annotate scanpy-run-umap.xml @ 15:135e8cacb57e draft

"planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit e572b5425460d57f1bd13ab6374674c1c6801ab3-dirty"
author ebi-gxa
date Thu, 17 Sep 2020 16:30:12 +0000
parents f53bb2c1fa07
children 3a7b97ddf3ff
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1 <?xml version="1.0" encoding="utf-8"?>
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2 <tool id="scanpy_run_umap" name="Scanpy RunUMAP" version="@TOOL_VERSION@+galaxy1" profile="@PROFILE@">
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3 <description>visualise cell clusters using UMAP</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[
88c1516e25e0 planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit 9bf9a6e46a330890be932f60d1d996dd166426c4
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9 ln -s '${input_obj_file}' input.h5 &&
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10 PYTHONIOENCODING=utf-8 scanpy-run-umap
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11 #set neighbors_key = $use_graph.replace('INPUT_OBJ', $input_obj_file.__getattr__('name'))
135e8cacb57e "planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit e572b5425460d57f1bd13ab6374674c1c6801ab3-dirty"
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12 #set embeddings_tsv='embeddings.tsv'
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13 --neighbors-key '${neighbors_key}'
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14 #if $key_added
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15 #if $neighbors_key
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16 #set key_added = $key_added.replace('NEIGHBORS_KEY', $neighbors_key.__str__)
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17 #set embeddings_tsv="embeddings_" + $key_added.__str__ + ".tsv"
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18 #end if
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19 --key-added '${key_added}'
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20 #end if
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21 #if $embeddings
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22 --export-embedding embeddings.tsv
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23 #end if
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24 #if $settings.default == "false"
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25 --n-components ${settings.n_components}
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26 --min-dist ${settings.min_dist}
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27 --spread ${settings.spread}
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28 --alpha ${settings.alpha}
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29 --gamma ${settings.gamma}
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30 --negative-sample-rate ${settings.negative_sample_rate}
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31 --random-state ${settings.random_seed}
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32 #if $settings.init_pos
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33 --init-pos '${settings.init_pos}'
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34 #end if
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35 #if $settings.maxiter
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36 --maxiter ${settings.maxiter}
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37 #end if
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38 #end if
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39 @INPUT_OPTS@
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40 @OUTPUT_OPTS@
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41 #if $embeddings
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42 #if $embeddings_tsv != 'embeddings.tsv'
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43 && mv '${embeddings_tsv}' embeddings.tsv
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44 #end if
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45 #end if
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46
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47 ]]></command>
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48
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49 <inputs>
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50 <expand macro="input_object_params"/>
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51 <expand macro="output_object_params"/>
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52 <param name="embeddings" type="boolean" checked="true" label="Output embeddings in tsv format"/>
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53 <param name="use_graph" argument="--neighbors-key" value="neighbors" type="text"
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54 label="Name of the slot that holds the KNN graph"/>
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55 <param name="key_added" argument="--key-added" type="text" optional="true"
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56 label="Additional suffix to the name of the slot to save the embedding"/>
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57
0
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58 <conditional name="settings">
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59 <param name="default" type="boolean" checked="true" label="Use programme defaults"/>
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60 <when value="true"/>
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61 <when value="false">
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62 <param name="n_components" argument="--n-components" type="integer" value="2" label="The number of dimensions of the embedding"/>
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63 <param name="min_dist" argument="--min-dist" type="float" value="0.5" label="The effective minimum distance between embedded points"/>
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64 <param name="spread" argument="--spread" type="float" value="1.0" label="The effective spread of embedded points"/>
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65 <param name="alpha" argument="--alpha" type="float" value="1.0" label="Initial learning rate"/>
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66 <param name="gamma" argument="--gamma" type="float" value="1.0" label="Weighting applied to negative samples"/>
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67 <param name="negative_sample_rate" argument="--negative-sample-rate" type="integer" value="5" label="The ratio of negative to positive edge in optimisation"/>
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68 <param name="init_pos" argument="--init-pos" type="text" label="Method to initialise embedding, any key for adata.obsm or choose from the preset methods">
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69 <option value="spectral" selected="true">spectral</option>
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70 <option value="paga">paga</option>
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71 <option value="random">random</option>
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72 </param>
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73 <param name="maxiter" argument="--maxiter" type="integer" optional="true" label="Number of iterations of optimisation"/>
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74 <param name="random_seed" argument="--random-state" type="integer" value="0" label="Seed for numpy random number generator"/>
0
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75 </when>
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76 </conditional>
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77 </inputs>
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78
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79 <outputs>
5
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80 <expand macro="output_data_obj" description="UMAP object"/>
12
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81 <data name="output_embed" format="tabular" from_work_dir="embeddings.tsv" label="${tool.name} on ${on_string}: UMAP embeddings">
0
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82 <filter>embeddings</filter>
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83 </data>
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84 </outputs>
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85
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86 <tests>
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87 <test>
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88 <param name="input_obj_file" value="find_cluster.h5"/>
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89 <param name="input_format" value="anndata"/>
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90 <param name="output_format" value="anndata"/>
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91 <param name="default" value="false"/>
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92 <param name="embeddings" value="true"/>
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93 <param name="random_seed" value="0"/>
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94 <output name="output_h5" file="run_umap.h5" ftype="h5" compare="sim_size"/>
12
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95 <output name="output_embed" file="run_umap.embeddings.tsv" ftype="tabular" compare="sim_size">
0
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96 <assert_contents>
12
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97 <has_n_columns n="3"/>
0
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98 </assert_contents>
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99 </output>
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100 </test>
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101 </tests>
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102
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103 <help><![CDATA[
1
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104 ==========================================================
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105 Embed the neighborhood graph using UMAP (`scanpy.tl.umap`)
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106 ==========================================================
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107
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108 For making UMAP plots, please use `Scanpy PlotEmbed` with the output of this tool and enter "umap" as the
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109 name of the embedding to plot.
0
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110
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111 UMAP (Uniform Manifold Approximation and Projection) is a manifold learning
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112 technique suitable for visualizing high-dimensional data. Besides tending to
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113 be faster than tSNE, it optimizes the embedding such that it best reflects
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114 the topology of the data, which we represent throughout Scanpy using a
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115 neighborhood graph. tSNE, by contrast, optimizes the distribution of
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116 nearest-neighbor distances in the embedding such that these best match the
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117 distribution of distances in the high-dimensional space. We use the
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118 implementation of `umap-learn <https://github.com/lmcinnes/umap>`__
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119 (McInnes et al, 2018). For a few comparisons of UMAP with tSNE, see this `preprint
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120 <https://doi.org/10.1101/298430>`__.
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121
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122 It yields `X_umap`, UMAP coordinates of data.
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123
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124 @HELP@
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125
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126 @VERSION_HISTORY@
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127 ]]></help>
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128 <expand macro="citations"/>
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129 </tool>