Hi,
I have been exploring my data in terms of gene expression in a lot of ways
(microarray, RNAseq, qPCR) and for my case it is important to account for
the first two PC for analysis of differential expression, which is easy for
the microarray data.

I am thinking about transforming my RNAseq data, but a lot of the genes
have very low counts. Won't either rlog or voom transformation require the
counts to be high to be valid?
Should I filter low count instances before doing the transformation and the
PCA? how low is too low for ti to work?

thanks
Lucia


On Tue, Apr 23, 2013 at 2:46 PM, Simon Anders <anders@embl.de> wrote:

> Hi
>
>
> On 23/04/13 19:10, Stephen Turner wrote:
>
>> Short story: I ran a PCA on a matrix of counts from an RNA-seq
>> experiment and then using the voom-tramsformed data, and they're
>> completely different. I imagine my results will be very different
>> using DESeq2 vs limma, and I am wondering how to proceed. Here are the
>> two PCA biplots:
>>
>
> Steve has already answered the question, but just for the record: Running
> PCA on a count matrix is never a good idea.
>
> PCA expects data to be homoscedastic and counts are not. There are various
> ways of transforming count data to a homoscedastic scale, and DESeq2's rlog
> and limma's voom are two of them.
>
>   Simon
>
>
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-- 
Lucia Peixoto PhD
Postdoctoral Research Fellow
Laboratory of Dr. Ted Abel
Department of Biology
School of Arts and Sciences
University of Pennsylvania

"Think boldly, don't be afraid of making mistakes, don't miss small
details, keep your eyes open, and be modest in everything except your
aims."
Albert Szent-Gyorgyi

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