annotate seurat_find_clusters.xml @ 5:eefaffdcd441 draft

"planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit 355bb52d2e9d170b1db237e649657cc14e0a047a"
author ebi-gxa
date Fri, 04 Mar 2022 07:25:55 +0000
parents 8ed848ef86fd
children
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eefaffdcd441 "planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit 355bb52d2e9d170b1db237e649657cc14e0a047a"
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1 <tool id="seurat_find_clusters" name="Seurat FindClusters" profile="18.01" version="@SEURAT_VERSION@+galaxy0">
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2 <description>find clusters of cells</description>
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3 <macros>
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4 <import>seurat_macros.xml</import>
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5 </macros>
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6 <expand macro="requirements" />
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7 <expand macro="version" />
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8 <command detect_errors="exit_code"><![CDATA[
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9 seurat-find-clusters.R
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10
1
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11 @INPUT_OBJECT@
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12 @OUTPUT_OBJECT@
0
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13 --output-text-file output_tab
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15 #if str($adv.resolution):
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16 --resolution '$adv.resolution'
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17 #end if
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19 #if str($adv.algorithm):
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20 --algorithm '$adv.algorithm'
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21 #end if
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22
1
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23 #if str($adv.modularity_fxn):
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24 --modularity-fxn '$adv.modularity_fxn'
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25 #end if
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27 #if str($adv.method):
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28 --method '$adv.method'
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29 #end if
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31 #if str($adv.graph_name):
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32 --graph-name '$adv.graph_name'
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33 #end if
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34
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35 #if str($adv.nrandom_starts):
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36 --nrandom-starts '$adv.nrandom_starts'
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37 #end if
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38
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39 $adv.group_singletons
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42
0
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43 ## TODO add pdf support as optional
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44 ]]></command>
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45
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46 <inputs>
1
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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 <section name="adv" title="Advanced Options">
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50 <param name="resolution" argument="--resolution" optional="true" type="float" label="Resolution" help="Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities. Defaults to 0.8."/>
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51 <param name="algorithm" argument="--algorithm" optional="true" type="select" label="Modularity organization algorithm">
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52 <option value="1" selected="true">Louvain</option>
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53 <option value="2">Louvain algorithm with multilevel refinement</option>
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54 <option value="3">SLM algorithm</option>
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55 <option value="4">Leiden</option>
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56 </param>
1
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57 <param name="modularity_fxn" argument="--modularity-fxn" optional="true" type="select" label="Modularity function">
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58 <option value="1" selected="true">Standard</option>
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59 <option value="2">Alternative</option>
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60 </param>
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61 <param name="method" argument="--method" type="select" label="Method for Leiden" help="Method for leiden (defaults to matrix which is fast for small datasets). Select iGraph to avoid casting large data to a dense matrix.">
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62 <option value="matrix" selected="true">Matrix</option>
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63 <option value="igraph">iGraph</option>
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64 </param>
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65 <param name="graph_name" argument="--graph-name" type="text" value="RNA_nn" label="Graph Name" help="Name of graph to use for the clustering algorith."/>
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66 <param name="nrandom_starts" argument="--nrandom-starts" type="integer" optional="true" label="Random starts" help="Number of random starts, 10 by default."/>
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67 <param name="group_singletons" argument="--group-singletons" type="boolean" truevalue="--group-singletons" falsevalue="" checked="false" label="Group singletons" help="Group singletons into nearest cluster. If FALSE, assign all singletons to a 'singleton' group."/>
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68 <param name="random_seed" argument="--random-seed" type="integer" optional="true" label="Random seed" help="Seed of the random number generator"/>
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69 </section>
1
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70
0
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71 </inputs>
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72 <outputs>
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73 <!-- <data name="out_pdf" format="pdf" from_work_dir="out.pdf" label="${tool.name} on ${on_string}: Plots" /> -->
1
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74 <expand macro="output_files"/>
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75 <data name="output_tab" format="csv" from_work_dir="output_tab" label="${tool.name} on ${on_string}: CSV Seurat Clusters"/>
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76 </outputs>
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77
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78 <tests>
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79 <!-- Ensure count matrix input works -->
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80 <test>
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81 <param name="rds_seurat_file" ftype="rdata" value="E-MTAB-6077-3k_features_90_cells-neighbours.rds"/>
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82 <output name="rds_seurat_file" ftype="rdata" >
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83 <assert_contents>
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84 <has_size value="5064329" delta="200000"/>
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85 </assert_contents>
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86 </output>
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87 <output name="output_tab" >
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88 <assert_contents>
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89 <has_n_lines n="92" />
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90 </assert_contents>
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91 </output>
0
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92 </test>
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93 </tests>
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94 <help><![CDATA[
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95 .. class:: infomark
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96
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97 **What it does**
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98
1
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99 Identify clusters of cells by a shared nearest neighbor (SNN) modularity optimization
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100 based clustering algorithm. First calculate k-nearest neighbors and construct t
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101 he SNN graph (using Seurat find neighbours).
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102 Then optimize the modularity function to determine clusters.
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103 For a full description of the algorithms, see Waltman and van Eck (2013)
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104 The European Physical Journal B.
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105
1
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106 @SEURAT_INTRO@
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107
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108 -----
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109
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110 **Inputs**
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111
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112 * RDS object
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113
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114 -----
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115
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116 **Outputs**
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117
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118 * Seurat RDS object
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119
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120 .. _Seurat: https://www.nature.com/articles/nbt.4096
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121 .. _Satija Lab: https://satijalab.org/seurat/
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122
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123 @VERSION_HISTORY@
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124
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125 ]]></help>
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126 <expand macro="citations" />
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127 </tool>