[BioC] Illumina - Beadarray - Limma

[Gordon Smyth]

>Date: Thu, 15 Feb 2007 10:45:16 -0500
>From: Nieves Velez de Mendizabal <nievesvelez at terra.es>
>Subject: [BioC] Illumina - Beadarray - Limma
>To: bioconductor at stat.math.ethz.ch
>Message-ID: <45D4800C.6010706 at terra.es>
>Content-Type: text/plain; charset=ISO-8859-1; format=flowed
>
>We are analyzing some data of Illumina. There are three kind of
>normalization. First of them is the method of rank invariant
>normalization, recommended by Illumina, and we would like to apply it:
>
>
>     BSData.bgnorm = backgroundNormalise(BSData)
>     T = apply(exprs(BSData.bgnorm), 1, mean)
>     BSData.rankinv = assayDataElementReplace(BSData.bgnorm, "exprs",
>     rankInvariantNormalise(exprs(BSData.bgnorm), T))
>
>
>But in BSData.rankinv I have negative values so I cannot apply the
>method lmFit in order to analyze the differential expression because of
>the log2 transformation applied.
>
>     fit = lmFit(log2(exprs(BSData.rankinv)), design)

Why can't you do this?

I personally think it is silly to introduce negative values, but it 
doesn't stop you using lmFit().

Best wishes
Gordon

>Are these two methods (rank inv method and lmFit) incompatible?
>What kind of normalization should I use in order to search
>differentially expressed genes in micro arrays of Illumina?
>
>Thanks