Mercurial > repos > bgruening > flexynesis_plot

annotate macros.xml @ 2:3c5d82bf6e8a 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 14:57:52 +0000
parents 262c2132b392
children 52b6f2ac38c7
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bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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1 <macros>
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2 <token name="@TOOL_VERSION@">0.2.20</token>
2
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3 <token name="@VERSION_SUFFIX@">2</token>
0
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4 <token name="@PROFILE@">24.1</token>
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5 <xml name="requirements">
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6 <requirements>
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7 <requirement type="package" version="@TOOL_VERSION@">flexynesis</requirement>
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8 <yield/>
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9 </requirements>
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10 </xml>
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11 <xml name="edam">
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12 <edam_topics>
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13 <edam_topic>topic_0622</edam_topic>
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14 <edam_topic>topic_3474</edam_topic>
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15 <edam_topic>topic_2640</edam_topic>
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16 </edam_topics>
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17 <edam_operations>
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18 <edam_operation>operation_3197</edam_operation>
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19 <edam_operation>operation_2403</edam_operation>
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20 <edam_operation>operation_2426</edam_operation>
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21 </edam_operations>
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22 </xml>
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23 <xml name="sanitizer_printable">
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24 <sanitizer invalid_char="">
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25 <valid initial="string.printable">
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26 <remove value="'"/>
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27 <remove value='"'/>
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28 <remove value=" "/>
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29 <yield/>
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30 </valid>
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31 </sanitizer>
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32 </xml>
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33 <xml name="sanitizer_letters">
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34 <sanitizer invalid_char=" ">
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35 <valid initial="string.letters">
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36 <add value="_"/>
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37 </valid>
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38 </sanitizer>
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39 </xml>
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40 <token name="@CHECK_NON_COMMERCIAL_USE@"><![CDATA[
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41 #if not $non_commercial_use
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42 >&2 echo "this tool is only available for non commercial use";
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43 exit 1;
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44 #end if
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45 ]]></token>
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46 <xml name="commercial_use_param">
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47 <param name="non_commercial_use" label="I certify that I am not using this tool for commercial purposes." type="boolean" truevalue="NON_COMMERCIAL_USE" falsevalue="COMMERCIAL_USE" checked="False">
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48 <validator type="expression" message="This tool is only available for non-commercial use.">value == True</validator>
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49 </param>
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50 </xml>
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51 <xml name="main_inputs">
2
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52 <param name="train_clin" type="data" format="tabular" label="Training clinical data"/>
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53 <param name="test_clin" type="data" format="tabular" label="Test clinical data"/>
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54 <param name="train_omics_main" type="data" format="tabular" label="Training omics data"/>
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55 <param name="test_omics_main" type="data" format="tabular" label="Test omics data"/>
0
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56 <param name="assay_main" type="text" optional="true" label="What type of assay is your input?" help="This would be used as output name.">
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57 <expand macro="sanitizer_letters"/>
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58 </param>
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59 </xml>
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60 <xml name="extra_inputs">
2
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61 <param name="train_omics" type="data" optional="true" format="tabular" label="Training omics data"/>
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62 <param name="test_omics" type="data" optional="true" format="tabular" label="Test omics data"/>
0
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63 <param name="assay" type="text" optional="true" label="What type of assay is your input?" help="This would be used as output name." >
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64 <expand macro="sanitizer_letters"/>
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65 </param>
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66 </xml>
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67 <xml name="advanced">
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68 <section name="advanced" title="Advanced Options">
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69 <param argument="--fusion_type" type="select" label="Fusion method" help="How to fuse the omics layers?">
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70 <option value="intermediate">intermediate</option>
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71 <option value="early">early</option>
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72 </param>
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73 <param argument="--finetuning_samples" type="integer" min="0" value="0" label="Number of samples from the test dataset to use for fine-tuning the model." help="Set to 0 to disable fine-tuning."/>
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74 <param argument="--variance_threshold" type="float" min="0" max="100" value="1" label="Variance threshold (as percentile) to drop low variance features." help="Set to 0 for no variance filtering."/>
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75 <param argument="--correlation_threshold" type="float" min="0" max="1" value="0.8" label="Correlation threshold to drop highly redundant features." help="Set to 1 for no redundancy filtering."/>
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76 <param argument="--subsample" type="integer" min="0" value="0" label="Downsample training set to randomly drawn N samples for training."/>
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77 <param argument="--features_min" type="integer" min="0" value="500" label="Minimum number of features to retain after feature selection."/>
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78 <param argument="--features_top_percentile" type="float" min="0" max="100" value="20" label="Top percentile features (among the features remaining after variance filtering and data cleanup) to retain after feature selection."/>
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79 <param argument="--log_transform" type="boolean" truevalue="--log_transform True" falsevalue="" checked="false" label="Whether to apply log-transformation to input data matrices"/>
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80 <param argument="--early_stop_patience" type="integer" min="-1" value="10" label="How many epochs to wait when no improvements in validation loss are observed." help="Set to -1 to disable early stopping."/>
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81 <param argument="--hpo_iter" type="integer" min="1" value="100" label="Number of iterations for hyperparameter optimization."/>
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82 <param argument="--val_size" type="float" min="0.0" max="1" value="0.2" label="Proportion of training data to be used as validation split"/>
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83 <param argument="--hpo_patience" type="integer" min="0" value="10" label="How many hyperparameter optimization iterations to wait for when no improvements are observed." help="Set to 0 to disable early stopping."/>
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84 <param argument="--use_cv" type="boolean" truevalue="--use_cv" falsevalue="" checked="false" label="Cross validation" help="If set, a 5-fold cross-validation training will be done. Otherwise, a single training on 80 percent of the dataset is done. "/>
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85 <param argument="--use_loss_weighting" type="boolean" truevalue="--use_loss_weighting True" falsevalue="" checked="true" label="Whether to apply loss-balancing using uncertainty weights method."/>
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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86 <param argument="--evaluate_baseline_performance" type="boolean" truevalue="--evaluate_baseline_performance" falsevalue="" checked="false" label="Enable modeling also with Random Forest + SVMs to see the performance of off-the-shelf tools on the same dataset."/>
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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87 <param argument="--feature_importance_method" type="select" label="which method(s) to use to compute feature importance scores.">
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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88 <option value="Both" selected="true">Both</option>
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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89 <option value="IntegratedGradients">IntegratedGradients</option>
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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90 <option value="GradientShap">GradientShap</option>
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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91 </param>
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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92 </section>
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93 </xml>
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94 <xml name="plots_common_param">
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95 <yield/>
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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96 <param name="format" type="select" label="Output format">
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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97 <option value="jpg" selected="true">jpg</option>
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98 <option value="png">png</option>
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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99 <option value="pdf">pdf</option>
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100 <option value="svg">svg</option>
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101 </param>
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102 <param name="dpi" type="integer" min="0" max="1200" value="300" label="DPI"/>
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103 </xml>
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104 <xml name="plots_common_input">
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105 <yield/>
2
3c5d82bf6e8a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit 1afbaf45449e25238935e222f983da62392c067a
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106 <param argument="--labels" type="data" format="tabular" label="Predicted labels" help="Generated by flexynesis"/>
0
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107 </xml>
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108 <token name="@PLOT_COMMON_CONFIG@"><![CDATA[
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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109 label_data = load_labels('inputs/$plot_conditional.labels.element_identifier.$plot_conditional.labels.ext')
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110 ]]></token>
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111 <token name="@PR_ROC_BOX_CONFIG@"><![CDATA[
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112 @PLOT_COMMON_CONFIG@
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113
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114 # Check if this is a regression problem (no class probabilities)
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115 non_na_probs = label_data['probability'].notna().sum()
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116
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117 print(f" Non-NaN probabilities: {non_na_probs}/{len(label_data)}")
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118
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119 # If most probabilities are NaN, this is likely a regression problem
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120 if non_na_probs < len(label_data) * 0.1: # Less than 10% valid probabilities
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121 raise ValueError(" Detected regression problem - precision-recall curves not applicable")
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122
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123 # Debug: Check data quality
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124 total_rows = len(label_data)
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125 missing_labels = label_data['known_label'].isna().sum()
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126 missing_probs = label_data['probability'].isna().sum()
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127 unique_samples = label_data['sample_id'].nunique()
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128 unique_classes = label_data['class_label'].nunique()
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129
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130 print(f" Data summary: {total_rows} total rows, {unique_samples} unique samples, {unique_classes} unique classes")
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131 print(f" Missing data: {missing_labels} missing known_label, {missing_probs} missing probability")
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132
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133 if missing_labels > 0:
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134 print(f" Warning: Found {missing_labels} missing known_label values")
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135 missing_samples = label_data[label_data['known_label'].isna()]['sample_id'].unique()[:5]
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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136 print(f" Sample IDs with missing known_label: {list(missing_samples)}")
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137
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138 # Remove rows with missing known_label
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139 label_data = label_data.dropna(subset=['known_label'])
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140 if label_data.empty:
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141 raise ValueError("Error: No valid known_label data remaining")
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142
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143 ]]></token>
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144 <token name="@PR_ROC_CONFIG@"><![CDATA[
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145 @PR_ROC_BOX_CONFIG@
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146
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147 # 1. Pivot to wide format
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148 prob_df = label_data.pivot(index='sample_id', columns='class_label', values='probability')
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149
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150 print(f" After pivot: {prob_df.shape[0]} samples x {prob_df.shape[1]} classes")
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151 print(f" Class columns: {list(prob_df.columns)}")
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152
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153 # Check for NaN values in probability data
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154 nan_counts = prob_df.isna().sum()
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155 if nan_counts.any():
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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156 print(f" NaN counts per class: {dict(nan_counts)}")
bb91bf19eb40 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit b2463fb68d0ae54864d87718ee72f5e063aa4587
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157 print(f" Samples with any NaN: {prob_df.isna().any(axis=1).sum()}/{len(prob_df)}")
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158
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159 # Drop only rows where ALL probabilities are NaN
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160 all_nan_rows = prob_df.isna().all(axis=1)
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161 if all_nan_rows.any():
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162 print(f" Dropping {all_nan_rows.sum()} samples with all NaN probabilities")
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163 prob_df = prob_df[~all_nan_rows]
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164
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165 remaining_nans = prob_df.isna().sum().sum()
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166 if remaining_nans > 0:
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167 print(f" Warning: {remaining_nans} individual NaN values remain - filling with 0")
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168 prob_df = prob_df.fillna(0)
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169
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170 if prob_df.empty:
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171 raise ValueError(f" Error: No valid probability data remaining for")
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172
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173 # 2. Get true labels
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174 true_labels_df = label_data.drop_duplicates('sample_id')[['sample_id', 'known_label']].set_index('sample_id')
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175
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176 # 3. Align indices - only keep samples that exist in both datasets
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177 common_indices = prob_df.index.intersection(true_labels_df.index)
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178 if len(common_indices) == 0:
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179 raise ValueError(f" Error: No common sample_ids between probability and true label data")
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180
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181 print(f" Found {len(common_indices)} samples with both probability and true label data")
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182
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183 # Filter both datasets to common indices
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184 prob_df_aligned = prob_df.loc[common_indices]
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185 y_true = true_labels_df.loc[common_indices]['known_label']
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186
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187 # 4. Final check for NaN values
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188 if y_true.isna().any():
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189 raise ValueError(f" Error: True labels still contain NaN after alignment")
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190
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191 if prob_df_aligned.isna().any().any():
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192 raise ValueError(f" Error: Probability data still contains NaN after alignment")
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193
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194 # 5. Convert categorical labels to integer labels
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195 # Create a mapping from class names to integers
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196 class_names = list(prob_df_aligned.columns)
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197 class_to_int = {class_name: i for i, class_name in enumerate(class_names)}
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198
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199 print(f" Class mapping: {class_to_int}")
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200
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201 # Convert true labels to integers
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202 y_true_np = y_true.map(class_to_int).to_numpy()
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203 y_probs_np = prob_df_aligned.to_numpy()
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204
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205 print(f" Data shape: y_true={y_true_np.shape}, y_probs={y_probs_np.shape}")
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206 print(f" Unique true labels (integers): {set(y_true_np)}")
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207 print(f" Class labels (columns): {class_names}")
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208 print(f" Label distribution: {dict(zip(*np.unique(y_true_np, return_counts=True)))}")
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209
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210 # Check for any unmapped labels (will be NaN)
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211 if pd.isna(y_true_np).any():
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212 raise ValueError(" Error: Some true labels could not be mapped to class columns")
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213
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214 ]]></token>
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215 <xml name="common_test">
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216 <param name="non_commercial_use" value="True"/>
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217 <conditional name="training_type">
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218 <param name="model" value="s_train"/>
2
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219 <param name="train_clin" value="train/clin" ftype="tabular"/>
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220 <param name="test_clin" value="test/clin" ftype="tabular"/>
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221 <param name="train_omics_main" value="train/gex" ftype="tabular"/>
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222 <param name="test_omics_main" value="test/gex" ftype="tabular"/>
0
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223 <param name="assay_main" value="bar"/>
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224 <repeat name="omics">
2
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225 <param name="train_omics" value="train/cnv" ftype="tabular"/>
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226 <param name="test_omics" value="test/cnv" ftype="tabular"/>
0
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227 <param name="assay" value="foo"/>
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228 </repeat>
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229 <conditional name="model_class">
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230 <param name="model_class" value="DirectPred"/>
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231 </conditional>
2
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232 <repeat name="targets">
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233 <param name="target_variables" value="3"/>
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234 </repeat>
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235 <param name="surv_event_var" value="15"/>
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236 <param name="surv_time_var" value="14"/>
0
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237 <section name="advanced">
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238 <param name="hpo_iter" value="1"/>
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239 </section>
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240 </conditional>
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241 <yield/>
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242 <output_collection name="results" type="list">
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243 <element name="job.embeddings_test">
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244 <assert_contents>
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245 <has_n_lines n="50"/>
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246 </assert_contents>
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247 </element>
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248 <element name="job.embeddings_train">
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249 <assert_contents>
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250 <has_n_lines n="50"/>
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251 </assert_contents>
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252 </element>
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253 <element name="job.feature_importance.GradientShap">
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254 <assert_contents>
2
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255 <has_text_matching expression="Erlotinib\t0\t\tbar\tA2M\t"/>
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256 <has_text_matching expression="Erlotinib\t0\t\tbar\tABCC4\t"/>
0
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257 <has_text_matching expression="GradientShap"/>
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258 </assert_contents>
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259 </element>
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260 <element name="job.feature_importance.IntegratedGradients">
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261 <assert_contents>
2
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262 <has_text_matching expression="Erlotinib\t0\t\tbar\tA2M\t"/>
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263 <has_text_matching expression="Erlotinib\t0\t\tbar\tABCC4\t"/>
0
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264 <has_text_matching expression="IntegratedGradients"/>
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265 </assert_contents>
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266 </element>
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267 <element name="job.feature_logs.bar">
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268 <assert_contents>
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269 <has_n_lines n="25"/>
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270 </assert_contents>
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271 </element>
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272 <element name="job.feature_logs.omics_foo">
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273 <assert_contents>
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274 <has_n_lines n="25"/>
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275 </assert_contents>
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276 </element>
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277 <element name="job.predicted_labels">
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278 <assert_contents>
2
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279 <has_text_matching expression="source_dataset:A-704\tErlotinib\t"/>
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280 <has_text_matching expression="target_dataset:KMRC-20\tErlotinib\t"/>
0
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281 </assert_contents>
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282 </element>
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283 <element name="job.stats">
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284 <assert_contents>
2
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285 <has_text_matching expression="DirectPred\tErlotinib\tnumerical\tmse\t"/>
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286 <has_text_matching expression="DirectPred\tErlotinib\tnumerical\tr2\t"/>
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287 <has_text_matching expression="DirectPred\tErlotinib\tnumerical\tpearson_corr\t"/>
0
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288 </assert_contents>
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289 </element>
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290 </output_collection>
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291 </xml>
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292 <token name="@COMMON_HELP@">
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293
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294 .. class:: warningmark
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295
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296 **WARNING: This tool is only available for NON-COMMERCIAL use. Permission is only granted for academic, research, and educational purposes. Before using, be sure to review, agree, and comply with the license.**
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297
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298 Flexynesis is a deep-learning based multi-omics bulk sequencing data integration suite with a focus on (pre-)clinical endpoint prediction.
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299 The package includes multiple types of deep learning architectures such as simple fully connected networks, supervised variational autoencoders, graph convolutional networks, multi-triplet networks different options of data layer fusion, and automates feature selection and hyperparameter optimization.
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300
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301 For more information, please check the Documentation_ :
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302
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303 For commercial use, please review the flexynesis license on GitHub and contact the `copyright holders`_ .
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304
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305 -----
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306
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307 </token>
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308 <xml name="creator">
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309 <creator>
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310 <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/"/>
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311 <person givenName="Amirhossein" familyName="Naghsh Nilchi" email="nilchia@informatik.uni-freiburg.de"/>
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312 <yield/>
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313 <person givenName="Björn" familyName="Grüning" email="gruening@informatik.uni-freiburg.de"/>
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314 </creator>
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315 </xml>
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316 <xml name="citations">
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317 <citations>
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318 <citation type="doi">10.1101/2024.07.16.603606</citation>
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319 </citations>
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320 </xml>
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321 </macros>