Description Author(s) See Also

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

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