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.

### Usage

    gls.series(M,design=rep(1,ncol(M)),ndups=2,spacing=1,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
`correlation` | the inter-duplicate 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

Normally `dupcor.series` will be called before `gls.series` to estimate the inter-duplicate correlation. Then t-statistics will be formed to rank the genes in order of evidence for differential expression.

### 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

`dupcor.series`.

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

### Examples

    M <- matrix(rnorm(10*6),10,6)
    cor.out <- dupcor.series(M)
    #  See cor.out$cor for estimated common correlation
    fit <- gls.series(M,correlation=cor.out$cor)

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