arrayWeights {limma} | R Documentation |
Estimates relative quality weights for each array in a multi-array experiment with replication.
arrayWeights(object, design = NULL, weights = NULL)
object |
object of class numeric , matrix , MAList , marrayNorm ,
exprSet or PLMset containing log-ratios or log-values of
expression for a series of microarrays. |
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. |
weights |
optional numeric matrix containing weights for each spot. |
The relative reliability of each array is estimated by measuring how well the expression values for that array follow the linear model.
A joint mean and dispersion model is fitted to the expression values for
each gene by calling the function lm.wfit
. The dispersion model
is fitted to the squared residuals from the mean fit, and is set up to
have array specific coefficients, which are updated in a gene-by-gene
manner. The final estimates of these array variances are converted to weights.
The arguments design
and weights
will be extracted from the data
object
if available and do not normally need to be set explicitly in
the call; if any of these are set in the call then they will over-ride
the slots or components in the data object
.
If object
is a PLMset
, then expression values will be taken
from the slot chip.coefs
and weights will be computed from
se.chip.coefs
. If object
is an exprSet
, then expression
values will be taken from the exprs
slot, but weights will not
be computed.
A matrix of array weights, suitable for use in the weights
argument of lmFit
.
Matthew Ritchie
An overview of linear model functions in limma is given by 5.LinearModels.
## Not run: array.wts <- arrayWeights(MA, design) fit.wts <- lmFit(MA, design, weights=array.wts) ## End(Not run)