loess.normalize {affy}R Documentation

Normalize arrays


This function treats PM and MM as the raw data on each chip. It fits loess curves to MVA plots and tries to normalize the chips with respect to each other by forcing log ratios to be scattered around the same constant.


loess.normalize(mat, subset = sample(1:(dim(mat)[2]), 5000), epsilon
                 = 10^-2, maxit = 1, log.it = TRUE, verbose = TRUE,
                 span = 2/3, family.loess = "symmetric")


mat a matrix with columns containing the values of the chips to normalize.
subset a subset of the data to fit a loess to.
epsilon small value used for the stopping criterion.
maxit maximum number of iterations.
log.it logical. If TRUE it takes the log2 of mat
verbose logical. If TRUE displays current pair of chip being worked on.
span span to be used by loess
family.loess "gaussian" or "symmetric" as in loess


Experience shows that you only need 1-2 iterations to obtain useful results. This function is not written in an efficient way. In order to make it faster, loess is fit to a sample of the data which we then use to predict the curve for all the data. By setting family.loess="gaussian" the function is faster, but you risk losing information on differentially expressed genes. The function normalize.quantiles is faster.


A matrix with normalized values for chips in columns.


Rafael A. Irizarry

See Also

normalize.quantiles, maffy.normalize, maffy.subset

[Package affy version 1.8.1 Index]