[BioC] RNASeq: normalization issues

[Biase, Fernando]

Dr. Wei,

If  I may I ask. What criteria do you use to find out which normalization suits better your data?

thanks,
Fernando

________________________________________
From: bioconductor-bounces at r-project.org [bioconductor-bounces at r-project.org] On Behalf Of Wei Shi [shi at wehi.EDU.AU]
Sent: Thursday, April 28, 2011 6:07 PM
To: João Moura
Cc: bioconductor at r-project.org list
Subject: Re: [BioC] RNASeq: normalization issues

Hi João:

        Maybe you can try different normalization methods for your data to see which one looks better. How to best normalize RNA-seq data is still of much debate at this stage.

        You can try scaling methods like TMM, RPKM, or 75th percentile, which as you said normalize data within samples. Or you can try quantile between-sample normalization (read counts should be adjusted by gene length first), which performs normalization across samples. You can try all these in edgeR package.

        From my experience, I actually found the quantile method performed better for my RNA-seq data. I used general linear model and likelihood ratio test in edgeR in my analysis.

        Hope this helps.

Cheers,
Wei

On Apr 28, 2011, at 7:36 PM, João Moura wrote:

> Dear all,
>
>
> Until now I was doing RNAseq DE analysis and to do that I understand that
> normalization issues only matter inside samples, because one can assume the
> length/content biases will cancel out when comparing same genes in different
> samples.
> Although, I'm now trying to compare correlation of different genes and so,
> this biases should be taken into account - for this is there any better
> method than RPKM?
>
> My main doubt is if I should also take into acount the biases inside samples
> and to do that is there any better approach then TMM by Robinson and Oshlack
> [2010]?
>
> Thank you all,
> --
> João Moura
>
>       [[alternative HTML version deleted]]
>
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