Mercurial > repos > kpbioteam > ewastools
comparison minfi_ppquantile.xml @ 7:f47e5cca1696 draft
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| author | kpbioteam |
|---|---|
| date | Fri, 22 Feb 2019 08:12:01 -0500 |
| parents | |
| children | 9c6fbb7d5a2a |
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| 6:4f34e3f73ebf | 7:f47e5cca1696 |
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| 1 <tool id="minfi_ppquantile" name="Minfi Preprocess Quantile" version="@MINFI_VERSION@"> | |
| 2 <description>implements stratified quantile normalization preprocessing</description> | |
| 3 <macros> | |
| 4 <import>macros.xml</import> | |
| 5 </macros> | |
| 6 <expand macro="requirements"> | |
| 7 <requirement type="package" version="0.6.0">bioconductor-illuminahumanmethylation450kanno.ilmn12.hg19</requirement> | |
| 8 </expand> | |
| 9 <command detect_errors="exit_code"> | |
| 10 <![CDATA[ | |
| 11 Rscript '$minfi_pp_script' | |
| 12 ]]> | |
| 13 </command> | |
| 14 <configfiles> | |
| 15 <configfile name="minfi_pp_script"><![CDATA[ | |
| 16 require("minfi", quietly = TRUE) | |
| 17 RGSet <- get(load('$rgset')) | |
| 18 | |
| 19 GRSet <- preprocessQuantile(RGSet, fixOutliers = TRUE, | |
| 20 removeBadSamples = TRUE, badSampleCutoff = 10.5, | |
| 21 quantileNormalize = TRUE, stratified = TRUE, | |
| 22 mergeManifest = FALSE, sex = NULL) | |
| 23 | |
| 24 save(GRSet,file = '$grset') | |
| 25 | |
| 26 ]]> | |
| 27 </configfile> | |
| 28 </configfiles> | |
| 29 <inputs> | |
| 30 <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." /> | |
| 31 </inputs> | |
| 32 <outputs> | |
| 33 <data name="grset" format="rdata" label="GenomicRatioSet"/> | |
| 34 </outputs> | |
| 35 <tests> | |
| 36 <test> | |
| 37 <param name="rgset" value="RGChannelSet.rdata"/> | |
| 38 <output name="grset" file="QuantileGenomicRatioSet.rdata"/> | |
| 39 </test> | |
| 40 </tests> | |
| 41 <help><![CDATA[ | |
| 42 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. | |
| 43 ]]></help> | |
| 44 <expand macro="citations" /> | |
| 45 </tool> | |
| 46 |