beadCountWeights: Bead Count Weights for Illumina BeadChips
In richierocks/limma2: Linear Models for Microarray Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Estimates weights which account for biological variation and technical variation resulting from varying bead numbers.

1	beadCountWeights(y, x, design = NULL, bead.stdev = NULL, nbeads = NULL, array.cv = TRUE, scale = FALSE)

`y`	normalized log2-expression values.
`x`	raw expression values.
`design`	the design matrix of the microarray experiment, with rows corresponding to arrays and columns to coefficients to be estimated. Defaults to the unit vector meaning that the arrays are treated as replicates.
`bead.stdev`	numeric matrix containing bead-level standard deviations.
`nbeads`	numeric matrix containing number of beads.
`array.cv`	logical, should technical variation for each observation be calculated from a constant or array-specific coefficient of variation? The default is to use array-specific coefficients of variation.
`scale`	logical, should weights be scaled so that the average weight size is the mean of the inverse technical variance along a probe? By default, weights are scaled so that the average weight size along a probe is 1.

This function estimates optimum weights using the bead statistics for each probe for an Illumina expression BeadChip. It can be used with any Illumina expression BeadChip, but is most likely to be useful with HumanHT-12 BeadChips.

Arguments x and y are both required. x contains the raw expression values and y contains the corresponding log2 values for the same probes and the same arrays after background correction and normalization. x and y be any type of object that can be coerced to a matrix, with rows corresponding to probes and columns to arrays. x and y must contain the same rows and columns in the same order.

The reliability of the normalized expression value for each probe on each array is measured by estimating its technical and biological variability. The bead number weights are the inverse sum of the technical and biological variances.

The technical variance for each probe on each array is inversely proportional to the number of beads and is estimated using array-specific bead-level coefficients of variation.

Coefficients of variation are calculated using raw expression values.

The biological variance for each probe across the arrays are estimated using a Newton iteration, with the assumption that the total residual deviance for each probe from lmFit is inversely proportional to the sum of the technical variance and biological variance.

If any of the arguments design, bead.stdev or nbeads are set explicitly in the call they will over-ride the slots or components in the data object. The argument design does not normally need to be set in the call but will be extracted from the data object if available. If arguments bead.stdev and nbeads are not set explicitly in the call, it is necessary that they are available for extraction from the data object.

A list object with the following components:

`weights`	numeric matrix of bead number weights
`cv.constant`	numeric value of constant bead-level coefficient of variation
`cv.array`	numeric vector of array-specific bead-level coefficient of variation
`var.technical`	numeric matrix of technical variance
`var.biological`	numeric vector of biological variance

Charity Law and Gordon Smyth

Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia. http://repository.unimelb.edu.au/10187/17598

An overview of linear model functions in limma is given by 06.LinearModels.

## Not run: 
ps <- read.ilmn(files="probesummaryprofile.txt",
     ctrfiles="controlprobesummary.txt",
     other.columns=c("BEAD_STDEV","Avg_NBEADS"))
y <- neqc(ps)
x <- ps[ps$genes$Status=="regular",]
bcw <- beadCountWeights(y,x,design)
fit <- lmFit(y,design,weights=bcw$weights)
fit <- eBayes(fit)

## End(Not run)