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1 ARTS: Automated Randomization of multiple Traits for Study design
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2 Written by Mark Maienschein-Cline
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3 mmaiensc@gmail.com
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4 Center for Research Informatics
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5 University of Illinois at Chicago
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6
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7 ARTS uses a genetic algorithm to optimize (minimize) a mutual information-based objective function, obtaining
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8 an optimal randomization for studies of arbitrary size and design.
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9
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10 The publication for this code is in preparation; citation to be added soon (hopefully!). When it is published,
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11 the section of the supplementary information will give more details about usage (in addition to what's below).
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12
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13 Please contact me at the email above with questions.
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14
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15
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16
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17 There are two ways of using this code: command-line (it's a perl script), or through Galaxy.
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18
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19 You can learn about, and download, Galaxy at http://galaxyproject.org.
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20
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21 ################
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22 # INSTALLATION #
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23 ################
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24
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25 #
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26 # Command line version:
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27 #
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28 No installation needed, as long as you have a perl interpreter. Should work fine on a Mac or Linux system;
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29 probably fine on Windows, but I haven't tested it.
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30
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31 #
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32 # Galaxy version:
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33 #
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34 Two options:
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35 1) You can download this tool from the Galaxy toolshed directly into your installation.
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36 2) Move the ARTS.pl and .xml files into tools/ in your Galaxy distribution, and edit the tool_config file
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37 appropriately. If you don't know how to do this, you should probably use strategy #1.
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38
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39 ###########
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40 # RUNNING #
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41 ###########
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42
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43 #
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44 # Galaxy version
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45 #
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46 Once you get the tools installed in Galaxy, there are help sections in the tool descriptions you can refer to.
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47 Also refer to the instructions for the command-line version below.
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48
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49 #
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50 # Command line version:
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51 #
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52
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53 Run ARTS.pl without any inputs to see the usage. All inputs are specified using the usual [-flag] [value]
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54 syntax (i.e., -i input.txt).
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55
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56 Sample command using the sample_data.txt file:
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57 ./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
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58
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59
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60 More information about the inputs (*'ed remarks refer to the values in the sample command above):
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61
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62 -i Input trait table: tab-delimited table, including 1 header line. See sample_data.txt for an example.
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63 You can prepare this table in Excel and save as a tab-delimited text, or just write it in a text file,
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64 or copy-paste from Excel to a text file. You can have more columns than you will actually care about
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65 randomizing here.
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66 * You can use the file sample_data.txt as an example input; there are 5 columns, Sample ID, Age, Sex,
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67 Collection Date, and Disease.
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68
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69 -c Trait columns to randomize. This is a comma- and semicolon-delimited list. Its syntax is important,
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70 so pay attention.
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71 Columns are numbered starting from 1. Traits that should be considered jointly should be listed together
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72 separated by commas. Each set of jointly considered traits should be listed separated by semicolons. Hence,
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73 * -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
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74 to consider each trait individually (that's the ;2;3;4;5 part).
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75 You could opt to only consider traits individually (-c "2;3;4;5"), or only jointly (-c "2,3,4,5"), or only
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76 pair-wise (-c "2,3;2,4;2,5;3,4;3,5;4,5"), or whatever you want.
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77 OUR GENERAL-PURPOSE RECOMMENDATION is to consider all traits jointly, plus all individually, as in the sample
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78 command. This corresponds to the MMI statistic discussed in the publication.
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79 GALAXY USERS: you just get to select the columns to consider, and the script will use the MMI statistic
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80 automatically (you don't get a choice).
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81 FINAL NOTE: you should put quotes around the value here, since otherwise semicolons will be interpreted
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82 as end-of-line characters.
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83
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84 -b Batch size (number of samples that can be processed at the same time). You have two options:
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85 1) Enter a single number. This will fill as many complete batches as possible, and put the remainder into a smaller
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86 batch. This is probably convenient, but you should do a quick count to make sure you don't end up with a really
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87 small last batch (e.g., if you have 105 samples and do batch size of 25, your last batch will only have 5 samples).
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88 2) Enter a comma-delimited list that adds up to the number of samples, which allows for uneven batch sizes
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89 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.
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90 * sample_data.txt has 30 samples, so "-b 10" makes 3 batches of 10 samples each.
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91
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92 -o Output file. Self-explanatory. The batch assignments are added as an extra column on the end, otherwise looks
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93 like the input.
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94 * batched_data.txt is our output file.
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95
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96 -p (sort-of optional: you MUST use both -b and -o, OR just -p) Print (to STDOUT) the statistics of a batched
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97 run using this column. The result will look like the last part of the STDOUT from an ARTS run (see below),
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98 but you can use this option for testing batch assignments from another algorithm, or if you did one by hand.
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99
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100 -cc Indices of continuously-valued columns. ARTS uses discrete values for its statistics, so these columns must
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101 be discretized (binned). If ARTS encounters a column with more than 20 values, it will generate a warning asking
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102 if you want it to be continuous. Comma-delimited list.
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103 * In sample_data.txt, columns 2 (age) and 4 (date) could be considered continuous (that is, it's worth treating
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104 a 35 year-old similarly to a 36 year-old), so we set "-cc 2,4".
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105
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106 -cd Date-valued columns. These columns should also be listed under -cc, but this lets ARTS know to expect a date
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107 (format MUST be M/D/Y, where month is a number (1 instead of January)) and convert the date to a number before
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108 binning.
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109 * In sample_data.txt, column 4 is a date, so set "-cd 4".
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110
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111 -cb Number of bins to use for discretizing the continuous columns. Again, you can set a single value, or give a comma-
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112 delimited list, which will match the order of the list given in the -cc flag.
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113 * For the sample run, we left the default value of 5, but we could do, for example, "-cb 5,7", which would bin
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114 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).
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115
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116 -bn Name for the batch column added to the output. Default is "batch".
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117
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118 -s Random number seed. Set as a large negative integer. The code always uses the same seed, but if you want to
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119 rerun with a different seed you can use this option.
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120
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121 ----------------------------------------------
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122
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123 When you run the sample command, the STDOUT looks like this (I added the N) line numbers):
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124
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125 """""""""""""""""""
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126 1) Using traits: Age Sex Collection date Disease
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127 2) Using trait combinations: {Age,Sex,Collection date,Disease} {Age} {Sex} {Collection date} {Disease}
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128 3) Generation 1 of 300, average fitness 0.1432
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129 4) Generation 2 of 300, average fitness 0.1342
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130 5) Generation 3 of 300, average fitness 0.1298
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131 6) Generation 4 of 300, average fitness 0.1279
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132 7) Generation 5 of 300, average fitness 0.1250
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133 8) Generation 6 of 300, average fitness 0.1227
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134 9) Generation 7 of 300, average fitness 0.1211
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135 10) Generation 8 of 300, average fitness 0.1194
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136 11) Generation 9 of 300, average fitness 0.1187
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137 12) Generation 10 of 300, average fitness 0.1181
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138 13) Generation 11 of 300, average fitness 0.1175
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139 14) Generation 12 of 300, average fitness 0.1165
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140 15) Generation 13 of 300, average fitness 0.1143
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141 16) Generation 14 of 300, average fitness 0.1133
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142 17) Generation 15 of 300, average fitness 0.1132
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143 18) Generation 16 of 300, average fitness 0.1127
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144 19) Generation 17 of 300, average fitness 0.1123
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145 20) Generation 18 of 300, average fitness 0.1116
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146 21) Generation 19 of 300, average fitness 0.1119
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147 22) Generation 20 of 300, average fitness 0.1113
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148 23) Generation 21 of 300, average fitness 0.1113
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149 24) Generation 22 of 300, average fitness 0.1110
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150 25) Generation 23 of 300, average fitness 0.1110
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151 26) Final MI 0.1045 ; Individual trait MIs (mean 0.0091 ): 0.0155 0.0000 0.0209 0.0000
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152 27) -----------------------------------------------------------------
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153 28) Age values Sex values Collection date values Disease values
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154 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
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155 30) ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- -------
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156 31) 1 (10) 2 2 2 1 3 5 5 3 2 2 2 1 5 5
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157 32) 2 (10) 2 2 1 2 3 5 5 2 2 4 1 1 5 5
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158 33) 3 (10) 3 2 1 1 3 5 5 3 2 2 2 1 5 5
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159 34) ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- -------
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160 35) Total 7 6 4 4 9 15 15 8 6 8 5 3 15 15
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161 """""""""""""""""""
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162
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163 Here's what the lines mean:
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164 1) Tells you what traits you've selected.
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165 2) Tells you what trait combinations you've selected.
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166 3-25) Prints the progress for each generation of the GA. Converges when average fitness changes by less than 0.0001.
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167 26) Final objective function value. Normalized between 0 and 1, ideal case is 0. Note that different choices of the
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168 objective function ARE NOT COMPARABLE: if you select fewer traits, or simpler combinations of traits (fewer
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169 joint traits) using different -c values, you will get lower MI values, but this does not necessarily indicate better
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170 overall randomization, because your choices may be overly simplistic. This is why we recommend sticking with the
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171 MMI definition (all joint + all individual) consistently. This line also gives the randomization values for all
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172 individual traits.
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173 27-24) Inividual trait counts per batch for different values. Continuously-valued columns are given as a range
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174 (e.g., age 19-27.2).
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175 35) Total number of traits in each bin over all samples.
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176
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177 ----------------------------------------------
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178
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179 The output, batched_data.txt, will look like this:
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180
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181 """""""""""""""""""
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182 Sample ID Age Sex Collection date Disease batch
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183 sample1 25 M 3/28/2012 Y 3
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184 sample2 37 F 4/27/2013 N 3
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185 sample3 36 F 3/10/2013 N 1
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186 sample4 52 M 7/1/2012 Y 1
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187 sample5 48 M 8/13/2011 Y 3
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188 sample6 60 M 9/21/2011 N 3
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189 sample7 31 F 10/22/2010 Y 3
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190 sample8 28 F 1/15/2010 N 2
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191 sample9 26 M 1/7/2012 N 1
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192 sample10 44 F 4/5/2012 Y 1
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193 sample11 33 M 5/18/2012 N 3
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194 sample12 25 F 7/27/2013 N 3
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195 sample13 28 M 1/20/2013 Y 2
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196 sample14 30 F 8/11/2012 Y 3
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197 sample15 51 M 11/23/2011 N 2
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198 sample16 22 M 12/21/2011 N 2
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199 sample17 28 M 9/26/2010 Y 1
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200 sample18 19 F 1/18/2010 Y 3
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201 sample19 35 M 2/10/2012 N 1
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202 sample20 38 F 2/17/2012 N 2
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203 sample21 25 F 4/28/2012 Y 1
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204 sample22 55 M 1/7/2013 Y 2
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205 sample23 33 F 6/30/2013 N 1
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206 sample24 24 M 7/1/2012 Y 2
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207 sample25 42 M 2/15/2011 N 3
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208 sample26 60 M 5/21/2011 N 1
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209 sample27 34 F 10/23/2010 Y 2
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210 sample28 37 F 12/18/2010 Y 1
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211 sample29 41 F 11/7/2012 N 2
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212 sample30 50 F 2/15/2012 Y 2
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213 """""""""""""""""""
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214
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215 Looks the same as the input file, with a sixth column titled "batch" added, saying which of the three
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216 batches each sample should be processed in (of course, you can permute the order of batches if you want).
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217
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218 Included file batched_data.txt is what the output should look like.
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219
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