rlm.series {limma} | R Documentation

## Robust Linear Model for Series of Microarrays

### Description

Fit linear models for each gene to a series of microarrays. Fit is by robust M-estimation.

### Usage

    rlm.series(M,design=NULL,ndups=1,spacing=1,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
`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.
`...` | any other arguments are passed to `rlm.default`.

### Details

The linear model is fit for each gene by calling the function `rlm` from the MASS library.

Warning: don't use weights with this function unless you understand how `rlm` treats weights. The treatment of weights is different from that of `lm.series` and `gls.series`.

### 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`.

### Author(s)

Gordon Smyth

### See Also

`rlm`.

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

### Examples

    #  Simulate gene expression data,
    #  6 microarrays and 100 genes with one gene differentially expressed in first 3 arrays
    M <- matrix(rnorm(100*6,sd=0.3),100,6)
    M[1,1:3] <- M[1,1:3] + 2
    #  Design matrix includes two treatments, one for first 3 and one for last 3 arrays
    design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
    fit <- rlm.series(M,design=design)
    eb <- ebayes(fit)
    #  Large values of eb$t indicate differential expression
    qqt(eb$t[,1],df=fit$df+eb$df.prior)
    abline(0,1)

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