view scanpy-scale-data.xml @ 0:96b851e96dd0 draft

planemo upload for repository commit 9bf9a6e46a330890be932f60d1d996dd166426c4
author ebi-gxa
date Wed, 03 Apr 2019 11:09:38 -0400
children 9acb5f068e6b
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<?xml version="1.0" encoding="utf-8"?>
<tool id="scanpy_scale_data" name="Scanpy ScaleData" version="@TOOL_VERSION@+galaxy1">
  <description>to make expression variance the same for all genes</description>
  <expand macro="requirements"/>
  <command detect_errors="exit_code"><![CDATA[
ln -s '${input_obj_file}' input.h5 &&
    -i input.h5
    -f '${input_format}'
    -o output.h5
    -F '${output_format}'
    #if $scale_max
        -x '${scale_max}'
    #end if
    #if $var_to_regress
        -V '${var_to_regress}'
    #end if
    #if $do_log
    #end if
    #if $zero_center
    #end if

    <expand macro="input_object_params"/>
    <expand macro="output_object_params"/>
    <param name="do_log" argument="--do-log" type="boolean" checked="true" label="Do log(x+1) transformation"/>
    <param name="zero_center" argument="--zero-center" type="boolean" checked="true" label="Zero center data before scaling"/>
    <param name="var_to_regress" argument="--var-to-regress" type="text" optional="true" label="Variable(s) to regress out">
      <option value="n_counts">n_counts</option>
    <param name="scale_max" argument="--scale-max" type="float" optional="true" label="Truncate to this value after scaling"/>

    <data name="output_h5" format="h5" from_work_dir="output.h5" label="${} on ${on_string}: Scaled data"/>

      <param name="input_obj_file" value="find_variable_genes.h5"/>
      <param name="input_format" value="anndata"/>
      <param name="output_format" value="anndata"/>
      <param name="do_log" value="true"/>
      <param name="zero_center" value="true"/>
      <param name="scale_max" value="10"/>
      <output name="output_h5" file="scale_data.h5" ftype="h5" compare="sim_size"/>

    .. class:: infomark

    **What it does**

    Scale data to unit variance and zero mean (`pp.scale`)
    Regress out unwanted sources of variation (`pp.regress_out`)

    Regress out unwanted sources of variation, using simple linear regression. This is
    inspired by Seurat's `regressOut` function in R.


  <expand macro="citations"/>