Mercurial > repos > ebi-gxa > scanpy_run_tsne
comparison scanpy-run-tsne.xml @ 1:2e74fd7b5f45 draft
"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 |
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date | Mon, 16 Sep 2019 08:17:58 -0400 |
parents | f6f189ce4ebc |
children | 4ed72fb8eaf8 |
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0:f6f189ce4ebc | 1:2e74fd7b5f45 |
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1 <?xml version="1.0" encoding="utf-8"?> | 1 <?xml version="1.0" encoding="utf-8"?> |
2 <tool id="scanpy_run_tsne" name="Scanpy RunTSNE" version="@TOOL_VERSION@+galaxy1"> | 2 <tool id="scanpy_run_tsne" name="Scanpy RunTSNE" version="@TOOL_VERSION@+galaxy1"> |
3 <description>visualise cell clusters using tSNE</description> | 3 <description>visualise cell clusters using tSNE</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-tsne.py | 10 PYTHONIOENCODING=utf-8 scanpy-run-tsne |
11 -i input.h5 | 11 #if $use_rep != "auto" |
12 -f '${input_format}' | 12 --use-rep '${use_rep}' |
13 -o output.h5 | 13 #end if |
14 -F '${output_format}' | 14 #if $key_added |
15 #if $embeddings | 15 --key-added '${key_added}' |
16 --output-embeddings-file embeddings.csv | 16 #end if |
17 #if $embeddings | |
18 --export-embedding embeddings.csv | |
19 #end if | |
20 #if $settings.default == "false" | |
21 #if $settings.perplexity_file | |
22 --perplexity \$( cat $settings.perplexity_file ) | |
23 #else | |
24 --perplexity '${settings.perplexity}' | |
17 #end if | 25 #end if |
18 #if $settings.default == "false" | 26 --early-exaggeration '${settings.early_exaggeration}' |
19 #if $settings.perplexity_file | 27 --learning-rate '${settings.learning_rate}' |
20 --perplexity \$( cat $settings.perplexity_file ) | 28 #if $settings.n_pc |
21 #else | 29 --n-pcs ${settings.n_pc} |
22 --perplexity '${settings.perplexity}' | |
23 #end if | |
24 --early-exaggeration '${settings.early_exaggeration}' | |
25 --learning-rate '${settings.learning_rate}' | |
26 #if $settings.use_rep != "auto" | |
27 -r '${settings.use_rep}' | |
28 #end if | |
29 #if $settings.n_pc | |
30 -n '${settings.n_pc}' | |
31 #end if | |
32 #if not $settings.fast_tsne | |
33 --no-fast-tsne | |
34 #end if | |
35 #if $settings.n_job | |
36 --n-jobs '${settings.n_job}' | |
37 #end if | |
38 #if $settings.random_seed is not None | |
39 -s '${settings.random_seed}' | |
40 #end if | |
41 #end if | 30 #end if |
31 #if not $settings.fast_tsne | |
32 --no-fast-tsne | |
33 #end if | |
34 #if $settings.n_job | |
35 --n-jobs ${settings.n_job} | |
36 #end if | |
37 #if $settings.random_seed is not None | |
38 --random-state ${settings.random_seed} | |
39 #end if | |
40 #end if | |
41 @INPUT_OPTS@ | |
42 @OUTPUT_OPTS@ | |
42 | 43 |
43 @PLOT_OPTS@ | |
44 ]]></command> | 44 ]]></command> |
45 | 45 |
46 <inputs> | 46 <inputs> |
47 <expand macro="input_object_params"/> | 47 <expand macro="input_object_params"/> |
48 <expand macro="output_object_params"/> | 48 <expand macro="output_object_params"/> |
49 <param name="embeddings" type="boolean" checked="true" label="Output embeddings in csv format"/> | 49 <param name="embeddings" type="boolean" checked="true" label="Output embeddings in csv format"/> |
50 | 50 |
51 <param name="use_rep" argument="--use-rep" type="select" label="Use the indicated representation"> | |
52 <option value="X_pca">X_pca, use PCs</option> | |
53 <option value="X">X, use normalised expression values</option> | |
54 <option value="auto" selected="true">Automatically chosen based on problem size</option> | |
55 </param> | |
56 <param name="key_added" argument="--key-added" type="text" optional="true" | |
57 label="Additional suffix to the name of the slot to save the embedding"/> | |
58 | |
51 <conditional name="settings"> | 59 <conditional name="settings"> |
52 <param name="default" type="boolean" checked="true" label="Use programme defaults"/> | 60 <param name="default" type="boolean" checked="true" label="Use programme defaults"/> |
53 <when value="true"/> | 61 <when value="true"/> |
54 <when value="false"> | 62 <when value="false"> |
55 <param name="use_rep" argument="--use-rep" type="select" label="Use the indicated representation"> | |
56 <option value="X_pca">X_pca, use PCs</option> | |
57 <option value="X">X, use normalised expression values</option> | |
58 <option value="auto" selected="true">Automatically chosen based on problem size</option> | |
59 </param> | |
60 <param name="perplexity" argument="--perplexity" type="float" value="30" label="The perplexity is related to the number of nearest neighbours, select a value between 5 and 50"/> | 63 <param name="perplexity" argument="--perplexity" type="float" value="30" label="The perplexity is related to the number of nearest neighbours, select a value between 5 and 50"/> |
61 <param name="perplexity_file" argument="--perplexity" type="data" format="txt,tsv" label="The perplexity is related to the number of nearest neighbours" help="For use with the parameter iterator. Overrides the persplexity option above" optional="true"/> | 64 <param name="perplexity_file" argument="--perplexity" type="data" format="txt,tsv" label="The perplexity is related to the number of nearest neighbours" help="For use with the parameter iterator. Overrides the persplexity option above" optional="true"/> |
62 <param name="early_exaggeration" argument="--early-exaggeration" type="float" value="12" label="Controls the tightness within and between clusters"/> | 65 <param name="early_exaggeration" argument="--early-exaggeration" type="float" value="12" label="Controls the tightness within and between clusters"/> |
63 <param name="learning_rate" argument="--learning-rate" type="float" value="1000" label="Learning rate, should be between 100 and 1000"/> | 66 <param name="learning_rate" argument="--learning-rate" type="float" value="1000" label="Learning rate, should be between 100 and 1000"/> |
64 <param name="fast_tsne" type="boolean" checked="true" label="Use multicoreTSNE"/> | 67 <param name="fast_tsne" type="boolean" checked="true" label="Use multicoreTSNE"/> |
66 <param name="n_pc" argument="--n-pcs" type="integer" optional="true" label="The number of PCs to use"/> | 69 <param name="n_pc" argument="--n-pcs" type="integer" optional="true" label="The number of PCs to use"/> |
67 <param name="random_seed" argument="--random-seed" type="integer" value="0" label="Seed for random number generator"/> | 70 <param name="random_seed" argument="--random-seed" type="integer" value="0" label="Seed for random number generator"/> |
68 </when> | 71 </when> |
69 </conditional> | 72 </conditional> |
70 | 73 |
71 <conditional name="do_plotting"> | |
72 <param name="plot" type="boolean" checked="false" label="Make tSNE plot"/> | |
73 <when value="true"> | |
74 <expand macro="output_plot_params"/> | |
75 <param name="color_by" argument="--color-by" type="text" value="louvain" label="Color by attributes, comma separated strings"/> | |
76 </when> | |
77 <when value="false"/> | |
78 </conditional> | |
79 </inputs> | 74 </inputs> |
80 | 75 |
81 <outputs> | 76 <outputs> |
82 <data name="output_h5" format="h5" from_work_dir="output.h5" label="${tool.name} on ${on_string}: tSNE object"/> | 77 <data name="output_h5" format="h5" from_work_dir="output.h5" label="${tool.name} on ${on_string}: tSNE object"/> |
83 <data name="output_png" format="png" from_work_dir="output.png" label="${tool.name} on ${on_string}: tSNE plot"> | |
84 <filter>do_plotting['plot']</filter> | |
85 </data> | |
86 <data name="output_embed" format="csv" from_work_dir="embeddings.csv" label="${tool.name} on ${on_string}: tSNE embeddings"> | 78 <data name="output_embed" format="csv" from_work_dir="embeddings.csv" label="${tool.name} on ${on_string}: tSNE embeddings"> |
87 <filter>embeddings</filter> | 79 <filter>embeddings</filter> |
88 </data> | 80 </data> |
89 </outputs> | 81 </outputs> |
90 | 82 |
94 <param name="input_format" value="anndata"/> | 86 <param name="input_format" value="anndata"/> |
95 <param name="output_format" value="anndata"/> | 87 <param name="output_format" value="anndata"/> |
96 <param name="default" value="false"/> | 88 <param name="default" value="false"/> |
97 <param name="embeddings" value="true"/> | 89 <param name="embeddings" value="true"/> |
98 <param name="random_seed" value="0"/> | 90 <param name="random_seed" value="0"/> |
99 <param name="plot" value="true"/> | |
100 <param name="color_by" value="louvain"/> | |
101 <output name="output_h5" file="run_tsne.h5" ftype="h5" compare="sim_size"/> | 91 <output name="output_h5" file="run_tsne.h5" ftype="h5" compare="sim_size"/> |
102 <output name="output_png" file="run_tsne.png" ftype="png" compare="sim_size"/> | |
103 <output name="output_embed" file="run_tsne.embeddings.csv" ftype="csv" compare="sim_size"> | 92 <output name="output_embed" file="run_tsne.embeddings.csv" ftype="csv" compare="sim_size"> |
104 <assert_contents> | 93 <assert_contents> |
105 <has_n_columns n="2" sep=","/> | 94 <has_n_columns n="2" sep=","/> |
106 </assert_contents> | 95 </assert_contents> |
107 </output> | 96 </output> |
108 </test> | 97 </test> |
109 </tests> | 98 </tests> |
110 | 99 |
111 <help><![CDATA[ | 100 <help><![CDATA[ |
112 ================================================================== | 101 ========================================================================= |
113 t-distributed stochastic neighborhood embedding (tSNE) (`tl.tsne`) | 102 t-distributed stochastic neighborhood embedding (tSNE) (`scanpy.tl.tsne`) |
114 ================================================================== | 103 ========================================================================= |
104 | |
105 For making TSNE plots, please use `Scanpy PlotEmbed` with the output of this tool and enter "tsne" as the | |
106 name of the embedding to plot. | |
115 | 107 |
116 t-distributed stochastic neighborhood embedding (tSNE) (Maaten et al, 2008) has been | 108 t-distributed stochastic neighborhood embedding (tSNE) (Maaten et al, 2008) has been |
117 proposed for visualizating single-cell data by (Amir et al, 2013). Here, by default, | 109 proposed for visualizating single-cell data by (Amir et al, 2013). Here, by default, |
118 we use the implementation of *scikit-learn* (Pedregosa et al, 2011). | 110 we use the implementation of *scikit-learn* (Pedregosa et al, 2011). |
119 | 111 |