view fastpca.xml @ 1:d9f8cc3258f9 draft

"planemo upload for repository commit 1b23e024af45cc0999d9142d07de6897d4189ec2"
author chemteam
date Mon, 24 Aug 2020 06:09:11 -0400
parents dd195f7a791c
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<tool id="fastpca" name="fastpca" version="@VERSION@">
    <description>- dimensionality reduction of MD simulations</description>
        <token name="@VERSION@">0.9.1</token>
         <requirement type="package" version="@VERSION@">fastpca</requirement>
    <command detect_errors="exit_code"><![CDATA[
            -f '$input'
            -p '$output_proj'
            #if str($inputs.cov) == 'None':
                -c '$output_cov'
            #elif str($inputs.vec) == 'None':
                -C '$inputs.cov'
            #end if
            #if str($inputs.vec) == 'None':
                -v $output_vec
                -V '$inputs.vec'
            #end if
            #if str($inputs.stats) == 'None':
                -s '$output_stats'
                -S '$inputs.stats'
            #end if
            -l '$output_val'

        <param format="tabular,xtc" name="input" type="data" label="Input data" help="Either a whitespace-separated tabular file or GROMACS XTC file."/>
        <section name="inputs" title="Inputs" expanded="true" help="Use these (optional) inputs to project new data onto a previously computed principal space. If not set, the PCA will be computed from scratch and will not be comparable to previous runs." >
            <param format="tabular" name="cov" type="data" label="Precomputed covariance/correlation matrix" optional="true"/>
            <param format="tabular" name="vec" type="data" label="Precomputed eigenvectors" optional="true"/>
            <param format="tabular" name="stats" type="data" label="Precomputed statistics (mean values, sigmas and boundary shifts)" optional="true"/>

        <param name="norm" type="select" label="How to normalize input:" help="Generally, normalization using the covariance matrix is appropriate when the variable scales are similar, and the correlation matrix is used when variables are on different scales." >
            <option value="">Covariance</option>
            <option value="-N">Correlation</option>
        <param name="periodic" type="boolean" label="Compute covariance and PCA on a torus?" truevalue="-P" falsevalue="" value="false" help="Useful for computing PCA on periodic data - for example, dihedral angles."/>
        <param name="dynamic_shift" type="boolean" label="Use dynamic shifting for periodic projection correction" truevalue="-D" falsevalue="" value="false" help="Default is fale, i.e. simply shift to region of lowest density"/>
        <data name="output_proj" format="tabular"/>
        <data name="output_cov" format="tabular">
            <filter>inputs["cov"] == None</filter>
        <data name="output_vec" format="tabular">
            <filter>inputs["vec"] == None</filter>
        <data name="output_stats" format="tabular">
            <filter>inputs["stats"] == None</filter>
        <data name="output_val" format="tabular"/>
        <!-- fastpca -f contacts.dat -p proj.dat -c cov.dat -v vec.dat -s stats.dat -l val.dat -N -->
            <param name="input" value="contacts.dat"/>
            <param name="norm" value="-N"/>
            <param name="periodic" value="false"/>
            <param name="dynamic_shift" value="false"/>
            <output name="output_proj" file="proj.dat"/>
            <output name="output_cov" file="cov.dat"/>
            <output name="output_vec" file="vec.dat"/>
            <output name="output_stats" file="stats.dat"/>
            <output name="output_val" file="val.dat"/>
        <!-- fastpca -f contacts2.dat -p proj2.dat -C cov.dat -V vec.dat -S stats.dat -l val2.dat -N -->
            <param name="input" value="contacts2.dat"/>
            <param name="cov" value="cov.dat"/>
            <param name="stats" value="stats.dat"/>
            <param name="norm" value="-N"/>
            <param name="periodic" value="false"/>
            <param name="dynamic_shift" value="false"/>
            <output name="output_proj" file="proj2.dat"/>
            <output name="output_val" file="val2.dat"/>
.. class:: infomark
**What it does**
Dimensionality reduction of molecular dynamics trajectories. Data can be input as 
tabular or GROMACS XTC files. In addition, data can be projected into a previously 
computed coordinate space by providing precomputed eigenvectors, statistics and 
a correlation/covariance matrix. 

Data can be normalized using the either the covariance or correlation matrix. Data
can also be calculated on a torus, which is useful for periodic data, such as protein
dihedral angles.


.. class:: infomark


       - Tabular or XTC file
       - If you want to project data into a previously calculated principal space, you can upload precomputed eigenvectors, statistics and correlation/covariance matrix.


.. class:: infomark


       - Projected data (tabular file) with each column representing a principal component
       - Eigenvectors, statistics and covariance/correlation matrix

      <citation type="doi">10.1063/1.4998259</citation>