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annotate w4mkmeans.xml @ 2:c415b7dc6f37 draft default tip

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planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit 3e916537da6bb37e6f3927d7a11e98e0ab6ef5ec
author eschen42
date Mon, 05 Mar 2018 12:40:17 -0500
parents 02cafb660b72
children
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c415b7dc6f37 planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit 3e916537da6bb37e6f3927d7a11e98e0ab6ef5ec
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1 <tool id="w4mkmeans" name="Kmeans for W4m" version="0.98.4">
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2 <description>Calculate K-means for W4m dataMatrix features or samples</description>
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4 <requirements>
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5 <requirement type="package" version="3.4.1">r-base</requirement>
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6 <requirement type="package" version="1.1_4">r-batch</requirement>
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7 <requirement type="package" version="1.8.0">libssh2</requirement>
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8 <requirement type="package" version="1.13.2">krb5</requirement>
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9 </requirements>
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11 <stdio>
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12 <exit_code range="1:" level="fatal" />
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13 </stdio>
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16 <command detect_errors="aggressive"><![CDATA[
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17 Rscript $__tool_directory__/w4mkmeans_wrapper.R
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18 tool_directory $__tool_directory__
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19 algorithm '$algorithm'
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20 categorical_prefix '$categoricalPrefix'
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21 data_matrix_path '$dataMatrix_in'
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22 iter_max '$iter_max'
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23 kfeatures '$kfeatures'
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24 ksamples '$ksamples'
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25 nstart '$nstart'
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26 sampleMetadata_out '$sampleMetadata_out'
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27 sample_metadata_path '$sampleMetadata_in'
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28 scores_out '$scores_out'
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29 slots "\${GALAXY_SLOTS:-1}"
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30 variableMetadata_out '$variableMetadata_out'
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31 variable_metadata_path '$variableMetadata_in'
0
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32 ]]></command>
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33
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34 <inputs>
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35 <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="Feature (variable) x sample; decimal point: '.'; missing: NA; mode: numerical; separator: tab" />
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36 <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="Sample x metadata columns; separator: tab" />
c415b7dc6f37 planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit 3e916537da6bb37e6f3927d7a11e98e0ab6ef5ec
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37 <param name="variableMetadata_in" label="Variable (feature) metadata file" type="data" format="tabular" help="Feature (variable) x metadata columns; separator: tab" />
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38 <param name="categoricalPrefix" label="Prefix for cluster names " type="text" value="c" help="String prepended to cluster numbers in output; default 'c'; leave blank for no prefix." />
0
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39 <param name="ksamples" label="K value(s) for samples" type="text" value = "0" help="[ksamples] Single K or comma-separated Ks for samples, or 0 for none." />
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40 <param name="kfeatures" label="K value(s) for features" type="text" value = "0" help="[kfeatures] Single K or comma-separated Ks for features (variables), or 0 for none." />
2
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41 <param name="iter_max" label="Maximum number of iterations" type="text" value = "20" help="[iter_max] The maximum number of iterations allowed; default 20." />
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42 <param name="nstart" label="Number of random sets of clusters" type="text" value = "20" help="[nstart] How many random sets of clusters should be chosen initially; default 20." />
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43 <param name="algorithm" label="Algorithm for clustering" type="select" value = "Hartigan-Wong" help="[algorithm] K-means clustering algorithm, default 'Hartigan-Wong'; &lt;br /&gt;alternatives 'Lloyd', 'MacQueen'; 'Forgy' (synonym for 'Lloyd'); see references.">
0
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44 <option value="Forgy">Forgy</option>
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45 <option value="Hartigan-Wong" selected="True">Hartigan-Wong</option>
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46 <option value="Lloyd">Lloyd</option>
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47 <option value="MacQueen">MacQueen</option>
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48 </param>
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49 </inputs>
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50
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51 <outputs>
2
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52 <data name="sampleMetadata_out" label="${sampleMetadata_in.name}.kmeans-smpl" format="tabular" ></data>
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53 <data name="variableMetadata_out" label="${variableMetadata_in.name}.kmeans-vrbl" format="tabular" ></data>
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54 <data name="scores_out" label="${dataMatrix_in.name}.kmeans-score" format="tabular" ></data>
0
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55 </outputs>
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56
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57 <tests>
1
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58 <test>
0
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59 <param name="dataMatrix_in" value="input_dataMatrix.tsv"/>
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60 <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/>
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61 <param name="variableMetadata_in" value="input_variableMetadata.tsv"/>
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62 <param name="ksamples" value="3,4"/>
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63 <param name="kfeatures" value="5,6,7"/>
2
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64 <param name="iter_max" value="20"/>
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65 <param name="nstart" value="20"/>
0
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66 <param name="algorithm" value="Hartigan-Wong"/>
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67 <output name="scores_out">
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68 <assert_contents>
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69 <has_text text="proportion" />
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70 <has_text text="0.87482" />
2
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71 <has_text text="0.91765" />
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72 <has_text text="0.95362" />
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73 <has_text text="0.95719" />
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74 <has_text text="0.97966" />
0
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75 </assert_contents>
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76 </output>
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77 </test>
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78 </tests>
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79
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80 <help>
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81 <![CDATA[
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82
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83 ===========================
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84 K-means for W4m data matrix
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85 ===========================
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86
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87 **Author** - Arthur Eschenlauer (University of Minnesota, esch0041@umn.edu)
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88
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89 **Source Code** - The source code for the w4mkmeans tool is available (from the Hegeman lab github repository) at https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper
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90
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91 **R code used** - The R code invoked by this wrapper is the R kmeans package at https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html
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92
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93 **Tool updates** - See the **NEWS** section at the bottom of this page
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94
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95 -----------
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96 Description
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97 -----------
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98
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99 This tool calculate K-means clusters for samples, features, or both using W4m dataMatrix (i.e., XCMS-preprocessed data files) as input and writes the results to new columns in sampleMetadata, variableMetadata, or both.
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100
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101 - If several, comma-separated K's are supplied, then one column is added for each K.
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102 - For feature-clustering, each feature is assigned to a cluster such that the feature's response for all samples is closer to the mean of all features for that cluster than to the mean for any other cluster.
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103 - For sample-clustering, each sample is assigned to a cluster such that the sample's response for all features is closer to the mean of all samples for that cluster than to the mean for any other cluster.
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104 - Clustering is mutually exclusive, **not** hierarchical.
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105
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106 - Hierarchical clustering is available through the W4m Heat Map tool, https://github.com/workflow4metabolomics/heatmap
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107
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108 -----------------
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109 Workflow Position
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110 -----------------
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111
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112 - Tool category: Statistical Analysis
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113 - Upstream tool category: Preprocessing
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114 - Downstream tool categories: Statistical Analysis
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115
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116 -----------
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117 Input files
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118 -----------
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119
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120 +--------------------------------------------+------------+
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121 | File | Format |
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122 +============================================+============+
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123 | Data matrix | tabular |
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124 +--------------------------------------------+------------+
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125 | Sample metadata | tabular |
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126 +--------------------------------------------+------------+
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127 | Variable (i.e., feature) metadata | tabular |
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128 +--------------------------------------------+------------+
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129
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130
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131 ----------
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132 Parameters
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133 ----------
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134
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135 **Data matrix** - input-file dataset
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136
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137 - W4m variable (i.e. feature) x sample 'dataMatrix' (tabular separated values) file of the numeric data matrix, with . as decimal, and NA for missing values; the table must not contain metadata apart from row and column names; the row and column names must be identical to the rownames of the sample and feature metadata, respectively (see below)
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138
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139 **Sample metadata** - input-file dataset
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140
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141 - W4m sample x metadata 'sampleMetadata' (tabular separated values) file of the numeric and/or character sample metadata, with . as decimal and NA for missing values
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142
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143 **Feature metadata** - input-file dataset
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144
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145 - W4m variable (i.e. feature) x metadata 'variableMetadata' (tabular separated values) file of the numeric and/or character feature metadata, with . as decimal and NA for missing values
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146
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147 **Prefix for cluster names** - character(s) to add as prefix to category number (default = 'c')
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148
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149 - Some tools treat only non-numeric data as categorical; this prefix ensures that clusters data will be treated as categorical; an empty string is permitted here if desired (and succeeding tools requiring categorical data accept integers).
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150
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151 **K-values for samples** - K or K-range for samples (default = 0)
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152
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153 - Integer or comma-separated positive integers ; zero (or less) will result in no calculation.
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154
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155 **K-values for features** - K or K's for features (default = 0)
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156
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157 - Integer or comma-separated positive integers ; zero (or less) will result in no calculation.
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158
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159 **Maximumn number of iterations** - (default = 20)
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160
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161 - Maximum number of iterations per calculation (see https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html for further info).
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162
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163 **Number of random sets** - how many random sets should be chosen to start (default = 20)
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164
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165 - Number of random sets of clusters to be chosen to start calculation (see https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html for further info).
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166
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167 **Algorithm** - Algorithm for clustering" (default = Hartigan-Wong)
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168
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169 - K-means clustering algorithm: 'Hartigan-Wong', 'Lloyd', or 'MacQueen'; 'Forgy' is a synonym for 'Lloyd' (see references for further info).
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170
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171 ------------
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172 Output files
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173 ------------
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174
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175 **Sample Metadata** - (tabular separated values) file identical to the Sample metadata file given as an input argument, excepting one column added for each K
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176
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177 - **k#** - cluster number for clustering samples with K = #
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178
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179 **Variable Metadata** - (tabular separated values) file identical to the Feature metadata file given as an input argument, excepting one column added for each K
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180
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181 - **k#** - cluster number for clustering features with K = #
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182
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183 **scores** - (tabular separated values) file with one line for each K.
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184
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185 - **clusterOn** - what was clustered - either 'sample' or 'feature'
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186 - **k** - the chosen K for clustering
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187 - **totalSS** - total (*between-treatements* plus total of *within-treatements*) sum of squares
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188 - **betweenSS** - *between-treatements* sum of squares
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189 - **proportion** - betweenSS / totalSS
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190
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191 ---------------
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192 Working example
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193 ---------------
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194
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195 .. class:: infomark
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196
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197 **Input files**
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198
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199 +-------------------------------------------------------------------------------------------------------------------+
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200 | URL |
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201 +===================================================================================================================+
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202 | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_dataMatrix.tsv |
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203 +-------------------------------------------------------------------------------------------------------------------+
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204 | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_sampleMetadata.tsv |
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205 +-------------------------------------------------------------------------------------------------------------------+
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206 | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_variableMetadata.tsv |
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207 +-------------------------------------------------------------------------------------------------------------------+
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208
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209 .. class:: infomark
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210
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211 **Other input parameters**
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212
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213 +-----------------+---------------+
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214 | Input Parameter | Value |
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215 +=================+===============+
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216 | prefix | c |
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217 +-----------------+---------------+
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218 | ksamples | 3,4 |
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219 +-----------------+---------------+
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220 | kfeatures | 5,6,7 |
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221 +-----------------+---------------+
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222 | iter_max | 20 |
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223 +-----------------+---------------+
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224 | nstart | 20 |
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225 +-----------------+---------------+
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226 | algorithm | Hartigan-Wong |
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227 +-----------------+---------------+
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228
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229 .. class:: infomark
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230
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231 **Expected output files**
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232
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233 +-------------------------------------------------------------------------------------------------------------------+
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234 | URL |
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235 +===================================================================================================================+
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236 | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/output_kmeans-score.tsv |
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237 +-------------------------------------------------------------------------------------------------------------------+
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238 | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/output_kmeans-vrbl.tsv |
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239 +-------------------------------------------------------------------------------------------------------------------+
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240 | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/output_kmeans-smpl.tsv |
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241 +-------------------------------------------------------------------------------------------------------------------+
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242
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243 ----
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244 NEWS
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245 ----
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246
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247 - February 2018, Version 0.98.4 - Renamed output datasets to append '``.kmeans-smpl``', '``.kmeans-vrbl``', or '``.kmeans-score``'; refactored multi-threading.
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248 - August 2017, Version 0.98.3 - Add (optional) prefix to category numbers for downstream tools that treat only non-numeric data as categorical.
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249 - August 2017, Version 0.98.1 - First release
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250
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251 ---------
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252 Citations
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253 ---------
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254
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255 ]]>
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256 </help>
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257 <citations>
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258 <citation type="bibtex"><![CDATA[
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259 @incollection{RCoreTeam2017,
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260 title = {stats::kmeans - K-Means Clustering},
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261 booktitle = {R: A Language and Environment for Statistical Computing},
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262 author = {{R Core Team}},
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263 publisher = {R Foundation for Statistical Computing},
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264 address = {Vienna, Austria},
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265 year = {2017},
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266 url = {https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html},
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267 }
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268 ]]></citation>
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269 <!-- Forgy algorithm -->
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270 <citation type="bibtex"><![CDATA[
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271 @article{forgy65,
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272 added-at = {2006-03-23T12:22:43.000+0100},
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273 author = {Forgy, E.},
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274 biburl = {https://www.bibsonomy.org/bibtex/21e31409932ce91df646c4731350e1207/hotho},
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275 interhash = {c86383cba8cfe00d5e6ef200016aca3f},
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276 intrahash = {1e31409932ce91df646c4731350e1207},
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277 journal = {Biometrics},
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278 keywords = {clustering kmeans},
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279 number = 3,
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280 pages = {768-769},
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281 timestamp = {2006-03-23T12:22:43.000+0100},
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282 title = {Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classification},
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283 volume = 21,
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284 year = 1965
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285 }
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286 ]]></citation>
2
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287 <!-- W4m 3.0 - Guitton et al. 2017-->
0
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288 <citation type="doi">10.1016/j.biocel.2017.07.002</citation>
2
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289 <!-- W4m 2.5 - Giacomini et al. 2014 -->
0
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290 <citation type="doi">10.1093/bioinformatics/btu813</citation>
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291 <!-- Hartigan and Wong algorithm -->
2
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292 <citation type="doi">10.2307/2346830</citation>
0
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293 <!-- Lloyd algorithm -->
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294 <citation type="doi">10.1109/TIT.1982.1056489</citation>
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295 <!-- MacQueen algorithm -->
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296 <citation type="bibtex"><![CDATA[
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297 @inproceedings{MacQueen1967,
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298 added-at = {2011-01-11T13:35:01.000+0100},
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299 author = {MacQueen, J. B.},
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300 biburl = {https://www.bibsonomy.org/bibtex/25dcdb8cd9fba78e0e791af619d61d66d/enitsirhc},
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301 booktitle = {Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability},
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302 editor = {Cam, L. M. Le and Neyman, J.},
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303 interhash = {8d7d4dfe7d3a06b8c9c3c2bb7aa91e28},
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304 intrahash = {5dcdb8cd9fba78e0e791af619d61d66d},
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305 keywords = {kmeans clustering},
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306 pages = {281-297},
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307 publisher = {University of California Press},
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308 timestamp = {2011-01-11T13:35:01.000+0100},
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309 title = {Some Methods for Classification and Analysis of MultiVariate Observations},
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310 volume = 1,
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311 year = 1967
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312 }
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313 ]]></citation>
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314 </citations>
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315 <!--
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316 vim:et:sw=2:ts=2:
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317 --> </tool>