Mercurial > repos > ebi-gxa > scanpy_run_pca
diff scanpy-run-pca.xml @ 1:7798c318e7d7 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 |
---|---|
date | Mon, 16 Sep 2019 08:12:12 -0400 |
parents | 5063cd7f8c89 |
children | 242cf7e1fd0c |
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--- a/scanpy-run-pca.xml Wed Apr 03 11:08:16 2019 -0400 +++ b/scanpy-run-pca.xml Mon Sep 16 08:12:12 2019 -0400 @@ -1,94 +1,64 @@ <?xml version="1.0" encoding="utf-8"?> -<tool id="scanpy_run_pca" name="Scanpy RunPCA" version="@TOOL_VERSION@+galaxy1"> +<tool id="scanpy_run_pca" name="Scanpy RunPCA" version="@TOOL_VERSION@+galaxy0"> <description>for dimensionality reduction</description> <macros> - <import>scanpy_macros.xml</import> + <import>scanpy_macros2.xml</import> </macros> <expand macro="requirements"/> <command detect_errors="exit_code"><![CDATA[ ln -s '${input_obj_file}' input.h5 && -PYTHONIOENCODING=utf-8 scanpy-run-pca.py - -i input.h5 - -f '${input_format}' - -o output.h5 - -F '${output_format}' - -n '${n_pcs}' - #if $run_mode.chunked - -c - --chunk-size '${run_mode.chunk_size}' - #else - #if $run_mode.zero_center - -z - #else - -Z - #end if - #if $run_mode.svd_solver - --svd-solver '${run_mode.svd_solver}' - #end if - #if $run_mode.random_seed is not None - -s '${run_mode.random_seed}' - #end if +PYTHONIOENCODING=utf-8 scanpy-run-pca + --n-comps '${n_pcs}' +#if $run_mode.chunked + --chunked + --chunk-size '${run_mode.chunk_size}' +#else + ${run_mode.zero_center} + #if $run_mode.svd_solver + --svd-solver '${run_mode.svd_solver}' #end if - #if $extra_outputs: - #set extras = ' '.join(['--output-{}-file {}.csv'.format(x, x) for x in str($extra_outputs).split(',')]) - ${extras} + #if $run_mode.random_seed is not None + --random-state '${run_mode.random_seed}' #end if - -@PLOT_OPTS@ +#end if +#if $extra_outputs and "embeddings" in str($extra_outputs).split(','): + --export-embedding embeddings.tsv +#end if + @INPUT_OPTS@ + @OUTPUT_OPTS@ ]]></command> <inputs> <expand macro="input_object_params"/> <expand macro="output_object_params"/> - <param name="n_pcs" argument="--n-pcs" type="integer" value="50" label="Number of PCs to produce"/> + <param name="n_pcs" argument="--n-comps" type="integer" min="2" value="50" label="Number of PCs to produce"/> <conditional name="run_mode"> <param name="chunked" argument="--chunked" type="boolean" checked="false" label="Perform incremental PCA by chunks"/> <when value="true"> <param name="chunk_size" argument="--chunk-size" type="integer" value="0" label="Chunk size"/> </when> <when value="false"> - <param name="zero_center" argument="--zero-center" type="boolean" checked="true" label="Zero center data before scaling"/> + <param name="zero_center" argument="--zero-center" type="boolean" truevalue="" falsevalue="--no-zero-center" checked="true" + label="Zero center data before scaling"/> <param name="svd_solver" argument="--svd-solver" type="select" optional="true" label="SVD solver"> <option value="arpack">ARPACK</option> - <option value="randomised">Randomised</option> + <option value="randomized">Randomised</option> </param> - <param name="random_seed" argument="--random-seed" type="integer" value="0" label="random_seed for numpy random number generator"/> + <param name="random_seed" argument="--random-state" type="integer" value="0" label="random seed for numpy random number generator"/> </when> </conditional> - <param name="extra_outputs" type="select" multiple="true" optional="true" label="Type of output"> + <param name="extra_outputs" type="select" multiple="true" display="checkboxes" optional="true" label="Export extra output"> <option value="embeddings">PCA embeddings</option> - <option value="loadings">PCA loadings</option> - <option value="stdev">PCs stdev</option> - <option value="var-ratio">PCs proportion of variance</option> </param> - <conditional name="do_plotting"> - <param name="plot" type="boolean" checked="false" label="Make PCA plot"/> - <when value="true"> - <expand macro="output_plot_params"/> - </when> - <when value="false"/> - </conditional> </inputs> <outputs> <data name="output_h5" format="h5" from_work_dir="output.h5" label="${tool.name} on ${on_string}: PCA object"/> - <data name="output_png" format="png" from_work_dir="output.png" label="${tool.name} on ${on_string}: PCA plot"> - <filter>do_plotting['plot']</filter> - </data> - <data name="output_embed" format="csv" from_work_dir="embeddings.csv" label="${tool.name} on ${on_string}: PCA embeddings"> + <data name="output_embed" format="tsv" from_work_dir="embeddings.tsv" label="${tool.name} on ${on_string}: PCA embeddings"> <filter>extra_outputs and 'embeddings' in extra_outputs.split(',')</filter> </data> - <data name="output_load" format="csv" from_work_dir="loadings.csv" label="${tool.name} on ${on_string}: PCA loadings"> - <filter>extra_outputs and 'loadings' in extra_outputs.split(',')</filter> - </data> - <data name="output_stdev" format="csv" from_work_dir="stdev.csv" label="${tool.name} on ${on_string}: PCA stdev"> - <filter>extra_outputs and 'stdev' in extra_outputs.split(',')</filter> - </data> - <data name="output_vprop" format="csv" from_work_dir="var-ratio.csv" label="${tool.name} on ${on_string}: PC explained proportion of variance"> - <filter>extra_outputs and 'var-ratio' in extra_outputs.split(',')</filter> - </data> </outputs> <tests> @@ -102,10 +72,7 @@ <param name="svd_solver" value="arpack"/> <param name="random_seed" value="0"/> <param name="chunked" value="false"/> - <param name="plot" value="true"/> - <param name="color_by" value="n_genes"/> <output name="output_h5" file="run_pca.h5" ftype="h5" compare="sim_size"/> - <output name="output_png" file="run_pca.png" ftype="png" compare="sim_size"/> <output name="output_embed" file="run_pca.embeddings.csv" ftype="csv" compare="sim_size"> <assert_contents> <has_n_columns n="50" sep=","/> @@ -115,9 +82,9 @@ </tests> <help><![CDATA[ -======================================================================================================= -Computes PCA (principal component analysis) coordinates, loadings and variance decomposition (`tl.pca`) -======================================================================================================= +================================================================================ +Dimensionality reduction by PCA (principal component analysis) (`scanpy.pp.pca`) +================================================================================ It uses the implementation of *scikit-learn*.