gls.series {limma}R Documentation

Generalized Least Squares for Series of Microarrays

Description

Fit linear models for each gene to a series of microarrays. Fit is by generalized least squares allowing for correlation between duplicate spots or between technical replicates.

Usage

gls.series(M,design=NULL,ndups=2,spacing=1,block=NULL,correlation=NULL,weights=NULL,...)

Arguments

M a numeric matrix. Usually the log-ratios of expression for a series of cDNA microarrrays with rows corresponding to genes and columns to arrays.
design the design matrix of the microarray experiment, with rows corresponding to arrays and columns to comparisons to be estimated. The number of rows must match the number of columns of M. Defaults to the unit vector meaning that the arrays are treated as replicates.
ndups a positive integer giving the number of times each gene is printed on an array. nrow(M) must be divisible by ndups.
spacing the spacing between the rows of M corresponding to duplicate spots, spacing=1 for consecutive spots
block vector or factor specifying a blocking variable
correlation the inter-duplicate or inter-technical replicate correlation.
weights an optional numeric matrix of the same dimension as M containing weights for each spot. If it is of different dimension to M, it will be filled out to the same size.
... other optional arguments to be passed to dupcor.series.

Details

This is the low level function for fitting gene-wise linear models when there is a correlation structure arising from duplicate spots or technical replicates. This function is called by lmFit if appropriate. Note that the correlation is assumed to be constant across genes.

If correlation=NULL then a call is made to duplicateCorrelation to estimated the correlation.

Value

A list with components

coefficients numeric matrix containing the estimated coefficients for each linear model. Same number of rows as M, same number of columns as design.
stdev.unscaled numeric matrix conformal with coef containing the unscaled standard deviations for the coefficient estimators. The standard errors are given by stdev.unscaled * sigma.
sigma numeric vector containing the residual standard deviation for each gene.
df.residual numeric vector giving the degrees of freedom corresponding to sigma.
correlation inter-duplicate correlation.

Author(s)

Gordon Smyth

See Also

duplicateCorrelation.

An overview of linear model functions in limma is given by 5.LinearModels.

Examples

M <- matrix(rnorm(10*6),10,6)
dupcor <- duplicateCorrelation(M)
fit <- gls.series(M,correlation=dupcor$consensus.correlation)

[Package limma version 1.6.7 Index]