Mercurial > repos > kpbioteam > ewastools
changeset 50:5cc814a1f5bb draft
Uploaded
author | kpbioteam |
---|---|
date | Fri, 22 Feb 2019 11:11:31 -0500 |
parents | 9f6e5d00629b |
children | 52cf1f7f11c5 |
files | minfi_ppquantile.xml |
diffstat | 1 files changed, 46 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/minfi_ppquantile.xml Fri Feb 22 11:11:31 2019 -0500 @@ -0,0 +1,46 @@ +<tool id="minfi_ppquantile" name="Minfi Preprocess Quantile" version="@MINFI_VERSION@"> + <description>implements stratified quantile normalization preprocessing</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 <- preprocessQuantile(RGSet, fixOutliers = TRUE, + removeBadSamples = TRUE, badSampleCutoff = 10.5, + quantileNormalize = TRUE, stratified = TRUE, + mergeManifest = FALSE, sex = NULL) + +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="QuantileGenomicRatioSet.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> + <expand macro="citations" /> +</tool> +