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annotate flexynesis_utils.xml @ 2:e5ecfffcfe45 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit 1afbaf45449e25238935e222f983da62392c067a
author bgruening
date Fri, 04 Jul 2025 14:57:15 +0000
parents 433a5f3f68a1
children f413f828ef30
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1 <tool id="flexynesis_utils" name="Flexynesis utils" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">
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2 <description>Utility functions for Flexynesis</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="requirements"/>
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7 <required_files>
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8 <include path="flexynesis_plot.py" />
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9 <include path="flexynesis_utils.py" />
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10 </required_files>
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11 <command detect_errors="exit_code"><![CDATA[
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12 @CHECK_NON_COMMERCIAL_USE@
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13 mkdir -p inputs/ output/ &&
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14 #if $utils_conditional.util != "compute_ami_ari" and $utils_conditional.util != "split_data" and $utils_conditional.util != "binarize":
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15 ln -s '$utils_conditional.X' 'inputs/$utils_conditional.X.element_identifier.$utils_conditional.X.ext' &&
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16 #end if
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17 #if $utils_conditional.util != "split_data" and $utils_conditional.util != "binarize":
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18 ln -s '$utils_conditional.labels' 'inputs/$utils_conditional.labels.element_identifier.$utils_conditional.labels.ext' &&
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19 cat '$flexynesis_utils_config' &&
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20 python '$flexynesis_utils_config'
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21 #end if
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22 #if $utils_conditional.util == "split_data":
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23 ln -s '$utils_conditional.clin' inputs/clin.tabular &&
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24 #set $omics_names = []
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25 #for $omics_file in $utils_conditional.omics:
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26 ln -s '$omics_file' 'inputs/${omics_file.element_identifier}.${omics_file.ext}' &&
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27 #silent $omics_names.append('inputs/' + str($omics_file.element_identifier) + '.' + str($omics_file.ext))
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28 #end for
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29
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30 python '$__tool_directory__/flexynesis_utils.py'
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31 --util split
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32 --clin inputs/clin.tabular
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33 --omics '$(",".join($omics_names))'
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34 --split $utils_conditional.split
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35 --out output
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36 #end if
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37 #if $utils_conditional.util == "binarize":
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38 ln -s '$utils_conditional.mutation' 'inputs/${utils_conditional.mutation.element_identifier}.${utils_conditional.mutation.ext}' &&
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39 python '$__tool_directory__/flexynesis_utils.py'
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40 --util binarize
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41 --mutation 'inputs/${utils_conditional.mutation.element_identifier}.${utils_conditional.mutation.ext}'
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42 --gene_idx $utils_conditional.gene_idx
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43 --sample_idx $utils_conditional.sample_idx
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44 --out output
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45 #end if
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46 ]]></command>
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47 <configfiles>
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48 <configfile name="flexynesis_utils_config"><![CDATA[
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49 import sys
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50 sys.path.append('$__tool_directory__/')
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51
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52 import numpy as np
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53 import pandas as pd
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54 from flexynesis import (
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55 louvain_clustering,
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56 get_optimal_clusters,
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57 compute_ami_ari,
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58 k_means_clustering
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59 )
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60
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61 from flexynesis_plot import (
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62 load_omics
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63 )
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64
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65 #if $utils_conditional.util == "louvain_clustering":
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66 label_data = load_omics('inputs/$utils_conditional.labels.element_identifier.$utils_conditional.labels.ext')
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67 X = load_omics('inputs/$utils_conditional.X.element_identifier.$utils_conditional.X.ext')
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68
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69 cluster_labels, G, partition = louvain_clustering(
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70 #if $utils_conditional.threshold != "":
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71 threshold=$utils_conditional.threshold,
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72 #end if
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73 #if $utils_conditional.k != "":
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74 k=$utils_conditional.k,
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75 #end if
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76 X=X)
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77 cluster_df = pd.DataFrame(data=cluster_labels, index=X.index, columns=['louvain_cluster'])
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78 label_data = label_data.merge(cluster_df[['louvain_cluster']], left_index=True, right_index=True, how='left')
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79
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80 output_path = f"output/clustered_labels.tabular"
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81 label_data.to_csv(output_path, sep="\t", index=True)
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82
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83 #else if $utils_conditional.util == "get_optimal_clusters":
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84 label_data = load_omics('inputs/$utils_conditional.labels.element_identifier.$utils_conditional.labels.ext')
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85 X = load_omics('inputs/$utils_conditional.X.element_identifier.$utils_conditional.X.ext')
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86
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87 kmeans_cluster_labels, optimal_k, silhouette_scores = get_optimal_clusters(
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88 data=X,
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89 min_k=$utils_conditional.min_k,
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90 max_k=$utils_conditional.max_k)
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91
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92 print(f"Optimal number of clusters: {optimal_k}\n")
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93 print(f"Silhouette scores: \n{silhouette_scores}")
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94
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95 cluster_df = pd.DataFrame(data=kmeans_cluster_labels, index=X.index, columns=['optimal_kmeans_cluster'])
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96 label_data = label_data.merge(cluster_df[['optimal_kmeans_cluster']], left_index=True, right_index=True, how='left')
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97
2
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98 output_path = f"output/optimal_clusters_labels.tabular"
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99 label_data.to_csv(output_path, sep="\t", index=True)
0
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100
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101 #else if $utils_conditional.util == "k_means_clustering":
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102 label_data = load_omics('inputs/$utils_conditional.labels.element_identifier.$utils_conditional.labels.ext')
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103 X = load_omics('inputs/$utils_conditional.X.element_identifier.$utils_conditional.X.ext')
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104
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105 cluster_labels, kmeans = k_means_clustering(
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106 data=X,
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107 k=$utils_conditional.k)
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108
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109 print(f"{kmeans}")
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110 cluster_df = pd.DataFrame(data=cluster_labels, index=X.index, columns=['kmeans_cluster'])
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111 label_data = label_data.merge(cluster_df[['kmeans_cluster']], left_index=True, right_index=True, how='left')
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112
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113 output_path = f"output/kmeans_labels.tabular"
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114 label_data.to_csv(output_path, sep="\t", index=True)
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115
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116 #else if $utils_conditional.util == "compute_ami_ari":
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117 label_data = load_omics('inputs/$utils_conditional.labels.element_identifier.$utils_conditional.labels.ext')
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118
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119 true_label = label_data.columns[$utils_conditional.true_label-2]
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120 predicted_label = label_data.columns[$utils_conditional.predicted_label-2]
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121
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122 true_labels = label_data[true_label]
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123 predicted_labels = label_data[predicted_label]
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124
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125 ami_ari = compute_ami_ari(labels1=true_labels, labels2=predicted_labels)
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126
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127 print(f"AMI: {ami_ari['ami']}")
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128 print(f"ARI: {ami_ari['ari']}")
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129
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130 output_path = f"output/ami_ari.txt"
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131 with open(output_path, 'w') as f:
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132 f.write(f"AMI: {ami_ari['ami']}\n")
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133 f.write(f"ARI: {ami_ari['ari']}\n")
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134
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135 #end if
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136 ]]></configfile>
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137 </configfiles>
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138 <inputs>
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139 <expand macro="commercial_use_param"/>
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140 <conditional name="utils_conditional">
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141 <param name="util" type="select" label="Flexynesis utils">
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142 <option value="louvain_clustering">Louvain Clustering</option>
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143 <option value="get_optimal_clusters">Get Optimal Clusters</option>
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144 <option value="k_means_clustering">K-Means Clustering</option>
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145 <option value="compute_ami_ari">Compute AMI and ARI</option>
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146 <option value="split_data">Split data to train and test</option>
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147 <option value="binarize">Binarize mutation data</option>
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148 </param>
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149 <when value="louvain_clustering">
2
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150 <param argument="--X" type="data" format="tabular" label="Matrix" help="Input matrix, (samples, features)"/>
0
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151 <expand macro="plots_common_input"/>
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152 <param argument="--threshold" type="float" min="0" optional="true" label="Distance threshold to create an edge between two nodes"/>
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153 <param argument="--k" type="integer" min="0" optional="true" label="Number of nearest neighbors to connect for each node"/>
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154 </when>
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155 <when value="get_optimal_clusters">
2
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156 <param argument="--X" type="data" format="tabular" label="Matrix" help="Input matrix, (samples, features)"/>
0
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157 <expand macro="plots_common_input"/>
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158 <param argument="--min_k" type="integer" min="0" value="2" optional="false" label="Minimum number of clusters to try"/>
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159 <param argument="--max_k" type="integer" min="0" value="10" optional="false" label="Maximum number of clusters to try"/>
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160 </when>
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161 <when value="k_means_clustering">
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162 <param argument="--X" type="data" format="tabular" label="Matrix" help="Input matrix, (samples, features)"/>
0
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163 <expand macro="plots_common_input"/>
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164 <param argument="--k" type="integer" min="0" optional="true" label="The number of clusters to form"/>
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165 </when>
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166 <when value="compute_ami_ari">
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167 <expand macro="plots_common_input"/>
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168 <param name="true_label" type="data_column" data_ref="labels" label="Column name in the labels file to use for the true labels"/>
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169 <param name="predicted_label" type="data_column" data_ref="labels" label="Column name in the labels file to use for the predicted labels"/>
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170 </when>
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171 <when value="split_data">
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172 <param argument="--clin" type="data" format="tabular" optional="false" label="Clinical data" help="Samples in rows"/>
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173 <param argument="--omics" type="data" format="tabular" optional="false" multiple="true" label="Omics data" help="samples in columns"/>
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174 <param argument="--split" type="float" min="0" max="1" value="0.7" label="Training/Test split ratio" help="Proportion of data to use for training (e.g., 0.7 means 70% train, 30% test)"/>
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175 </when>
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176 <when value="binarize">
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177 <param argument="--mutation" type="data" format="tabular" label="Mutation data" help="Mutation data with both genes and samples in rows"/>
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178 <param argument="--gene_idx" type="data_column" data_ref="mutation" label="Column in the mutation file with genes"/>
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179 <param argument="--sample_idx" type="data_column" data_ref="mutation" label="Column in the mutation file with samples"/>
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180 </when>
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181 </conditional>
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182 </inputs>
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183 <outputs>
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184 <data name="util_out" auto_format="true" from_work_dir="output/*" label="${tool.name} on ${on_string}: ${utils_conditional.util}">
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185 <filter>utils_conditional['util'] != "split_data"</filter>
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186 </data>
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187 <collection name="train_out" type="list" label="${tool.name} on ${on_string}: train datasets">
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188 <discover_datasets pattern="__name_and_ext__" format="tabular" directory="output/train"/>
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189 <filter>utils_conditional['util'] == "split_data"</filter>
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190 </collection>
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191 <collection name="test_out" type="list" label="${tool.name} on ${on_string}: test datasets">
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192 <discover_datasets pattern="__name_and_ext__" format="tabular" directory="output/test"/>
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193 <filter>utils_conditional['util'] == "split_data"</filter>
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194 </collection>
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195 </outputs>
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196 <tests>
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197 <!-- test 1: Louvain clustering -->
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198 <test expect_num_outputs="1">
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199 <param name="non_commercial_use" value="True"/>
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200 <conditional name="utils_conditional">
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201 <param name="util" value="louvain_clustering"/>
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202 <param name="X" value="embeddings.tabular"/>
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203 <param name="labels" value="labels_pr.tabular"/>
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204 <param name="k" value="15"/>
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205 </conditional>
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206 <output name="util_out">
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207 <assert_contents>
2
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208 <has_text_matching expression="sample_id\tvariable\tclass_label\tprobability\tknown_label\tpredicted_label\tsplit\tlouvain_cluster"/>
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209 <has_text_matching expression="MB-4818\tCLAUDIN_SUBTYPE\tLumA\t0.8582904\tLumB\tLumA\ttest\t3.0"/>
0
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210 </assert_contents>
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211 </output>
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212 </test>
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213 <!-- test 2: Get optimal clusters -->
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214 <test expect_num_outputs="1">
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215 <param name="non_commercial_use" value="True"/>
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216 <conditional name="utils_conditional">
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217 <param name="util" value="get_optimal_clusters"/>
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218 <param name="X" value="embeddings.tabular"/>
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219 <param name="labels" value="labels_pr.tabular"/>
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220 <param name="min_k" value="2"/>
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221 <param name="max_k" value="10"/>
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222 </conditional>
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223 <assert_stdout>
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224 <has_text text="Optimal number of clusters: 2"/>
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225 <has_text text="Silhouette scores: "/>
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226 </assert_stdout>
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227 <output name="util_out">
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228 <assert_contents>
2
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229 <has_text_matching expression="sample_id\tvariable\tclass_label\tprobability\tknown_label\tpredicted_label\tsplit\toptimal_kmeans_cluster"/>
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230 <has_text_matching expression="MB-4818\tCLAUDIN_SUBTYPE\tLumA\t0.8582904\tLumB\tLumA\ttest\t0.0"/>
0
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231 </assert_contents>
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232 </output>
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233 </test>
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234 <!-- test 3: K-Means clustering -->
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235 <test expect_num_outputs="1">
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236 <param name="non_commercial_use" value="True"/>
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237 <conditional name="utils_conditional">
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238 <param name="util" value="k_means_clustering"/>
2
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239 <param name="X" value="embeddings.tabular"/>
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240 <param name="labels" value="labels_pr.tabular"/>
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241 <param name="k" value="2"/>
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242 </conditional>
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243 <assert_stdout>
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244 <has_text text="KMeans(n_clusters=2, random_state=42)"/>
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245 </assert_stdout>
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246 <output name="util_out">
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247 <assert_contents>
2
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248 <has_text_matching expression="sample_id\tvariable\tclass_label\tprobability\tknown_label\tpredicted_label\tsplit\tkmeans_cluster"/>
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249 <has_text_matching expression="MB-4818\tCLAUDIN_SUBTYPE\tLumA\t0.8582904\tLumB\tLumA\ttest\t0.0"/>
0
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250 </assert_contents>
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251 </output>
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252 </test>
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253 <!-- test 4: Compute AMI and ARI -->
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254 <test expect_num_outputs="1">
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255 <param name="non_commercial_use" value="True"/>
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256 <conditional name="utils_conditional">
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257 <param name="util" value="compute_ami_ari"/>
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258 <param name="labels" value="labels.tabular"/>
0
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259 <param name="true_label" value="5"/>
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260 <param name="predicted_label" value="6"/>
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261 </conditional>
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262 <assert_stdout>
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263 <has_text_matching expression="AMI: 0.5108[0-9]+"/>
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264 <has_text_matching expression="ARI: 0.5258[0-9]+"/>
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265 </assert_stdout>
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266 <output name="util_out">
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267 <assert_contents>
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268 <has_text_matching expression="AMI: 0.5108[0-9]+"/>
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269 <has_text_matching expression="ARI: 0.5258[0-9]+"/>
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270 </assert_contents>
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271 </output>
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272 </test>
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273 <!-- test 5: Split data to train and test -->
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274 <test expect_num_outputs="2">
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275 <param name="non_commercial_use" value="True"/>
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276 <conditional name="utils_conditional">
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277 <param name="util" value="split_data"/>
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278 <param name="clin" value="train/clin"/>
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279 <param name="omics" value="train/cnv,train/gex"/>
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280 <param name="split" value="0.7"/>
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281 </conditional>
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282 <output_collection name="train_out" type="list" count="3">
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283 <element name="clin">
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284 <assert_contents>
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285 <has_text_matching expression="sample"/>
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286 <has_n_lines n="645"/>
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287 </assert_contents>
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288 </element>
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289 <element name="cnv">
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290 <assert_contents>
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291 <has_text_matching expression="gene"/>
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292 <has_n_lines n="25"/>
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293 </assert_contents>
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294 </element>
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295 <element name="gex">
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296 <assert_contents>
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297 <has_text_matching expression="gene"/>
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298 <has_n_lines n="25"/>
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299 </assert_contents>
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300 </element>
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301 </output_collection>
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302 <output_collection name="test_out" type="list" count="3">
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303 <element name="clin">
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304 <assert_contents>
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305 <has_text_matching expression="sample"/>
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306 <has_n_lines n="277"/>
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307 </assert_contents>
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308 </element>
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309 <element name="cnv">
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310 <assert_contents>
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311 <has_text_matching expression="gene"/>
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312 <has_n_lines n="25"/>
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313 </assert_contents>
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314 </element>
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315 <element name="gex">
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316 <assert_contents>
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317 <has_text_matching expression="gene"/>
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318 <has_n_lines n="25"/>
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319 </assert_contents>
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320 </element>
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321 </output_collection>
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322 </test>
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323 <!-- test 6: Binarize mutation data -->
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324 <test expect_num_outputs="1">
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325 <param name="non_commercial_use" value="True"/>
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326 <conditional name="utils_conditional">
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327 <param name="util" value="binarize"/>
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328 <param name="mutation" value="mut.tabular"/>
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329 <param name="gene_idx" value="1"/>
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330 <param name="sample_idx" value="17"/>
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331 </conditional>
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332 <output name="util_out">
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333 <assert_contents>
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334 <has_n_lines n="1611"/>
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335 <has_text text="Hugo_Symbol"/>
2
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336 <has_text_matching expression="AADACL2\t0.0\t0.0"/>
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337 <has_text_matching expression="ABCB1\t0.0\t0.0\t0.0\t1.0"/>
0
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338 </assert_contents>
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339 </output>
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340 </test>
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341 </tests>
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342 <help><![CDATA[
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343 @COMMON_HELP@
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344
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345 Flexynesis Utils provides a collection of clustering and evaluation utilities for multi-omics data analysis. This tool offers four main functionalities:
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346
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347 1. **Louvain Clustering**: Community detection clustering algorithm that partitions data into clusters based on network modularity optimization
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348 2. **Get Optimal Clusters**: Determines the optimal number of clusters using K-means clustering with silhouette score evaluation
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349 3. **K-Means Clustering**: Standard K-means clustering algorithm for partitioning data into k clusters
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350 4. **Compute AMI and ARI**: Calculates Adjusted Mutual Information (AMI) and Adjusted Rand Index (ARI) to evaluate clustering performance
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351 5. **Split data to train and test**: Splits multi-omics data into training and testing sets based on a specified ratio
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352 6. **Binarize mutation data**: Converts mutation data into a binary format, indicating presence or absence of mutations for each gene in each sample
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353
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354 **Louvain Clustering**
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355 Uses the Louvain algorithm for community detection in networks. This method builds a graph from the input data and finds communities (clusters) by optimizing modularity.
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356
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357 Outputs Original labels file with added 'louvain_cluster' column
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358
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359 **Get Optimal Clusters**
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360 Performs K-means clustering for a range of k values and determines the optimal number of clusters using silhouette analysis.
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361
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362 Outputs Original labels file with added 'optimal_kmeans_cluster' column
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363 Console output shows the optimal k value and silhouette scores for each k.
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364
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365 **K-Means Clustering**
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366 Standard K-means clustering algorithm that partitions data into k clusters by minimizing within-cluster sum of squares.
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367
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368 Outputs Original labels file with added 'kmeans_cluster' column
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369
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370 **Compute AMI and ARI**
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371 Evaluates clustering performance by comparing true labels with predicted labels using two standard metrics:
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372 - *AMI (Adjusted Mutual Information)*: Measures mutual information between clusterings, adjusted for chance
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373 - *ARI (Adjusted Rand Index)*: Measures similarity between clusterings, adjusted for chance
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374
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375 Outputs Text file containing AMI and ARI scores
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376 Both metrics range from 0 to 1, where 1 indicates perfect agreement
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377
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378 **Search cBioPortal**
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379 Fetches available data files from cBioPortal for a specified study ID. This allows users to explore and retrieve data from cBioPortal studies.
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380 Outputs a text file listing available data files for the specified study ID
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381
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382 **Split data to train and test**
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383 Splits multi-omics data into training and testing sets based on a specified ratio. This is useful for preparing datasets for machine learning tasks.
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384 You can use `Flexynesis cBioPortal import` to fetch data from cBioPortal and then use this tool to split the data into training and testing sets.
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385
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386 **Binarize mutation data**
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387 Converts mutation data into a binary format, indicating presence or absence of mutations for each gene in each sample. This is useful for preparing mutation data for analysis.
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388 The output will be a tabular file with genes as rows and samples as columns, where each cell indicates whether a mutation is present (1) or absent (0).
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389
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390
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391 .. _Documentation: https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis/site/
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392 .. _copyright holders: https://github.com/BIMSBbioinfo/flexynesis
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393 ]]></help>
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394 <expand macro="creator"/>
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395 <expand macro="citations"/>
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396 </tool>