Thanks Steve and Mike.

Ah, yes, thanks for the clarification that it is the mean of the NORMALIZED counts, it all makes sense now. I was comparing to the mean of raw counts. I guess- interpret the the "rows" being samples, but yes the word "samples" clarifies things definitively. 

After thinking a bit on the LFC question, I think I have a logical error in constructing the contrast, and would like to get some help. 

My design is 2 cell lines (C1, C2) and 4 treatments (T1,T2,T3,TVeh). 

---My intention is to compare C1.T1 vs. C2.T1, and ask for the cell-specific effect of treatment T1---

Normally this is broken down into two steps (below) but I wondered about 1-step comparison using interaction terms:
1) first find sig DEs of (C1.T1 over C1.TVeh), and conversely (C2.T1 over C2.TVeh). 
2) then compare these delta DEs (over vehicle) from their respective cell lines, to find delta delta DEs. 

So for the LFC, I want to take the straight normalized counts fold change: log2(C1.T1/C2.T1). It is the p-value that will change depending what hypotheses I am testing. Now as I am typing it out loud, I realize that the LFC and p-value are both dependent on the contrasting terms that I put in, which brings to my massive confusion with interaction terms.

inputFormula=formula(~celltype+treatment+celltype:treatment)
dds_fit=DESeqDataSetFromMatrix(countData=inputData,colData=inputDesign,design=inputFormula)
dds_fit=nbinomWaldTest(dds_fit,betaPrior=TRUE,maxit=local_maxit,useOptim=TRUE)

res1=results(dds_fit, contrast=list("celltypeC1.treatmentT1","celltypeC2.treatmentT1"))
res2=results(dds_fit, contrast=list(c("treatmentT1","celltypeC2.treatmentT1"),
                                   c("treatmentTVeh","celltypeC2.treatmentTVeh")))
res3=results(dds_fit, contrast=list("celltypeC1.treatmentT1","celltypeC1.treatmentTVeh"))


My interpretation is that:
res1 is saying, contrasting C1-specific T1 effect over C2-specific T1 effect that is different from main effect of T1.
res2 is saying, contrasting T1 effect over veh in cell type C2 that is different from any other cell types (C1 in this case)
res3 is saying, contrasting C1-specific T1 effect over C1-specific TVeh effect that is different from those of C2-specific counter parts

To me, all 3 of the above contrasts answer my intended question of C1.T1 vs C2.T1. 

Could someone help me with answering my intended question. And also corrections to my interpretations to the above contrast will also be very much appreciated.

-Rob

Date: Fri, 1 Aug 2014 19:38:12 -0400
Subject: Re: [BioC] DESeq2 basemean and log2foldchange
From: michaelisaiahlove@gmail.com
To: nextgame@hotmail.com
CC: bioconductor@r-project.org

Hi Rob,

On Aug 1, 2014 2:20 PM, "rob yang" <nextgame@hotmail.com> wrote:

>

> Hello DESeq community,

>

> I wanted to ask how basemean and log2foldchange are calculated.

>

> 1) From mcol(mcol(dds_fit)), the basemean is denoted as "basemean over all rows". But I can't pin down what "all rows" this means. For a single transcript, my manual mean calculation of all counts across all replicates, all conditions do not equal to the DESeq reported basemean for a single transcript.


>
Yes that is kind of funny, I should change it to say "columns" or "samples". It is just as Steve described, the mean of normalized counts for all samples in the dds.
> 2) log2foldchange seems to be changing with the contrasting variable. This is a little surprising since I'd expect log2foldchange to be a static number when comparing two conditions, and the contrasting variables only dictate testing for significance, ie., p-value.


>
I don't follow. Can you say more what you mean by changing with contrasting variable
Mike
> Thank you in advance.

>

> -Rob

>

>         [[alternative HTML version deleted]]

>

> _______________________________________________

> 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]]

