Dear Gordon,

Thank you for the wonderful help. I will Run edgeR based on your
suggestions and let you know if I have any problems/questions.

Again, thank you for the wonderful support you are providing to the
research community.


Tilahun
-----------------------

On Thu, Feb 2, 2012 at 7:16 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Dear Tilahun,
>
> There is no way to specify a random block in edgeR, nor in any other
> existing statistical software designed to analyse RNA-Seq counts. However
> there is no purpose in doing so for your experiment.  The only reason that
> you might want to define block as random rather than fixed is to recover
> inter-block information about the experimental treatments. Your experiment
> is entirely balanced, in that all treatment conditions appear in each
> block, so there is no inter-block information to recover. Hence the edgeR
> analysis that I have suggested, which uses intra-block information only, is
> fully efficient.
>
> If you want to test for a treatment effect in each individual tissue:
>
>  design <- model.matrix(~Tissue+Tissue:**Treatment+Block)
>
> Then
>
>  lrtA <- glmLRT(d, fit, coef="TissueA:**Treatmentstressed")
>
> finds genes DE between stress and control in tissue A,
>
>  lrtB <- glmLRT(d, fit, coef="TissueB:**Treatmentstressed")
>
> finds genes DE between stress and control in tissue B.  And so on.
>
> You will find it useful to examine colnames(design).
>
> Best wishes
> Gordon
>
>  Date: Tue, 31 Jan 2012 11:24:49 -0600
>> From: Tilahun Abebe <tilahun.abebe@uni.edu>
>> To: bioconductor@r-project.org
>> Subject: Re: [BioC] Bioconductor Digest, Vol 107, Issue 31
>>
>> Dear Gordon,
>>
>> Thank you for the tip. I think I am making some progress. I should have
>> phrased my last question better. I meant what code do I use to get genes
>> differentially expressed in one tissue under control and stress
>> conditions.
>>
>> I have one more question. Is it possible to specify the random effect in
>> the design matrix in edgeR? I am thinking of something similar to the
>> RANDOM statement in SAS that will allow me to treat "block" as a random
>> effect?
>>
>> Cheers.
>>
>> Tilahun
>> ----------------------------
>>
>>  Date: Tue, 31 Jan 2012 11:18:59 +1100 (AUS Eastern Daylight Time)
>>> From: Gordon K Smyth <smyth@wehi.EDU.AU>
>>> To: Tilahun Abebe <tilahun.abebe@uni.edu>
>>> Cc: Bioconductor mailing list <bioconductor@r-project.org>
>>> Subject: [BioC] Please help! How to specify factors for a RCBD in
>>>       edgeR
>>>
>>> Dear Tilahun,
>>>
>>> The first step is that you need to create a data frame containing the
>>> experimental factors, just as you would for a SAS analysis.  So you need
>>> to create three factors:
>>>
>>>  Tissue
>>>  Treatment
>>>  Block
>>>
>>> each containing 24 values, one for each RNA sample.  Then the design
>>> marix
>>> is formed by:
>>>
>>>  design <- model.matrix(~Tissue***Treatment+Block)
>>>
>>> Type colnames(design) to see how the coefficients are defined.  You will
>>> see that the interaction coefficients are coefficients 6 to 8.
>>>
>>> After fitting your linear model, you could find genes that show
>>> significant Tissue*Treatment interaction (on 3df) by
>>>
>>>  lrt <- glmLRT(d, fit, coef=6:8)
>>>
>>> and so on.
>>>
>>> I don't understand your question "How do I obtain differentially
>>> expressed
>>> genes in each Tissue*Stress combination?", so I can't give specific
>>> advice
>>> on that.  Differentially expressed compared to what?
>>>
>>> Best wishes
>>> Gordon
>>>
>>> ---------------- original message -------------------
>>> [BioC] Please help! How to specify factors for a RCBD in edgeR
>>> Tilahun Abebe tilahun.abebe at uni.edu
>>> Wed Jan 25 22:16:41 CET 2012
>>>
>>> Hi,
>>>
>>> I am learning how to use edgeR to analyze RNA-seq data generated from
>>> Illumina GAII. The experimental design is fairly complex.
>>>
>>> I have a mixed 4x2 factorial randomized complete block design (RCBD)
>>> consisting of:
>>>
>>> 4 tissues: A, B, C, D
>>> 2 treatments: control, stressed
>>> 3 blocks: Block1, Block2, Block3
>>>
>>> Tissue and treatment are fixed effects and block is a random effect.
>>>
>>> Here is the code I tried to use in edgeR:
>>>
>>>  counts <- read.delim( file = "Mycounts.txt", header = TRUE)
>>>> rownames <-counts [ , 1 ]
>>>> d <- counts [, 2:25]    # counts are in columns 2-25
>>>> d
>>>> group <- c(rep("Control", 3), rep("Stress", 3), rep("Control", 3),
>>>>
>>> rep("Stress", 3), rep("Control", 3), rep("Stress", 3), rep("Control", 3),
>>> rep("Stress", 3))
>>>
>>>> d <- DGEList(counts = d, group = group)
>>>> design <- model.matrix(~group)
>>>> d <- calcNormFactors(d)
>>>> d$samples
>>>> d <- estimateGLMCommonDisp(d, design)
>>>> d <- estimateGLMTagwiseDisp(d, design)
>>>> d$common.dispersion
>>>> fit <- glmFit(d, design)
>>>> lrt <- glmLRT(d, fit, coef=2)
>>>> topTags(lrt, n=4)
>>>>
>>>
>>> I am interested to know genes differentially expressed in each of the
>>> four
>>> tissues under stress. However, I feel like I am not specifying the
>>> factors
>>> correctly in the design statement. My questions are:
>>>
>>> 1) How do I specify the fixed effects Tissue, Stress, and Tissue*Stress
>>> interaction in the model?
>>> 2) How do I tell edgeR to use block as a random effect?
>>> 3) How do I obtain differentially expressed genes in each Tissue*Stress
>>> combination?
>>>
>>> I appreciate your help.
>>>
>>> Cheers.
>>>
>>> Tilahun Abebe, Ph.D.
>>> University of northern Iowa
>>> Cedar Falls, IA
>>>
>>
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