Normalizes microarray expression intensities using different methods with or without background correction.
numeric matrix of intensities data where each row corresponds to a probe (gene, transcript), and each column correspondes to a specimen (patient).
numeric vector specifying numbers of rows containing negative controls (non-coding areas).
character string specifying normalization method. Possible values are:
logical value indicating whether rows with negative control should be deleted from intensity matrix after normalization.
logical value indicating whether background correction should be done before normalization.
Could be used for background correction only (without data normalization) if
This function is intended to normalize microarray intensities data between arrays.
Background correction is optional.
Background correction method is "normexp", which is based on a convolution model (Ritchie, 2007). See
backgroundCorrect for details.
Quantile normalization method implies that we can give each array the same distribution See
normalize.quantiles for details.
Subset quantile normalization is performed based on a subset of negative (or non-coding) controls according to (Wu and Aryee, 2010). Number of normal distributions in the mixture approximation is 5, weight given to the parametric normal mixture model is 0.9. See
SQN for details.
Cyclic loess normalization implements method of Ballman et al (2004), whereby each array is normalized to the average of all the arrays. See
normalizeCyclicLoess for details.
A matrix of the same dimensions as
Matrix containing normalized values with or without
background correction. If
leaveNC=FALSE the function returns a matrix with
normalized values without rows containing negative controls.
Elena N. Filatova
Ballman K.V., Grill D.E., Oberg A.L. and Therneau T.M. (2004). Faster cyclic loess: normalizing RNA arrays via linear models. Bioinformatics 20, 2778-2786.
Bolstad B.M., Irizarry R.A., Astrand M. and Speed T.P. (2003) A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2), 185-193. https://doi.org/10.1093/bioinformatics/19.2.185
Ritchie M.E., Silver J., Oshlack A., Silver J., Holmes M., Diyagama D., Holloway A. and Smyth G.K. (2007). A comparison of background correction methods for two-colour microarrays. Bioinformatics 23, 2700-2707. https://doi.org/10.1093/bioinformatics/btm412
Wu Z and Aryee M. (2010). Subset Quantile Normalization using Negative Control Features. Journal of Computational Biology 17(10), 1385-1395. https://doi.org/10.1089/cmb.2010.0049
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