Hi Simon,

Thank you for the reply

As you said in the post, I was plotting to see if the size factor was hitting the median, in some samples (3), the size factor was way off the median and hence I tried shorth, but this didn't help either, and I tried filtering down the genes (filtering genes on row sums for 10,15,20, 25...) but again in the plot size factor was way off, so I was wondering what I should follow.

regards,
Sudeep.


________________________________
 From: Simon Anders <anders@embl.de>
To: bioconductor@r-project.org 
Sent: Tuesday, 7 August 2012 12:59 PM
Subject: Re: [BioC] DESeq normalization

Hi Sudeep

On 2012-08-01 11:47, sudeep s wrote:

> I was checking DESeq normalization success for my samples following
> the suggestions in this post :
> https://stat.ethz.ch/pipermail/bioconductor/2010-October/035933.html.
> I observed that filtering down to the genes that are present in both
> case and control samples improved normalization  (visualized by
> plotting as per the post), but for a few samples ,say  3 samples
> these procedures did not help, and I tried calculating
 shorth
> estimator (again as per the post) and this did n't help either. My
> question is what should I follow for the normalization of these
> samples ?

You would need to tell us what exactly you did not like about the histograms.

Anyway, I revised my opinion a bit, and I now think that MA plots are mroe helpful.

Starting as in the post that you cited, plot an MA plot instead of a histogram, with

plot( geomeans, log2( counts(cds)[,j] / geomeans ), log="x",
   pch=".", col="#00000060" )
abline( h=log2( sizeFactors(cds)[ j ] ), col="red" )


  Simon

_______________________________________________
Bioconductor mailing list
Bioconductor@r-project.org
https://stat.ethz.ch/mailman/listinfo/bioconductor
Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
	[[alternative HTML version deleted]]

