view minfi_ppfun.xml @ 57:a63802ce1d26 draft

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<tool id="minfi_ppfun" name="Minfi Preprocess Funnorm" version="@MINFI_VERSION@">
    <description>implements the functional normalization algorithm</description><macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements">
        <requirement type="package" version="0.6.0">bioconductor-illuminahumanmethylation450kanno.ilmn12.hg19</requirement>
    </expand>
    <command detect_errors="exit_code">
    <![CDATA[
     Rscript '$minfi_pp_script'
    ]]>
    </command>
    <configfiles>
    <configfile name="minfi_pp_script"><![CDATA[
require("minfi", quietly = TRUE)
RGSet <- get(load('$rgset'))

GRSet <- preprocessFunnorm(RGSet)

save(GRSet,file = '$grset')
 ]]> 
    </configfile>
    </configfiles> 

<inputs>
        <param type="data" name="rgset" format="rdata" label="RGChannelSet" help="These classes represents raw (unprocessed) data from a two color micro array; specifically an Illumina methylation array." />
    </inputs>
    <outputs>
        <data name="grset" format="rdata" label="GenomicRatioSet"/>
    </outputs>
    <tests>
        <test>
            <param name="rgset" value="RGChannelSet.rdata"/>
            <output name="grset" file="FunGenomicRatioSet.rdata"/>
        </test>
    </tests>
    <help><![CDATA[
        This tool uses the internal control probes present on the array to infer between-array technical variation. It is particularly useful for studies comparing conditions with known large-scale differences, such as cancer/normal studies, or between-tissue studies. It has been shown that for such studies, functional normalization outperforms other existing approaches (Jean-Philippe Fortin et al. 2014).
    ]]></help>
    <expand macro="citations" />
</tool>