Mercurial > repos > artbio > gsc_high_dimensions_visualisation

annotate high_dim_visu.xml @ 8:fe6f76030168 draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/gsc_high_dimension_visualization commit a3dc683410fc240f428c8fbee3c63aa9965fbf38
author artbio
date Wed, 29 Nov 2023 17:28:18 +0000
parents 18a1dc4aec4a
children 58aa18e1fe14
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8
fe6f76030168 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/gsc_high_dimension_visualization commit a3dc683410fc240f428c8fbee3c63aa9965fbf38
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1 <tool id="high_dimensions_visualisation" name="Generate PCA, tSNE and HCPC" version="4.3+galaxy0" profile="20.01">
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2 <description>from highly dimensional expression data</description>
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3 <requirements>
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4 <requirement type="package" version="1.7.3=r43hc72bb7e_2">r-optparse</requirement>
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5 <requirement type="package" version="2.9=r43h57805ef_0">r-factominer</requirement>
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6 <requirement type="package" version="1.0.7=r43hc72bb7e_3">r-factoextra</requirement>
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7 <requirement type="package" version="0.16=r43h7ce84a7_2">r-rtsne</requirement>
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8 <requirement type="package" version="0.4.16=r43hc72bb7e_1">r-ggfortify</requirement>
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9 <requirement type="package" version="1.3.1=r43h08d816e_1">r-clusterr</requirement>
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10 <requirement type="package" version="1.5.1=r43hc72bb7e_2">r-polychrome</requirement>
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11 </requirements>
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12 <stdio>
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13 <exit_code range="1:" level="fatal" description="Tool exception" />
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14 </stdio>
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15 <command detect_errors="exit_code"><![CDATA[
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16 Rscript $__tool_directory__/high_dim_visu.R
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17 --data '$input'
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18 #if $factor_condition.factor_choice == 'Yes'
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19 --factor '$factor_condition.factor'
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20 #end if
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21 #if $labels == "yes":
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22 --labels 'TRUE'
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23 #else
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24 --labels 'FALSE'
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25 #end if
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26 --visu_choice '$visualisation.visu_choice'
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27
0
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28 #if $visualisation.visu_choice == "tSNE":
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29 --Rtsne_seed '$visualisation.Rtsne_seed'
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30 --Rtsne_perplexity '$visualisation.Rtsne_perplexity'
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31 --Rtsne_theta '$visualisation.Rtsne_theta'
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32 --Rtsne_max_iter '$visualisation.Rtsne_max_iter'
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33 --Rtsne_dims '$visualisation.Rtsne_dims'
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34 --Rtsne_initial_dims '$visualisation.Rtsne_initial_dims'
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35 --Rtsne_pca '$visualisation.Rtsne_pca'
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36 --Rtsne_pca_center '$visualisation.Rtsne_pca_center'
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37 --Rtsne_pca_scale '$visualisation.Rtsne_pca_scale'
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38 --Rtsne_normalize '$visualisation.Rtsne_normalize'
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39 --Rtsne_exaggeration_factor '$visualisation.Rtsne_exaggeration_factor'
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40 #end if
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41
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42 #if $visualisation.visu_choice == "HCPC":
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43 --HCPC_ncluster '$visualisation.HCPC_ncluster'
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44 --HCPC_npc '$visualisation.HCPC_npc'
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45 --HCPC_metric '$visualisation.HCPC_metric'
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46 --HCPC_method '$visualisation.HCPC_method'
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47 --HCPC_consol '$visualisation.HCPC_consol'
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48 --HCPC_itermax '$visualisation.HCPC_itermax'
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49 --HCPC_min '$visualisation.HCPC_min'
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50 --HCPC_max '$visualisation.HCPC_max'
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51 --HCPC_clusterCA '$visualisation.HCPC_clusterCA'
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52 --HCPC_kk '$visualisation.HCPC_kk'
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53 --HCPC_contributions '$HCPC_contributions'
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54 --HCPC_cell_clust '$HCPC_cell_clust'
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55 #if $factor_condition.factor_choice == "Yes":
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56 --HCPC_mutual_info '$HCPC_mutual_info'
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57 #end if
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58 #end if
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59
0
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60 #if $visualisation.visu_choice == "PCA":
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61 --PCA_npc '$visualisation.PCA_npc'
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62 --x_axis '$visualisation.x_axis'
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63 --y_axis '$visualisation.y_axis'
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64 --item_size '$visualisation.item_size'
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65 #end if
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66 --pdf_out '$pdf_out'
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67 ]]></command>
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68 <inputs>
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69 <param name="input" type="data" format="txt,tabular" label="expression data"/>
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70 <param name="labels" type="select" label="Add sample labels to scatter plot" >
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71 <option value="no" selected="true">No Labels</option>
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72 <option value="yes" >Label points</option>
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73 </param>
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74 <conditional name="factor_condition">
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75 <param label="Do you wish to contrast cells with a factor" name="factor_choice" type="select">
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76 <option value="Yes">Yes</option>
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77 <option value="No" selected="true">No</option>
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78 </param>
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79 <when value="Yes">
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80 <param name="factor" type="data" format="tabular" label="Factor to constrast data"
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81 help="A two-column data frame, with column headers, first column contains data labels,
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82 second column contains the levels of a factor to contrast visualisation" />
0
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83 </when>
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84 <when value="No">
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85 </when>
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86 </conditional>
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87 <conditional name="visualisation">
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88 <param label="Choose visualisation method" name="visu_choice" type="select">
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89 <option value="PCA" selected="True">PCA</option>
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90 <option value="HCPC">HCPC</option>
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91 <option value="tSNE">t-SNE</option>
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92 </param>
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93 <when value="tSNE">
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94 <param name="Rtsne_seed" value="42" type="integer" label="Seed value for reproducibility of t-SNE" help="Set to 42 as default"/>
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95 <param name="Rtsne_dims" value="2" type="integer" label="dims (t-SNE)" help="Output dimensionality (should not be greater than 3)"/>
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96 <param name="Rtsne_pca" type="select" label="pca (t-SNE)" help="Whether an initial PCA step should be performed" >
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97 <option value="TRUE" selected="true">Yes</option>
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98 <option value="FALSE">False</option>
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99 </param>
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100 <param name="Rtsne_initial_dims" value="50" type="integer" label="initial dims (t-SNE)"
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101 help="The number of dimensions that should be retained in the initial PCA step"/>
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102 <param name="Rtsne_pca_center" type="select" label="Centering data" help="Should data be centered before pca is applied?">
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103 <option value="TRUE" selected="true">Yes</option>
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104 <option value="FALSE">False</option>
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105 </param>
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106 <param name="Rtsne_pca_scale" type="select" label="Scalling data" help="Should data be scaled before pca is applied?">
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107 <option value="TRUE">Yes</option>
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108 <option value="FALSE" selected="true">False</option>
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109 </param>
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110 <param name="Rtsne_normalize" type="select" label="Normalisation of data"
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111 help="Should variables (gene expressions) be normalized internally prior to distance calculations?">
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112 <option value="TRUE" selected="true">Yes</option>
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113 <option value="FALSE">False</option>
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114 </param>
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115 <param name="Rtsne_perplexity" value="10.0" type="float" label="perplexity (t-SNE)" help="should be less than ((nbr observations)-1)/3"/>
0
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116 <param name="Rtsne_theta" value="1.0" type="float" label="theta (t-SNE)"/>
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117 <param name="Rtsne_exaggeration_factor" value="12.0" type="float" label="Exageration factor"
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118 help="Exaggeration factor used to multiply the P matrix in the first part of the optimization"/>
0
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119 <param name="Rtsne_max_iter" value="1000" type="integer" label="Number of iterations (default: 1000)"
8
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120 help="The number of iterations that Rtsne executes to improve low dim representation (gradient descent optimization)"/>
0
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121 </when>
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122 <when value="HCPC">
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123 <param name="HCPC_npc" value="5" type="integer" label="Number of principal components to keep"
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124 help="The number of dimensions which are kept for HCPC analysis (default=5)"/>
0
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125 <param name="HCPC_ncluster" value="-1" type="integer" label="Number of clusters in Hierar. Clustering"
5
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126 help="nb.clust - an integer. If 0, the tree is cut at the level the user clicks on (not working in Galaxy). If -1, the tree is
8
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127 automatically cut at the suggested level (see details). If a (positive) integer, the tree is cut with nb.cluters clusters."/>
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128 <param name="HCPC_metric" type="select" label="Dissimilarity metric"
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129 help="Metric to be used for calculating dissimilarities between observations, can be 'euclidean' or 'manhattan'">
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130 <option value="euclidean" selected="true">euclidean</option>
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131 <option value="manhattan">manhattan</option>
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132 </param>
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133 <param name="HCPC_method" type="select" label="Clustering method"
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134 help="character string defining the clustering method. The four methods implemented are 'average'
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135 ([unweighted pair-]group [arithMetic] average method, aka ‘UPGMA’), 'single' (single linkage), 'complete'
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136 (complete linkage), and 'ward' (Ward's method). The default with this Galaxy tool is is 'ward'.">
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137 <option value="ward" selected="true">ward</option>
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138 <option value="average">average</option>
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139 <option value="single">single</option>
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140 <option value="complete">complete</option>
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141 </param>
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142 <param name="HCPC_consol" type="select" label="k-means consolidation"
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143 help="A boolean. If TRUE, a k-means consolidation is performed (consolidation cannot be performed
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144 if kk is used and equals a number).">
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145 <option value="TRUE" selected="true">Yes</option>
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146 <option value="FALSE">False</option>
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147 </param>
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148 <param name="HCPC_itermax" value="10" type="integer" label="Maximum number of iterations for consolidation"
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149 help="An integer. The maximum number of iterations for the consolidation. (default=10)"/>
0
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150 <param name="HCPC_min" value="3" type="integer" label="min number of clusters"
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151 help="an integer. The least possible number of clusters suggested. (default=3)"/>
0
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152 <param name="HCPC_max" value="-1" type="text" label="max number of clusters"
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153 help="The higher possible number of clusters suggested, by default the minimum between 10
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154 and the number of individuals divided by 2. (default = NULL)"/>
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155 <param name="HCPC_clusterCA" type="select" label="cluster.CA, Clustering against rows or columns"
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156 help="A string equals to 'rows' or 'columns' for the clustering of Correspondence Analysis results.default(rows)">
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157 <option value="rows" selected="true">Rows</option>
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158 <option value="cols">Columns</option>
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159 </param>
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160 <param name="HCPC_kk" value="Inf" type="text" label="kk, Number of clusters used in a Kmeans preprocessing"
5
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161 help="An integer corresponding to the number of clusters used in a Kmeans preprocessing before the
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162 hierarchical clustering; the top of the hierarchical tree is then constructed from this partition.
569334568afa "planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_high_dimension_visualization commit 1b98c85982a2a9f9df4b318f672b9b68cff66a93"
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163 This is very useful if the number of individuals is high. Note that consolidation cannot be performed
569334568afa "planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_high_dimension_visualization commit 1b98c85982a2a9f9df4b318f672b9b68cff66a93"
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164 if kk is different from Inf and some graphics are not drawn. Inf is used by default and no preprocessing
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165 is done, all the graphical outputs are then given."/>
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166 </when>
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167 <when value="PCA">
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168 <param name="PCA_npc" value="5" type="integer" label="Number of principal components to keep"
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169 help="The number of dimensions which are kept for PCA analysis (default=5)"/>
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170 <param name="item_size" value="1" type="float" label="Adjust size of points/labels in PCA graph" help="size of points/labels (default=1)"/>
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171 <param name="x_axis" value="1" type="integer" label="Principal component to plot to x axis" help="PC to plot as x (default=1)"/>
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172 <param name="y_axis" value="2" type="integer" label="Principal component to plot to y axis" help="PCA plot as y (default=2)"/>
0
cad0001b9cfb planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_high_dimension_visualization commit 09dcd74dbc01f448518cf3db3e646afb0675a6fe
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173 </when>
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174 </conditional>
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175 </inputs>
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176 <outputs>
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177 <data name="pdf_out" format="pdf" label="${visualisation.visu_choice} of ${on_string}"/>
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178 <data name="HCPC_cell_clust" format="tabular" label="Clustering table from ${visualisation.visu_choice} of ${on_string}">
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179 <filter>visualisation['visu_choice'] == 'HCPC'</filter>
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180 </data>
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181 <data name="HCPC_contributions" format="tabular" label="Cluster information from ${visualisation.visu_choice}">
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182 <filter>visualisation['visu_choice'] == 'HCPC'</filter>
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183 </data>
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184 <data name="HCPC_mutual_info" format="txt" label="External validation of clustering from ${visualisation.visu_choice} of ${on_string}">
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185 <filter>visualisation['visu_choice'] == 'HCPC' and factor_condition['factor_choice'] == 'Yes'</filter>
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186 </data>
0
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187 </outputs>
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188 <tests>
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189 <!-- test tSNE -->
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190 <test expect_num_outputs="1">
0
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191 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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192 <param name="labels" value="yes" />
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193 <param name="visu_choice" value="tSNE" />
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194 <param name="Rtsne_seed" value="49"/>
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195 <param name="Rtsne_perplexity" value="10"/>
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196 <param name="Rtsne_theta" value="1" />
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197 <output name="pdf_out" file="tsne.1.pdf" ftype="pdf" compare="sim_size" delta="500"/>
0
cad0001b9cfb planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_high_dimension_visualization commit 09dcd74dbc01f448518cf3db3e646afb0675a6fe
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198 </test>
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199 <test expect_num_outputs="1">
0
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200 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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201 <param name="labels" value="no" />
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202 <param name="visu_choice" value="tSNE" />
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203 <param name="factor_choice" value="Yes" />
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204 <param name="factor" value="numeric_factor.tsv" ftype="txt"/>
0
cad0001b9cfb planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_high_dimension_visualization commit 09dcd74dbc01f448518cf3db3e646afb0675a6fe
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205 <param name="Rtsne_seed" value="49"/>
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206 <param name="Rtsne_perplexity" value="10"/>
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207 <param name="Rtsne_theta" value="1" />
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208 <output name="pdf_out" file="tsne.2.pdf" ftype="pdf" compare="sim_size" delta="500"/>
0
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209 </test>
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210 <test expect_num_outputs="1">
0
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211 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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212 <param name="labels" value="yes" />
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213 <param name="visu_choice" value="tSNE" />
cad0001b9cfb planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_high_dimension_visualization commit 09dcd74dbc01f448518cf3db3e646afb0675a6fe
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214 <param name="factor_choice" value="Yes" />
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215 <param name="factor" value="shuffled_factor.tsv" ftype="txt"/>
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216 <param name="Rtsne_seed" value="49"/>
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217 <param name="Rtsne_perplexity" value="10"/>
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218 <param name="Rtsne_theta" value="1" />
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219 <output name="pdf_out" file="tsne.3.pdf" ftype="pdf" compare="sim_size" delta="500"/>
4
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220 </test>
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221 <test expect_num_outputs="1">
4
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222 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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223 <param name="labels" value="no" />
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224 <param name="visu_choice" value="tSNE" />
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225 <param name="Rtsne_seed" value="49" />
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226 <param name="Rtsne_dims" value="3" />
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227 <param name="Rtsne_perplexity" value="10"/>
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228 <param name="Rtsne_theta" value="1" />
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229 <param name="Rtsne_normalize" value="FALSE" />
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230 <output name="pdf_out" file="tsne.4.pdf" ftype="pdf" compare="sim_size" delta="1000"/>
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231 </test>
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232 <!-- test PCA -->
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233 <test expect_num_outputs="1">
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234 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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235 <param name="labels" value="yes" />
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236 <param name="visu_choice" value="PCA" />
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237 <param name="factor_choice" value="No" />
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238 <param name="item_size" value="0.5" />
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239 <output name="pdf_out" file="pca.1.pdf" ftype="pdf"/>
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240 </test>
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241 <!-- test PCA PC2 vs PC3 -->
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242 <test expect_num_outputs="1">
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diff changeset
243 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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244 <param name="labels" value="no" />
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245 <param name="visu_choice" value="PCA" />
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246 <param name="factor_choice" value="No" />
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247 <param name="x_axis" value="2" />
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248 <param name="y_axis" value="3" />
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249 <output name="pdf_out" file="pca.2.pdf" ftype="pdf"/>
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250 </test>
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251 <!-- test factor contrasting on PCA -->
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252 <test expect_num_outputs="1">
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253 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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254 <param name="labels" value="no" />
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255 <param name="visu_choice" value="PCA" />
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256 <param name="factor_choice" value="Yes" />
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257 <param name="factor" value="factor.tsv" ftype="txt"/>
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258 <output name="pdf_out" file="pca.3.pdf" ftype="pdf"/>
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259 </test>
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260 <!-- test numerical factor contrasting on PCA -->
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261 <test expect_num_outputs="1">
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262 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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263 <param name="labels" value="no" />
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264 <param name="visu_choice" value="PCA" />
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265 <param name="factor_choice" value="Yes" />
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266 <param name="factor" value="numeric_factor.tsv" ftype="txt"/>
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267 <output name="pdf_out" file="pca.4.pdf" compare="sim_size" ftype="pdf"/>
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268 </test>
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269 <test expect_num_outputs="1">
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270 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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271 <param name="labels" value="no" />
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272 <param name="visu_choice" value="PCA" />
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273 <param name="factor_choice" value="Yes" />
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274 <param name="factor" value="shuffled_factor.tsv" ftype="txt"/>
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275 <output name="pdf_out" file="pca.5.pdf" compare="sim_size" ftype="pdf"/>
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276 </test>
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277 <!-- HCPC tests -->
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278 <test expect_num_outputs="3">
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279 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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280 <param name="labels" value="yes" />
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281 <param name="visu_choice" value="HCPC" />
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282 <param name="HCPC_npc" value="5"/>
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283 <param name="HCPC_ncluster" value="-1"/>
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284 <output name="pdf_out" file="hcpc.1.pdf" compare="sim_size" ftype="pdf"/>
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285 <output name="HCPC_cell_clust" file="hcpc.cell-cluster.1.tsv" ftype="tabular"/>
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286 <output name="HCPC_contributions" file="hcpc.component-impact.1.tsv" ftype="tabular"/>
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287 </test>
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288 <test expect_num_outputs="4">
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289 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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290 <param name="labels" value="no" />
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291 <param name="visu_choice" value="HCPC" />
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292 <param name="HCPC_npc" value="5"/>
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293 <param name="HCPC_ncluster" value="-1"/>
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294 <param name="factor_choice" value="Yes" />
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295 <param name="factor" value="factor.tsv" ftype="txt"/>
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296 <output name="pdf_out" file="hcpc.2.pdf" compare="sim_size" ftype="pdf"/>
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297 <output name="HCPC_mutual_info" file="hcpc.factor.extval.txt" ftype="txt"/>
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298 <output name="HCPC_cell_clust" file="hcpc.cell-cluster.2.tsv" ftype="tabular"/>
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299 <output name="HCPC_contributions" file="hcpc.component-impact.2.tsv" ftype="tabular"/>
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300 </test>
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301 <test expect_num_outputs="4">
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302 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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303 <param name="labels" value="yes" />
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304 <param name="visu_choice" value="HCPC" />
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305 <param name="factor_choice" value="Yes" />
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306 <param name="factor" value="factor.tsv" ftype="txt"/>
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307 <param name="HCPC_method" value="average"/>
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308 <param name="HCPC_metric" value="manhattan"/>
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309 <param name="HCPC_npc" value="4" />
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310 <output name="pdf_out" file="hcpc.3.pdf" ftype="pdf"/>
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311 <output name="HCPC_mutual_info" file="hcpc.extval.1.txt" ftype="txt"/>
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312 <output name="HCPC_cell_clust" file="hcpc.cell-cluster.3.tsv" ftype="tabular"/>
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313 <output name="HCPC_contributions" file="hcpc.component-impact.3.tsv" ftype="tabular"/>
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314 </test>
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315 <test expect_num_outputs="3">
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316 <param name="input" value="cpm_input.tsv" ftype="txt"/>
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317 <param name="labels" value="no" />
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318 <param name="visu_choice" value="HCPC" />
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319 <param name="HCPC_method" value="single"/>
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320 <param name="HCPC_metric" value="euclidean"/>
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321 <param name="HCPC_npc" value="4" />
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322 <param name="HCPC_clusterCA" value="cols" />
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323 <output name="pdf_out" file="hcpc.4.pdf" compare="sim_size" ftype="pdf"/>
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324 <output name="HCPC_cell_clust" file="hcpc.cell-cluster.4.tsv" ftype="tabular"/>
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325 <output name="HCPC_contributions" file="hcpc.component-impact.4.tsv" ftype="tabular"/>
0
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326 </test>
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327 </tests>
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328 <help>
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329
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330 **What it does**
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331
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332 **Inputs**
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333 Takes as an input a tabulation separated value file (tsv) of n observations (columns,
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334 generally n RNAseq library) of k variables (rows, generally k genes).
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335
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336 The table must contain a header, ie the first line describes the content of each column.
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337
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338 k variables define a space of k dimensions. Any observation
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339 of k expression values for k genes (the purpose of one RNAseq experiment) can be assigned
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340 to a position in the k-dim space, of coordinates c1, c2, c3, ..., ck.
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341
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342 Since visualisation in more than 3 dimensions is not easy for a human beeing, there is
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343 a number of methods to "reduce" or "project" a k-dim space in a space of 2 or 3 dimensions.
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344 This is of great help, not only to summarise the data, but also to find similarities, common trends
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345 between the data (under the hypothesis that similar data are closer in the k-dimension space).
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346
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347 **Outputs**
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348
0
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349 This tool returns the visualisation of a dimensional reduction using either:
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350
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351 * Principal Components Analysis (PCA)
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352 * Hierarchical Clustering of Principal Components (HCPC)
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353 * t-distributed Stochastic Neighbor Embedding (t-SNE)
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354
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355 If HCPC is used, this tool can also return a 2-column cluster correspondence table:
4
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356
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357 * Observation labels
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358 * Cluster labels
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359
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360 **Contrast data with a factor**
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361
0
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362 The tool offers the possibility to colour data points according to the levels of a factor.
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363 To use the option "Factor to contrast data", provide a tabulated-separated, two-column table
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364 with first column containing the cell/data library identifiers (same identifiers as those
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365 provided as column headers in the input data table) and second column containing the corresponding
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366 factor levels value (if this vector is numerical, then the color palette used is quantitative).
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367 This table does not need to be sorted in the same order as in the data
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368 table. It may also contain more identifiers than those provided in the data table.
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369
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370 If HCPC visualisation and constrasting factor is provided, a text file containing metrics
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371 of external validation of the clustering is returned.
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372 These metrics measure the capacity of HCPC clustering to find classes overlapping the levels
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373 of the provided factor.
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374
0
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375 </help>
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376 <citations>
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377 <citation type="bibtex">@Article{,
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378 title = {Visualizing High-Dimensional Data Using t-SNE},
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379 volume = {9},
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380 pages = {2579-2605},
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381 year = {2008},
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382 author = {L.J.P. {van der Maaten} and G.E. Hinton},
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383 journal = {Journal of Machine Learning Research},
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384 }
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385 </citation>
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386 <citation type="bibtex">@Article{,
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387 title = {Accelerating t-SNE using Tree-Based Algorithms},
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388 volume = {15},
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389 pages = {3221-3245},
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390 year = {2014},
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391 author = {L.J.P. {van der Maaten}},
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392 journal = {Journal of Machine Learning Research},
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393 }
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394 </citation>
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395 <citation type="bibtex">@Manual{,
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396 title = {{Rtsne}: T-Distributed Stochastic Neighbor Embedding using
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397 Barnes-Hut Implementation},
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398 author = {Jesse H. Krijthe},
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399 year = {2015},
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400 note = {R package version 0.15},
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401 url = {https://github.com/jkrijthe/Rtsne},
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402 }
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403 </citation>
4
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404 <citation type="bibtex">@Manual{,
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405 title = {{ClusterR}: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans,
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406 K-Medoids and Affinity Propagation Clustering},
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407 author = {Lampros Mouselimis},
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408 year = {2019},
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409 note = {R package version 1.1.9},
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410 url = {https://github.com/mlampros/ClusterR},
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411 }
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412 </citation>
0
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413 </citations>
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414 </tool>