# HG changeset patch
# User kpbioteam
# Date 1550841121 18000
# Node ID f47e5cca1696abaa206e43a5f57ee12bd9bc4c3e
# Parent  4f34e3f73ebfd9154bb852743fa83c64b6573e3d
Uploaded

diff -r 4f34e3f73ebf -r f47e5cca1696 minfi_ppquantile.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/minfi_ppquantile.xml	Fri Feb 22 08:12:01 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>
+