[BioC] Bioc-devel Digest, Vol 57, Issue 4

[rcaloger]

Dear Eske,
this mail should be delivered to the bioconductor maliling list bioconductor at stat.math.ethz.ch and not to the developers mailing list bioc-devel at stat.math.ethz.ch

Therefore, I am answering you and delivery the answer to the Bioc mailing list.
Normalization is the process of removing non-biological sources of variation between array experiments. Quantile normalization method is based on the assumption that all samples belong to the same distribution, therefore the number of differentially expressed genes present in the data set are not enough to affect the overall distribution of the data. In your case the quantile method is not the right approach since you have more than 80% changes in the treated sample.

I have seen an article on the normalization for boutique microarrays that might apply to your problem:
http://www.biomedcentral.com/1471-2105/6/37

A more general concern on your experiment is associated to the number of replications. Two arrays for each condition, especially if you wish to evaluate exon-level changes, are really too little in number.

eskeatnaf mulugeta wrote:

> Date: Fri, 19 Dec 2008 08:35:29 -0800 (PST)
> From: eskeatnaf mulugeta <eskeww at yahoo.com>
> Subject: [Bioc-devel] normalize an affymetrix exon array
> To: bioc-devel at stat.math.ethz.ch
> Message-ID: <343887.57174.qm at web51702.mail.re2.yahoo.com>
> Content-Type: text/plain
>
> Dear All, 
> I am trying to normalize an affymetrix exon array. I can use
> the usual normalization methods that are implemented in different packages. But
> my problem is a bit different here. 
> I have 4 samples, 4 affy exon microarrays. Two are treated
> with let¢s say a drug. The other one is a normal array. But what I sould
> mention here is that, the difference between the plus and normal sample is very
> large. There will be a more than 80% difference in expression. The genes in the
> plus sample will go high. My problem here I don¢t want to miss this difference,
> it is very crucial for my analysis. 
> When I applied the Quintile or other normalizations, the
> normal samples are scaled up and I can¢t see these big differences I want to
> see. The normal array is scaled up. The global normalization techniques, which
> were designed with the idea that, only few genes are differentially expressed
> does not work in my case.  What kind of
> solution will you suggest? 
> I know I can use the internal controls, but they will not
> work in my case. It is a very new tech I am testing. 
> Could you please suggest a good normalization method that
> can remove all the systemic bias without scaling up the other array? 
> With kind regards
> eske
>
>
>
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> Sent: Friday, December 19, 2008 12:00:03 PM
> Subject: Bioc-devel Digest, Vol 57, Issue 3
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>    1. Adding tables to an annotation package (Sean Davis)
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> ----------------------------------------------------------------------
>
> Message: 1
> Date: Thu, 18 Dec 2008 08:17:51 -0500
> From: "Sean Davis" <sdavis2 at mail.nih.gov>
> Subject: [Bioc-devel] Adding tables to an annotation package
> To: bioC-devel <bioc-devel at stat.math.ethz.ch>,    "Marc Carlson"
>     <mcarlson at fhcrc.org>
> Message-ID:
>     <264855a00812180517q75ab202dp4f136161565bf5db at mail.gmail.com>
> Content-Type: text/plain; charset=UTF-8
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> Hi, Marc.
>
> I am looking to build an annotation package with all the usual
> information but will need several extra tables (still bimap, though).
> Is there a suggested way to do this using AnnotationDbi/SQLForge?
>
> Thanks,
> Sean
>
>
>
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-- 

----------------------------------------
Prof. Raffaele A. Calogero
Bioinformatics and Genomics Unit
Dipartimento di Scienze Cliniche e Biologiche
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