[BioC] right normalization?
Guido.Hooiveld at wur.nl
Mon Sep 26 11:02:04 CEST 2011
A fourth one could be to use ComBat:
Guido Hooiveld, PhD
Nutrition, Metabolism & Genomics Group
Division of Human Nutrition
Biotechnion, Bomenweg 2
NL-6703 HD Wageningen
tel: (+)31 317 485788
fax: (+)31 317 483342
email: guido.hooiveld at wur.nl
From: bioconductor-bounces at r-project.org [mailto:bioconductor-bounces at r-project.org] On Behalf Of Wolfgang Huber
Sent: Monday, September 26, 2011 10:48
To: bioconductor at r-project.org
Subject: Re: [BioC] right normalization?
I see three options, in order of complexity:
- add a 'batch' factor into the linear model that you fit with limma in order to absorb the batch effect
- do the limma analysis separately for both batches, then combine afterwards
- use the 'sva' package.
Sep/26/11 10:29 AM, andrea.grilli at ior.it scripsit::
> Hi list,
> I've got 16 gene chip affymetrix arrays of 2 cell lines at different
> time/conditions. Chips were done in 2 different moments, and this bias
> seems to affect my analysis.
> I've normalized with RMA and analyzed with eBayes models to compare
> the 2 cell lines at each time point.
> Following analysis are affected by the above mentioned "time bias",
> like in clustering were data are grouped according to when the arrays
> were hybridized.
> - Do you suggest a different normalization? Which one could reduce
> this effect?
> - could be a good approach normalizing the 2 groups of samples
> separately (will be 8 and 8) and merging the data after normalization?
> I did quality control steps like in bioconductor book, and all
> parameters are in the right ranges. Also box plot shows a similar
> expression range across samples.
> Any help will be really appreciated,
> thanks in advance,
> PS This is a semplification of a previous mail, sorry for the repetition.
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