preprocess {Ringo}R Documentation

Preprocess Nimblegen Raw Intensities

Description

Calls one of various limma functions to transform raw probe intensities into (background-corrected) normalized log ratios (M-values).

Usage

preprocess(myRG, method="vsn", ChIPChannel="R", inputChannel="G",
           returnMAList=FALSE, idColumn="PROBE_ID", verbose=TRUE, ...)

Arguments

myRG object of class RGList
method string; denoting which normalization method to choose, see below for details
ChIPChannel string; which element of the RGList holds the ChIP result, see details
inputChannel string; which element of the RGList holds the untreated input sample; see details
returnMAList logical; should an MAList object be returned? Default is to return an ExpressionSet object.
idColumn string; indicating which column of the genes data.frame of the RGList holds the identifier for reporters on the microarray. This column, after calling make.names on it, will make up the unique featureNames of the resulting ExpressionSet. If argument returnMAList is TRUE, this argument is ignored.
verbose logical; progress output to STDOUT?
... further arguments to be passed on normalizeWithinArrays and normalizeBetweenArrays

Details

The procedure and called limma functions depend on the choice of method.

loess
Calls normalizeWithinArrays with method="loess".
vsn
Calls normalizeBetweenArrays with method="vsn".
Gquantile
Calls normalizeBetweenArrays with method="Gquantile".
Rquantile
Calls normalizeBetweenArrays with method="Rquantile".
median
Calls normalizeWithinArrays with method="median".
nimblegen
Scaling procedure used by Nimblegen. Yields scaled log-ratios by a two step procedure: srat = log2(R) - log2(G) srat = srat - tukey.biweight(srat)
Gvsn
Learns vsn model on green channel intensities only and applies that transformation to both channels before computing fold changes.
Rvsn
Learns vsn model on red channel intensities only and applies that transformation to both channels before computing fold changes.
none
No normalization of probe intensities, takes raw log2(R)-log2(G) as component M and (log2(R)+log2(G))/2 as component A; uses normalizeWithinArrays with method="none".

Mostly with two-color ChIP-chip, the ChIP sample is marked with the red Cy5 dye and for the untreated input sample the green Cy3 dye is used. In that case the RGListmyRG's element R holds the ChIP data, and element G holds the input data. If this is not the case with your data, use the arguments ChIPChannel and inputChannel to specify the respective elements of myRG.

Value

Returns normalized, transformed values as an object of class ExpressionList or MAList.

Note

Since Ringo version 1.5.6, this function does not call limma's function backgroundCorrect directly any longer. If wanted by the user, background correction should be indicated as additional arguments passed on to normalizeWithinArrays or normalizeBetweenArrays, or alternatively call backgroundCorrect on the RGList before preprocessing.

Author(s)

Joern Toedling toedling@ebi.ac.uk

See Also

normalizeWithinArrays, normalizeBetweenArrays, malist,ExpressionSet, vsnMatrix

Examples

   exDir <- system.file("exData",package="Ringo")
   exRG <- readNimblegen("example_targets.txt","spottypes.txt",
                         path=exDir)
   exampleX <- preprocess(exRG)
   sampleNames(exampleX) <- make.names(paste(exRG$targets$Cy5,"vs",
                                        exRG$targets$Cy3,sep="_"))
   print(exampleX)
   ### compare VSN to NimbleGen's tukey-biweight scaling
   exampleX.NG <- preprocess(exRG, method="nimblegen")
   sampleNames(exampleX.NG) <- sampleNames(exampleX)
   if (interactive())
     corPlot(cbind(exprs(exampleX),exprs(exampleX.NG)),
       grouping=c("VSN normalized","Tukey-biweight scaled"))

[Package Ringo version 1.6.0 Index]