Mercurial > repos > ebi-gxa > scanpy_compute_graph

annotate scanpy-neighbours.xml @ 26:0fe56ddbab0d draft

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"planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit 9748fb7ca203206ecac97461f60ce75890d00fa4-dirty"
author ebi-gxa
date Fri, 15 Oct 2021 15:05:21 +0000
parents fcaa9048cdaf
children 6417cccad133
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1 <?xml version="1.0" encoding="utf-8"?>
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2 <tool id="scanpy_compute_graph" name="Scanpy ComputeGraph" version="@TOOL_VERSION@+galaxy0" profile="@PROFILE@">
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3 <description>to derive kNN graph</description>
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4 <macros>
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5 <import>scanpy_macros2.xml</import>
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6 </macros>
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7 <expand macro="requirements"/>
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8 <command detect_errors="exit_code"><![CDATA[
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9 ln -s '${input_obj_file}' input.h5 &&
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10 PYTHONIOENCODING=utf-8 scanpy-neighbors
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11 #if $settings.default == "false"
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12 #if $settings.n_neighbors_file
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13 #set f = open($settings.n_neighbors_file.__str__)
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14 #set n_neighbors = f.read().strip()
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15 #silent f.close
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16 #elif $settings.n_neighbors
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17 #set n_neighbors = $settings.n_neighbors.__str__.strip()
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18 #end if
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19
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20 #if $n_neighbors
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21 --n-neighbors $n_neighbors
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22 #end if
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23 #if $settings.key_added
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24 #set key_added = $settings.key_added
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25 #if $n_neighbors
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26 #set key_added = $key_added.replace('N_NEIGHBORS', $n_neighbors.__str__)
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27 #end if
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28 --key-added '${key_added}'
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29 #end if
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30
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31 --method '${settings.method}'
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32 --metric '${settings.metric}'
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33 --random-state '${settings.random_seed}'
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34 #if $settings.use_rep
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35 --use-rep '${settings.use_rep}'
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36 #end if
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37 #if $settings.n_pcs
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38 --n-pcs '${settings.n_pcs}'
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39 #end if
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40 ${settings.knn}
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41 #end if
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42 @INPUT_OPTS@
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43 @OUTPUT_OPTS@
0
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44 ]]></command>
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45
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46 <inputs>
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47 <expand macro="input_object_params"/>
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48 <expand macro="output_object_params"/>
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49 <conditional name="settings">
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50 <param name="default" type="boolean" checked="true" label="Use programme defaults"/>
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51 <when value="true"/>
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52 <when value="false">
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53 <param name="key_added" argument="--key-added" type="text" optional="true" label="Key added"
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54 help="If not specified, the neighbors data is stored in .uns[‘neighbors’], distances and connectivities are stored in .obsp[‘distances’] and .obsp[‘connectivities’] respectively. If specified, the neighbors data is added to .uns[key_added], distances are stored in .obsp[key_added+’_distances’] and connectivities in .obsp[key_added+’_connectivities’]." value='' />
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55 <param name="n_neighbors" argument="--n-neighbors" type="integer" value="15" label="Maximum number of neighbors used"/>
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56 <param name="n_neighbors_file" argument="--n-neighbors" type="data" format="txt,tsv" optional="true"
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57 label="File with n_neighbours, use with parameter iterator. Overrides the n_neighbors setting"/>
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58 <param name="use_rep" argument="--use-rep" type="text" optional="true" label="Use the indicated representation" help="'X' (for the content of .X, usually normalised expression values) or any key for .obsm (e.g. X_pca for PCA) is valid. If not set, the representation is chosen automatically: For .n_vars less than 50, .X is used, otherwise ‘X_pca’ is used. If ‘X_pca’ is not present, it’s computed with default parameters."/>
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59 <param name="n_pcs" argument="--n-pcs" type="integer" value="50" optional="true" label="Number of PCs to use"/>
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60 <param name="knn" argument="--knn" type="boolean" truevalue="" falsevalue="--no-knn" checked="true"
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61 label="Use hard threshold to restrict neighbourhood size (otherwise use a Gaussian kernel to down weight distant neighbors)"/>
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62 <param name="method" argument="--method" type="select" label="Method for calculating connectivity">
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63 <option value="umap" selected="true">UMAP</option>
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64 <option value="gauss">Gaussian</option>
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65 <option value="rapids">RAPIDS</option>
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66 </param>
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67 <param name="metric" argument="--metric" type="select" label="A known metric’s name">
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68 <option value="euclidean" selected="true">Euclidean</option>
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69 <option value="cityblock">cityblock</option>
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70 <option value="cosine">cosine</option>
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71 <option value="l1">l1</option>
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72 <option value="l2">l2</option>
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73 <option value="manhattan">manhattan</option>
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74 <option value="braycurtis">braycurtis</option>
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75 <option value="canberra">canberra</option>
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76 <option value="chebyshev">chebyshev</option>
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77 <option value="correlation">correlation</option>
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78 <option value="dice">dice</option>
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79 <option value="hamming">hamming</option>
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80 <option value="jaccard">jaccard</option>
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81 <option value="kulsinski">kulsinski</option>
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82 <option value="mahalanobis">mahalanobis</option>
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83 <option value="minkowski">minkowski</option>
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84 <option value="rogerstanimoto">rogerstanimoto</option>
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85 <option value="russelrao">russelrao</option>
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86 <option value="seuclidan">seuclidian</option>
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87 <option value="sokalmichener">sokalmichener</option>
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88 <option value="sokalsneath">sokalsneath</option>
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89 <option value="sqeuclidean">sqeuclidean</option>
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90 <option value="yule">yule</option>
0
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91 </param>
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92 <param name="random_seed" argument="--random-seed" type="integer" value="0" label="Seed for random number generator"/>
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93 </when>
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94 </conditional>
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95 </inputs>
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96
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97 <outputs>
5
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98 <expand macro="output_data_obj" description="Graph object"/>
0
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99 </outputs>
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100
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101 <tests>
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102 <test>
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103 <param name="input_obj_file" value="run_pca.h5"/>
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104 <param name="input_format" value="anndata"/>
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105 <param name="output_format" value="anndata"/>
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106 <param name="default" value="false"/>
14
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107 <param name="n_neighbors_file" value="n_neighbors.txt"/>
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108 <param name="n_pcs" value="50"/>
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109 <param name="knn" value="true"/>
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110 <param name="random_seed" value="0"/>
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111 <param name="method" value="umap"/>
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112 <output name="output_h5" file="compute_graph.h5" ftype="h5" compare="sim_size"/>
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113 </test>
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114 <test>
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115 <param name="input_obj_file" value="run_pca.h5"/>
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116 <param name="input_format" value="anndata"/>
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117 <param name="output_format" value="anndata"/>
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118 <param name="default" value="false"/>
11
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119 <param name="n_neighbors" value="15"/>
0
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120 <param name="n_pcs" value="50"/>
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121 <param name="knn" value="true"/>
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122 <param name="random_seed" value="0"/>
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123 <param name="method" value="umap"/>
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124 <output name="output_h5" file="compute_graph.h5" ftype="h5" compare="sim_size"/>
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125 </test>
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126 </tests>
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127
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128 <help><![CDATA[
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129 =============================================================
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130 Compute a neighborhood graph of observations (`pp.neighbors`)
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131 =============================================================
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132
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133 The neighbor search efficiency of this heavily relies on UMAP (McInnes et al, 2018),
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134 which also provides a method for estimating connectivities of data points -
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135 the connectivity of the manifold (`method=='umap'`). If `method=='diffmap'`,
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136 connectivities are computed according to Coifman et al (2005), in the adaption of
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137 Haghverdi et al (2016).
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138
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139 @HELP@
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140
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141 @VERSION_HISTORY@
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142 ]]></help>
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143 <expand macro="citations"/>
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144 </tool>