789"/> + </inputs> + <outputs> + <data format="tabular" name="out" /> + </outputs> + <help> + +**Purpose** + +This tool completes automated study randomization for a selected number of traits over the samples in your data set by minimizing a mutual information-based objective function using a genetic algorithm. + +NOTE: in the history output, click the i (view details) icon (between the save-to-disk and rerun icons), then click on stdout to see a summary of the run. This allows you to confirm which traits are being considered, and gives you a snapshot of how randomized individual traits are (it does not inform you about combinations of traits, which ARE ALSO being randomized). + +----- + +**Input traits per sample** + +- A list of traits associated with each sample, including a header line giving the name of each type of trait. For example:: + + ID Sex Age Sample date Diseased + Sample1 M 15 6/7/2011 Y + Sample2 M 25 8/5/2012 Y + Sample3 F 23 1/30/2012 N + Sample4 F 45 4/1/2013 N + Sample5 M 52 3/21/2011 Y + Sample6 F 37 3/12/2013 N + Sample7 M 31 7/17/2011 N + +----- + +**Batch size** + +- The size of each batch. You can specify this with a single number (e.g., 50), or a list of numbers (separated by commas, for example 50,50,49,49. + +- The first choice will fill up full batches as much as possible, and put all remaining samples in a smaller batch. Thus, the latter choice may be better if the batch size does not evenly divide the number of samples. For example, lets say you have 105 samples and can do a batch size of up to 30. Then:: + + (First option) Batch size = 30 --> batch sizes of 30, 30, 30, and 15 + -or- + (Second option) Batch size = 27,26,26,26 + +The second option has a more evenly distributed batch size, and will give better results. + +----- + +**Traits to randomize** + +- Which traits should be randomized. On Macs, hold command to multi-select. You do not need to select all columns (it would be silly, for example, to randomize over sample ID). + +- Note missing values for traits will be treated as an additional trait value (i.e., empty). + +- For the example above, we would select c2, c3, c4, and c5 (Sex, Age, Sample date, and Diseased). Not all traits need be selected, just the relevant ones (we may not care about Sample date, for example). + +----- + +**Continuous- and date-valued columns (optional)** + +- Use if you have columns with continuous values (e.g., age, blood pressure) or dates. They will be discretized prior to running. + +- For the example above, we would select c3 and c4 (Age, Sample date). + +----- + +**Date-valued columns (optional)** + +- Use if any of the columns selected as continuous are dates (MUST be formatted M/D/Y, where month is a number, for example 7/9/1985). + +- For the example above, we would select c4 (Sample date). + +----- + +**Bin sizes** + +- This only relates to any columns selected as continuous, and determines how many discrete bins the data will be split up in to. + +- You can set it to a single number, and all columns will use that number of bins. Or you can set it to a list of numbers to specify a different number of bins for each column. + +- For the example above, where we selected c3 and c4 as continuous, we could set:: + + Bin sizes=5,6 + +- which would split the Age column (c3) into 5 bins, and the Sample date column (c4) into 6 bins. + +----- + +**Batch name** + +- The output file will look exactly the same as the input, except an additional column will be added indicated which batch each sample should belong to. You can specify the name of that column here. + +----- + +**Random number seed** + +- This will not be need to be changed in general, but if you want to force the use of a different seed, you can. + + + + +</help> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/galaxy_arts_score.xml Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,84 @@ +<tool id="ARTSscore" name="ARTS Score"> + <description>compute the score for a study randomization</description> + <command interpreter="perl">ARTS.pl -i $input -p $batch -c "$column" -cc $conts -cd $dates -cb $bins -mmi -v l > $out </command> + <inputs> + <param name="input" type="data" format="tabular" label="Input traits per sample" help="Ensure input is formatted as tabular"/> + <param name="batch" type="data_column" data_ref="input" multiple="False" numerical="False" label="Batch column to use" help="Select which column corresponds to the batching you want to score." /> + <param name="column" type="data_column" data_ref="input" multiple="True" numerical="False" label="Trait columns" help="Multi-select list - hold the appropriate key while clicking to select multiple columns." /> + <param name="conts" type="data_column" data_ref="input" multiple="True" numerical="False" optional="True" label="Continuous- and date-valued columns for binning (if any)" help="Multi-select list. Values should be numbers." /> + <param name="dates" type="data_column" data_ref="input" multiple="True" numerical="False" optional="True" label="Date-valued columns for binning (if any)" help="Multi-select list. Dates should be M/D/Y, where M, D, and Y are all integers (e.g., 7/9/1985)." /> + <param name="bins" type="text" size="40" label="Bin sizes (for continuously-valued columns)" value="5" optional="False" help="Set to a single number, or a comma-delimited list. If given as a list, will be used in same order as continuous columns."/> + </inputs> + <outputs> + <data format="tabular" name="out" /> + </outputs> + <help> + +**Purpose** + +This tool computes the score for a completed study randomization (e.g., by ARTS) for a selected number of traits over the samples in your data, and a particular column giving the batch assignments. The output here is identical to the stdout obtained from a standard ARTS run. + +----- + +**Input traits per sample** + +- A list of traits associated with each sample, including a header line giving the name of each type of trait, and a batch column. For example:: + + ID Sex Age Sample date Diseased Batch + Sample1 M 15 6/7/2011 Y 1 + Sample2 M 25 8/5/2012 Y 2 + Sample3 F 23 1/30/2012 N 1 + Sample4 F 45 4/1/2013 N 1 + Sample5 M 52 3/21/2011 Y 2 + Sample6 F 37 3/12/2013 N 2 + Sample7 M 31 7/17/2011 N 2 + +----- + +**Batch column to use** + +- Which column indicate the batch assignment. In the example above, this would be c6 (batch). + +----- + +**Traits to randomize** + +- Which traits should be randomized. On Macs, hold command to multi-select. You do not need to select all columns (it would be silly, for example, to randomize over sample ID). + +- Note missing values for traits will be treated as an additional trait value (i.e., empty). + +- For the example above, we would select c2, c3, c4, and c5 (Sex, Age, Sample date, and Diseased). Not all traits need be selected, just the relevant ones (we may not care about Sample date, for example). + +----- + +**Continuous- and date-valued columns (optional)** + +- Use if you have columns with continuous values (e.g., age, blood pressure) or dates. They will be discretized prior to running. + +- For the example above, we would select c3 and c4 (Age, Sample date). + +----- + +**Date-valued columns (optional)** + +- Use if any of the columns selected as continuous are dates (MUST be formatted M/D/Y, where month is a number, for example 7/9/1985). + +- For the example above, we would select c4 (Sample date). + +----- + +**Bin sizes** + +- This only relates to any columns selected as continuous, and determines how many discrete bins the data will be split up in to. + +- You can set it to a single number, and all columns will use that number of bins. Or you can set it to a list of numbers to specify a different number of bins for each column. + +- For the example above, where we selected c3 and c4 as continuous, we could set:: + + Bin sizes=5,6 + +- which would split the Age column (c3) into 5 bins, and the Sample date column (c4) into 6 bins. + + +</help> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ARTS/sample_data.txt Wed Nov 13 16:13:17 2013 -0500 @@ -0,0 +1,31 @@ +Sample ID Age Sex Collection date Disease +sample1 25 M 3/28/2012 Y +sample2 37 F 4/27/2013 N +sample3 36 F 3/10/2013 N +sample4 52 M 7/1/2012 Y +sample5 48 M 8/13/2011 Y +sample6 60 M 9/21/2011 N +sample7 31 F 10/22/2010 Y +sample8 28 F 1/15/2010 N +sample9 26 M 1/7/2012 N +sample10 44 F 4/5/2012 Y +sample11 33 M 5/18/2012 N +sample12 25 F 7/27/2013 N +sample13 28 M 1/20/2013 Y +sample14 30 F 8/11/2012 Y +sample15 51 M 11/23/2011 N +sample16 22 M 12/21/2011 N +sample17 28 M 9/26/2010 Y +sample18 19 F 1/18/2010 Y +sample19 35 M 2/10/2012 N +sample20 38 F 2/17/2012 N +sample21 25 F 4/28/2012 Y +sample22 55 M 1/7/2013 Y +sample23 33 F 6/30/2013 N +sample24 24 M 7/1/2012 Y +sample25 42 M 2/15/2011 N +sample26 60 M 5/21/2011 N +sample27 34 F 10/23/2010 Y +sample28 37 F 12/18/2010 Y +sample29 41 F 11/7/2012 N +sample30 50 F 2/15/2012 Y