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1 <tool id="hmm_1" name="Fit HMM " version="1.0.0">
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2 <description>on numeric data</description>
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3 <command interpreter="bash">\$R_SCRIPT_PATH/r_wrapper.sh $script_file</command>
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4
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5 <inputs>
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6 <param name="input" type="data" format="tabular" label="Dataset"/>
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7 <param name="var_cols" label="Select columns containing observations " type="data_column" data_ref="input" numerical="True" multiple="true" >
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8 <validator type="no_options" message="Please select at least one column."/>
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9 </param>
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10 <param name="samp_col" label="Select column containing sample numbers " type="data_column" data_ref="input" numerical="True" multiple="false" >
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11 <validator type="no_options" message="Please select a column."/>
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12 </param>
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13 <param name="header" type="select" label="Treat first line as header? ">
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14 <option value="yes" selected="true">Yes</option>
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15 <option value="no">No</option>
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16 </param>
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17 <param name="nStates" size="10" type="integer" value="2" label="Number of hidden states " />
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18 <conditional name="disChoice">
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19 <param name="dis" type="select" label="Distribution">
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20 <option value="NORMAL" selected="true">Normal</option>
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21 <option value="DISCRETE">Discrete</option>
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22 <option value="MIXTURE">Mixture</option>
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23 </param>
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24 <when value="NORMAL" />
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25 <when value="DISCRETE" />
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26 <when value="MIXTURE">
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27 <param name="nMixt" size="10" type="integer" value="2" label="Number of mixtures of normal distributions " />
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28 </when>
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29 </conditional>
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30 <!--
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31 <conditional name="asymptChoice">
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32 <param name="asymptCov" type="select" label="Compute asymptotic covariance matrix? ">
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33 <option value="FALSE" selected="true">No</option>
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34 <option value="TRUE">Yes</option>
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35 </param>
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36 <when value="FALSE" />
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37 <when value="TRUE">
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38 <param name="asymptMethod" type="select" label="Method for computing asymptotic covariance matrix ">
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39 <option value="nlme" selected="true">nlme</option>
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40 <option value="optim">optim</option>
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41 </param>
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42 </when>
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43 </conditional>
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44 -->
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45 </inputs>
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46
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47 <configfiles>
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48 <configfile name="script_file">
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49 ## Setup R error handling to go to stderr
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50 options( show.error.messages=F,
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51 error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) },
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52 warn = -1 )
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53 suppressPackageStartupMessages(library('RHmm'))
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54
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55 #if str($header) == "yes"
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56 inp = read.table( "${input.file_name}", header=T )
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57 #else
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58 inp = read.table( "${input.file_name}", header=F )
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59 #end if
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60
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61 samp_numbers = unique(inp[, ${samp_col}])
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62
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63 if (length(samp_numbers) == 1){
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64 samp_list = inp[,c(${var_cols})]
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65 } else {
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66 samp_list=list()
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67 for (i in 1:length(samp_numbers)) {
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68 samp_list[[i]] = inp[(inp[,${samp_col}] == samp_numbers[i]),c(${var_cols})]
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69 }
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70 }
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71
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72 nStates = ${nStates}
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73 dis = "$disChoice['dis']"
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74 nMixt = 0
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75
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76 #if $disChoice['dis'] == "MIXTURE"
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77 nMixt = ${disChoice.nMixt}
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78 #end if
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79
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80 ##asymptCov = $asymptChoice['asymptCov']
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81 asymptCov = "FALSE"
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82 asymptMethod = "nlme"
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83
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84 ##if (asymptCov == "TRUE") {
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85 ## asymptMethod = "${asymptChoice.asymptMethod}"
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86 ##}
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87
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88 #if $disChoice['dis'] == "MIXTURE"
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89 if (asymptCov == "TRUE") {
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90 myfit = HMMFit(samp_list, nStates=nStates, dis=dis, nMixt=nMixt, asymptCov=asymptCov, asymptMethod=asymptMethod)
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91 } else {
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92 myfit = HMMFit(samp_list, nStates=nStates, dis=dis, nMixt=nMixt)
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93 }
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94 #else
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95 if (asymptCov == "TRUE") {
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96 myfit = HMMFit(samp_list, nStates=nStates, dis=dis, asymptCov=asymptCov, asymptMethod=asymptMethod)
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97 } else {
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98 myfit = HMMFit(samp_list, nStates=nStates, dis=dis)
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99 }
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100 #end if
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101
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102 myfittxt=capture.output(myfit)
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103 cat(myfittxt,file="${out_file1}",sep="\n")
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104
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105
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106 samp_list_stateSol = list()
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107 if (length(samp_numbers) == 1){
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108 samp_list_stateSol[[1]]=unlist(viterbi(myfit, samp_list)["states"])
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109 } else {
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110 for (i in 1:length(samp_numbers)) {
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111 samp_list_stateSol[[i]]=unlist(viterbi(myfit, samp_list[[i]])["states"])
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112 }
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113 }
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114 inp_stateSol=cbind(inp,unlist(samp_list_stateSol))
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115 write.table(inp_stateSol,file="${out_file2}",sep="\t",row.names=F,col.names=F,quote=F)
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116
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117 </configfile>
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118 </configfiles>
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119
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120 <outputs>
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121 <data format="txt" name="out_file1" />
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122 <data format="input" name="out_file2" />
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123 </outputs>
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124
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125 <requirements>
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126 <requirement type="set_environment">R_SCRIPT_PATH</requirement>
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127 <requirement type="package" version="2.15.0">R</requirement>
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128 </requirements>
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129
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130 <help>
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131
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132 .. class:: infomark
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133
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134 **What it does**
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135
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136 This tool uses the 'HMMFit' and 'viterbi' functions from 'RHmm' library from R statistical package to fit an Hidden Markov Model using Baum-Welch algorithm, and calculate the optimal hidden states sequence using Viterbi's algorithm.
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137
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138 It returns two outputs - one containing summary statistics for HMMFit, and the other containing state numbers appended as a new column to the input data.
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139
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140 *Ollivier TARAMASCO and Sebastian Bauer (2010). RHmm: Hidden Markov Models simulations and estimations. R package version 1.4.4. http://CRAN.R-project.org/package=RHmm.*
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141
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142 -----
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143
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144 .. class:: warningmark
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145
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146 **Note**
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147
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148 The tool fails if any of the observation columns contain non-numeric data.
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149
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150
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151 </help>
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152 </tool>
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