comparison minfi_ppquantile.xml @ 0:41ab8ba3a901 draft default tip

planemo upload for repository https://github.com/kpbioteam/minfi_ppquantile commit 93ac44e4428f7560ef032adcf5749ada58d15f57-dirty

0:41ab8ba3a901

<tool id="minfi_ppquantile" name="minfi_ppquantile" version="0.1.0">
    <description>implements stratified quantile normalization preprocessing</description>
    <requirements>
        <requirement type="package" version="1.24.0">bioconductor-minfi</requirement>
        <requirement type="package" version="0.4.0">bioconductor-illuminahumanmethylation450kmanifest</requirement>
        <requirement type="package" version="0.6.0">bioconductor-illuminahumanmethylation450kanno.ilmn12.hg19</requirement>
    </requirements>
    <command detect_errors="exit_code"><![CDATA[
        Rscript ${__tool_directory__}/minfi_ppquantile.R "$input1" "$output1"
    ]]></command>
    <inputs>
        <param type="data" name="input1" format="rdata" />
    </inputs>
    <outputs>
        <data name="output1" format="rdata" />
    </outputs>
    <tests>
        <test>
            <param name="input1" value="RGSet.rdata"/>
            <output name="output1" file="quantile.rdata"/>
        </test>
    </tests>
    <help><![CDATA[
        The normalization procedure is applied to the Meth and Unmeth intensities separately. The distribution of type I and type II signals is forced to be the same by first quantile normalizing the type II probes across samples and then interpolating a reference distribution to which we normalize the type I probes. Since probe types and probe regions are confounded and we know that DNAm distributions vary across regions we stratify the probes by region before applying this interpolation.
    ]]></help>
    <citations>
        <citation type="doi">10.1093/bioinformatics/btu049</citation>
    </citations>
</tool>