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1 <tool id="minfi_ppfun" name="Minfi Preprocess Funnorm" version="@MINFI_VERSION@">
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2 <description>implements the functional normalization algorithm</description><macros>
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3 <import>macros.xml</import>
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4 </macros>
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5 <expand macro="requirements">
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6 <requirement type="package" version="0.6.0">bioconductor-illuminahumanmethylation450kanno.ilmn12.hg19</requirement>
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7 </expand>
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8 <command detect_errors="exit_code">
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9 <![CDATA[
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10 Rscript '$minfi_pp_script'
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11 ]]>
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12 </command>
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13 <configfiles>
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14 <configfile name="minfi_pp_script"><![CDATA[
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15 require("minfi", quietly = TRUE)
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16 RGSet <- get(load('$rgset'))
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17
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18 GRSet <- preprocessFunnorm(RGSet)
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19
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20 save(GRSet,file = '$grset')
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21 ]]>
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22 </configfile>
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23 </configfiles>
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24
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25 <inputs>
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26 <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." />
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27 </inputs>
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28 <outputs>
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29 <data name="grset" format="rdata" label="GenomicRatioSet"/>
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30 </outputs>
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31 <tests>
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32 <test>
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33 <param name="rgset" value="RGChannelSet.rdata"/>
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34 <output name="grset" file="FunGenomicRatioSet.rdata"/>
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35 </test>
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36 </tests>
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37 <help><![CDATA[
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38 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).
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39 ]]></help>
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40 <expand macro="citations" />
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41 </tool>
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