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 > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor > > >Best Regards > >Han Weinong > > > >--------------------------------- > > Click here to donate to the Hurricane Katrina relief effort. > [[alternative HTML version deleted]] > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor > > > >? 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