| 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