Cheers!

I think this is applicable to our needs.

Thank you Gentlemen!


-----Original Message-----

From: Gordon K Smyth <smyth@wehi.EDU.AU>

To: Michael Breen <mbreen@vapop.ucsd.edu>

Cc: Bioconductor mailing list <bioconductor@r-project.org>

Date: Tue, 11 Jun 2013 10:44:34 +1000 (AUS Eastern Standard Time)

Subject: DESEQ ANODEV : A time course study




Dear Michael,



What you want to do is easy and fast using the edgeR package, without any 

need for ad hoc workarounds like subsetting your data.  See McCarthy et 

all (NAR 2012):



  http://www.ncbi.nlm.nih.gov/pubmed/22287627 
[http://www.ncbi.nlm.nih.gov/pubmed/22287627]



Best wishes

Gordon





> Date: Mon, 10 Jun 2013 08:41:49 +0200

> From: Simon Anders <anders@embl.de>

> To: bioconductor@r-project.org

> Subject: Re: [BioC] DESEQ ANODEV : A time course study

>

> Hi Michael

>

> On 08/06/13 03:00, Michael Breen wrote:



>> What we aim to do is to test for DE of transcripts across all 3 time 

>> points for disease and controls seperatly (using DESeq ANODEV) but we 

>> want to be able to identify at which time points these transcripts are 

>> being DE. In other words, we want to compare DE transcripts with 

>> respect to specific time points between cases and controls. Our 

>> remaining code looks like this:

>>

>> fit0 <- fitNbinomGLMs (cds, count ~ timecourse)

>> fit1 <- fitNbinomGLMs ( cds, count ~ timecourse + condition )

>> str(fit1)

>

> One possibility would be to subset your data to only samples from one 

> time point and then test cases against control to see the genes that are 

> DE at this time point, then go on to the next one. If you consider this 

> a post-hoc test and only look at the genes which show overall 

> sensitivity, you can probably be more lenient on the significance 

> threshold. Maybe other people on the list have input on this point.

>

>    Simon



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