[BioC] What wrong with my data using LIMMA
TEXTORIS Julien
julien.textoris at gmail.com
Tue Sep 13 20:20:38 CEST 2005
weinong han wrote:
>Hi,all
>
>If i want to downweight the lower quality arrays using the arrayWeights() function in limma, how to do? please in detail.
>
>Thanks
>
>Gordon Smyth <smyth at wehi.edu.au> wrote:
>At 08:08 AM 6/09/2005, Adaikalavan Ramasamy wrote:
>
>
>>Gordon, this is a good point that you raise. But can I ask you another,
>>somewhat harder, question ?
>>
>>What should one do when the an arrays fail quality assessment. Do we
>>simply omit it or can we possible correct for them. The decision to omit
>>might be the obvious one but it seems a bit wasteful when the the
>>proportion of arrays is large.
>>
>>I would be equally happy to hear from others on the list as well.
>>
>>Regards, Adai
>>
>>
>
>In some cases poor quality arrays will have to be dropped, but an
>alternative is to downweight the lower quality arrays using the
>arrayWeights() function in limma or array level standard errors from affyPLM.
>
>Gordon
>
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>
>Best Regards
>
>Han Weinong
>
>
>
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>
>? arrayWeights
in R that gives you this :
arrayWeights package:limma R Documentation
Array Quality Weights
Description:
Estimates relative quality weights for each array in a multi-array
experiment with replication.
Usage:
arrayWeights(object, design = NULL, weights = NULL, method =
"genebygene", maxiter = 50, tol = 1e-15, trace=FALSE)
Arguments:
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 prior weights for each
spot.
method: character string specifying the estimating algorithm to be
used. Choices are '"genebygene"' and '"reml"'.
maxiter: maximum number of iterations allowed.
tol: convergence tolerance.
trace: logical variable. If true then output diagnostic information
at each iteration of '"reml"' algorithm.
Details:
The relative reliability of each array is estimated by measuring
how well the expression values for that array follow the linear
model.
A heteroscedastic 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
either full REML scoring iterations, or using an efficient
gene-by-gene update algorithm. 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.
Value:
A matrix of array weights, suitable for use in the 'weights'
argument of 'lmFit'.
Author(s):
Matthew Ritchie
See Also:
An overview of linear model functions in limma is given by
06.LinearModels.
Examples:
## Not run:
array.wts <- arrayWeights(MA, design)
fit.wts <- lmFit(MA, design, weights=array.wts)
## End(Not run)
Regards,
julien
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