04.Background {limma} | R Documentation |
This page deals with background correction methods provided by the backgroundCorrect
, kooperberg
or neqc
functions.
Microarray data is typically background corrected by one of these functions before normalization and other downstream analysis.
backgroundCorrect
works on matrices, EListRaw
or RGList
objects, and calls backgroundCorrect.matrix
.
The movingmin
method of backgroundCorrect
uses utility functions ma3x3.matrix
and ma3x3.spottedarray
.
The normexp
method of backgroundCorrect
uses utility functions normexp.fit
and normexp.signal
.
kooperberg
is a Bayesian background correction tool designed specifically for two-color GenePix data.
It is computationally intensive and requires several additional columns from the GenePix data files.
These can be read in using read.maimages
and specifying the other.columns
argument.
neqc
is for single-color data.
It performs normexp background correction and quantile normalization using control probes.
It uses utility functions normexp.fit.control
and normexp.signal
.
If robust=TRUE
, then normexp.fit.control
uses the function huber
in the MASS package.
Gordon Smyth
01.Introduction, 02.Classes, 03.ReadingData, 04.Background, 05.Normalization, 06.LinearModels, 07.SingleChannel, 08.Tests, 09.Diagnostics, 10.GeneSetTests, 11.RNAseq