04.Background {limma} | R Documentation

## Topic: Background Correction

### Description

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.

### Author(s)

Gordon Smyth

### See Also

01.Introduction, 02.Classes, 03.ReadingData, 04.Background, 05.Normalization, 06.LinearModels, 07.SingleChannel, 08.Tests, 09.Diagnostics, 10.GeneSetTests, 11.RNAseq

[Package limma version 3.42.2 Index]