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comparison scanpy-run-umap.xml @ 1:0ac2f9f2313b 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 4846776f55931e176f7e77af7c185ec6fec7d142"
author ebi-gxa
date Mon, 16 Sep 2019 08:16:59 -0400
parents 88c1516e25e0
children 6eb7a2e37adf
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0:88c1516e25e0 1:0ac2f9f2313b
1 <?xml version="1.0" encoding="utf-8"?> 1 <?xml version="1.0" encoding="utf-8"?>
2 <tool id="scanpy_run_umap" name="Scanpy RunUMAP" version="@TOOL_VERSION@+galaxy1"> 2 <tool id="scanpy_run_umap" name="Scanpy RunUMAP" version="@TOOL_VERSION@+galaxy1">
3 <description>visualise cell clusters using UMAP</description> 3 <description>visualise cell clusters using UMAP</description>
4 <macros> 4 <macros>
5 <import>scanpy_macros.xml</import> 5 <import>scanpy_macros2.xml</import>
6 </macros> 6 </macros>
7 <expand macro="requirements"/> 7 <expand macro="requirements"/>
8 <command detect_errors="exit_code"><![CDATA[ 8 <command detect_errors="exit_code"><![CDATA[
9 ln -s '${input_obj_file}' input.h5 && 9 ln -s '${input_obj_file}' input.h5 &&
10 PYTHONIOENCODING=utf-8 scanpy-run-umap.py 10 PYTHONIOENCODING=utf-8 scanpy-run-umap
11 -i input.h5 11 --use-graph '${use_graph}'
12 -f '${input_format}' 12 --key-added '${key_added}'
13 -o output.h5 13 #if $embeddings
14 -F '${output_format}' 14 --export-embedding embeddings.csv
15 #if $embeddings 15 #end if
16 --output-embeddings-file embeddings.csv 16 #if $settings.default == "false"
17 --n-components ${settings.n_components}
18 --min-dist ${settings.min_dist}
19 --spread ${settings.spread}
20 --alpha ${settings.alpha}
21 --gamma ${settings.gamma}
22 --negative-sample-rate ${settings.negative_sample_rate}
23 --random-state ${settings.random_seed}
24 #if $settings.init_pos
25 --init-pos '${settings.init_pos}'
17 #end if 26 #end if
18 #if $settings.default == "false" 27 #if $settings.maxiter
19 -n '${settings.n_components}' 28 --maxiter ${settings.maxiter}
20 --min-dist '${settings.min_dist}' 29 #end if
21 --spread '${settings.spread}' 30 #end if
22 --alpha '${settings.alpha}' 31 @INPUT_OPTS@
23 --gamma '${settings.gamma}' 32 @OUTPUT_OPTS@
24 --negative-sample-rate '${settings.negative_sample_rate}'
25 #if $settings.init_pos
26 --init-pos '${settings.init_pos}'
27 #end if
28 #if $settings.maxiter
29 --maxiter '${settings.maxiter}'
30 #end if
31 #if $settings.a
32 -a '${settings.a}'
33 #end if
34 #if $settings.b
35 -b '${settings.b}'
36 #end if
37 #if $settings.random_seed is not None
38 -s '${settings.random_seed}'
39 #end if
40 #end if
41 33
42 @PLOT_OPTS@
43 ]]></command> 34 ]]></command>
44 35
45 <inputs> 36 <inputs>
46 <expand macro="input_object_params"/> 37 <expand macro="input_object_params"/>
47 <expand macro="output_object_params"/> 38 <expand macro="output_object_params"/>
48 <param name="embeddings" type="boolean" checked="true" label="Output embeddings in csv format"/> 39 <param name="embeddings" type="boolean" checked="true" label="Output embeddings in csv format"/>
40 <param name="use_graph" argument="--use-graph" value="neighbors" type="text"
41 label="Name of the slot that holds the KNN graph"/>
42 <param name="key_added" argument="--key-added" type="text" optional="true"
43 label="Additional suffix to the name of the slot to save the embedding"/>
44
49 <conditional name="settings"> 45 <conditional name="settings">
50 <param name="default" type="boolean" checked="true" label="Use programme defaults"/> 46 <param name="default" type="boolean" checked="true" label="Use programme defaults"/>
51 <when value="true"/> 47 <when value="true"/>
52 <when value="false"> 48 <when value="false">
53 <param name="n_components" argument="--n-components" type="integer" value="2" label="The number of dimensions of the embedding"/> 49 <param name="n_components" argument="--n-components" type="integer" value="2" label="The number of dimensions of the embedding"/>
60 <option value="spectral" selected="true">spectral</option> 56 <option value="spectral" selected="true">spectral</option>
61 <option value="paga">paga</option> 57 <option value="paga">paga</option>
62 <option value="random">random</option> 58 <option value="random">random</option>
63 </param> 59 </param>
64 <param name="maxiter" argument="--maxiter" type="integer" optional="true" label="Number of iterations of optimisation"/> 60 <param name="maxiter" argument="--maxiter" type="integer" optional="true" label="Number of iterations of optimisation"/>
65 <param name="a" argument="-a" type="float" optional="true" label="More specific parameter controlling embedding, automatically determined from --min-dist and --spread if unset"/> 61 <param name="random_seed" argument="--random-state" type="integer" value="0" label="Seed for numpy random number generator"/>
66 <param name="b" argument="-b" type="float" optional="true" label="More specific parameter controlling embedding, automatically determined from --min-dist and --spread if unset"/>
67 <param name="random_seed" argument="--random-seed" type="integer" value="0" label="Seed for numpy random number generator"/>
68 </when> 62 </when>
69 </conditional>
70 <conditional name="do_plotting">
71 <param name="plot" type="boolean" checked="false" label="Make UMAP plot"/>
72 <when value="true">
73 <expand macro="output_plot_params"/>
74 <param name="color_by" argument="--color-by" type="text" value="louvain" label="Color by attributes, comma separated strings"/>
75 </when>
76 <when value="false"/>
77 </conditional> 63 </conditional>
78 </inputs> 64 </inputs>
79 65
80 <outputs> 66 <outputs>
81 <data name="output_h5" format="h5" from_work_dir="output.h5" label="${tool.name} on ${on_string}: UMAP object"/> 67 <data name="output_h5" format="h5" from_work_dir="output.h5" label="${tool.name} on ${on_string}: UMAP object"/>
82 <data name="output_png" format="png" from_work_dir="output.png" label="${tool.name} on ${on_string}: UMAP plot">
83 <filter>do_plotting['plot']</filter>
84 </data>
85 <data name="output_embed" format="csv" from_work_dir="embeddings.csv" label="${tool.name} on ${on_string}: UMAP embeddings"> 68 <data name="output_embed" format="csv" from_work_dir="embeddings.csv" label="${tool.name} on ${on_string}: UMAP embeddings">
86 <filter>embeddings</filter> 69 <filter>embeddings</filter>
87 </data> 70 </data>
88 </outputs> 71 </outputs>
89 72
93 <param name="input_format" value="anndata"/> 76 <param name="input_format" value="anndata"/>
94 <param name="output_format" value="anndata"/> 77 <param name="output_format" value="anndata"/>
95 <param name="default" value="false"/> 78 <param name="default" value="false"/>
96 <param name="embeddings" value="true"/> 79 <param name="embeddings" value="true"/>
97 <param name="random_seed" value="0"/> 80 <param name="random_seed" value="0"/>
98 <param name="plot" value="true"/>
99 <param name="color_by" value="louvain"/>
100 <output name="output_h5" file="run_umap.h5" ftype="h5" compare="sim_size"/> 81 <output name="output_h5" file="run_umap.h5" ftype="h5" compare="sim_size"/>
101 <output name="output_png" file="run_umap.png" ftype="png" compare="sim_size"/>
102 <output name="output_embed" file="run_umap.embeddings.csv" ftype="csv" compare="sim_size"> 82 <output name="output_embed" file="run_umap.embeddings.csv" ftype="csv" compare="sim_size">
103 <assert_contents> 83 <assert_contents>
104 <has_n_columns n="2" sep=","/> 84 <has_n_columns n="2" sep=","/>
105 </assert_contents> 85 </assert_contents>
106 </output> 86 </output>
107 </test> 87 </test>
108 </tests> 88 </tests>
109 89
110 <help><![CDATA[ 90 <help><![CDATA[
111 ======================================================== 91 ==========================================================
112 Embed the neighborhood graph using UMAP (`tl.umap`) 92 Embed the neighborhood graph using UMAP (`scanpy.tl.umap`)
113 ======================================================== 93 ==========================================================
94
95 For making UMAP plots, please use `Scanpy PlotEmbed` with the output of this tool and enter "umap" as the
96 name of the embedding to plot.
114 97
115 UMAP (Uniform Manifold Approximation and Projection) is a manifold learning 98 UMAP (Uniform Manifold Approximation and Projection) is a manifold learning
116 technique suitable for visualizing high-dimensional data. Besides tending to 99 technique suitable for visualizing high-dimensional data. Besides tending to
117 be faster than tSNE, it optimizes the embedding such that it best reflects 100 be faster than tSNE, it optimizes the embedding such that it best reflects
118 the topology of the data, which we represent throughout Scanpy using a 101 the topology of the data, which we represent throughout Scanpy using a