[BioC] How to validate normalization?

[krasikov@science.uva.nl]

Hi, Paul

Take a look on my previous posts:
[BioC] Channel splitting problem
[BioC] What to do with multiple probes?

There are I pose some questions,
unfortunately w/o firm answers yet,
and describe my design briefly

regards
Vladimir

Paul Lang wrote:
> what is your experimental design / chip system?
> 
> I am using single-colour cDNA arrays and stablilizing with vsn, and 
> would be interested to see how it works on other setups.
> 
> best
> 
> Paul Lang
> 
> 
> -----Original Message-----
> From: bioconductor-bounces at stat.math.ethz.ch on behalf of 
> krasikov at science.uva.nl
> Sent: Thu 12/1/2005 5:58 AM
> To: bioconductor at stat.math.ethz.ch
> Subject: [BioC] How to validate normalization?
> 
> 
> 
> 
> -----Original Message-----
> From: bioconductor-bounces at stat.math.ethz.ch on behalf of 
> krasikov at science.uva.nl
> Sent: Thu 12/1/2005 5:58 AM
> To: bioconductor at stat.math.ethz.ch
> Subject: [BioC] How to validate normalization?
> 
> Dear all
> 
> Here I post again the question about normalization
> I'm sorry that this question might be obvious for statistician.
> 
> The general question:
> How to validate the normalization outcome?
> Density plots?
> I have tried "loees with aquantile" and "vsn" and outcome of the
> decideTests is more or less the same - a lot of probes with differential
> expression.
> 
> Here below the code I used in limma:
> 
> RG <- read.maimages(...)
> ...assigning spotTypes
> ...removing controlspots from the RG
> RGb <- backgroundCorrect(RG,method="minimum")
> MA <- normalizeWithinArrays(RGb, method="loess")
> MA <- normalizeBetweenArrays(MA, method="Aquantile")
> ...design
> fit <- lmFit(MA, design)
> ...contrast.matrix
> fit <- contrasts.fit(fit, contrast.matrix)
> fit <- eBayes(fit)
> res <- decideTests(fit, method = "separate", adjust.method="BH",
> + p.value=0.001)
> write.fit(fit, results = res, file = "...", digits=2, adjust="BH", sep="\t")
> 
> In that condition I've got 1800 up and 1800 down probes (out from 8100)
> Decreasing p.value to 0.0001 gave me 800 up and 800 down.
> 
> I would like to mention here, that quite a big part of obtained data
> is physiologically relevant in my experiment,
> and the nature of the experiment suggests big differential expression.
> 
> Thanks in advance for any comments on this?
> 
> Best wishes
> Vladimir
> 
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> 
>