Il giorno Dec 15, 2012, alle ore 5:53 PM, "Akula, Nirmala (NIH/NIMH)
[C]" <akulan at="" mail.nih.gov=""> ha scritto:
> What would be a reasonable/widely used cut-off for overall variance
and overall sum?
> Thanks for pointing out the number format. The example I gave is
from eXpress software and I rounded the numbers to closest integer
before I input into DESeq
it's a bit more subtle than that. DESeq expects actual counts of
fragments, please do read the DESeq vignette.
I have no experience with combining eXpress and DESeq, or whether what
you are doing is scientifically valid, but unless you are comfortable
with making your own statistical models and strategies, I'd recommend
following an established path rather than cutting your own - where you
would be on your own.
> From: Wolfgang Huber [whuber at embl.de]
> Sent: Saturday, December 15, 2012 11:05 AM
> To: Davis, Sean (NIH/NCI) [E]
> Cc: Akula, Nirmala (NIH/NIMH) [C]; bioconductor at r-project.org
> Subject: Re: [BioC] filtering before using DESeq
> Dear Akula, Sean
> besides overall variance, overall sum is also a good filter
> Akula, please note that DESeq expects counts, which need to be
positive integer values. The values you state are not integers.
> Best wishes
> Il giorno Dec 14, 2012, alle ore 10:45 PM, Sean Davis <sdavis2 at="" mail.nih.gov=""> ha scritto:
>> On Fri, Dec 14, 2012 at 2:42 PM, Akula, Nirmala (NIH/NIMH) [C] <
>> akulan at mail.nih.gov> wrote:
>>> We counted the reads in our RNA-seq data using HT-seq and removed
>>> isoforms that have <5 reads/sample. We then used DESeq for
>>> expression analysis.
>>> Here's an example of a transcript that has the following read
>>> GeneA_cases counts:
>>> GeneA_control counts:
>>> DESeq p-value for GeneA is 10-4. Do we have to filter out
>>> (that have high variance between samples as shown in the above
>>> before giving the data to DESeq or will DESeq take this into
>>> calculating the normalization?
>> Hi, Nirmala.
>> If you mean filtering out transcripts that show one or more
>> a given group, then you should ABSOLUTELY NOT do that as this will
>> your statistical results. If you mean filtering based on overall
>> (across groups) to find highly-variable transcripts, that is a
>> story and is acceptable.
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