Hi Gilgi,
All you have to do is remove "batch" from the formula in your call to
"model.matrix", and using the resulting matrix in your call to
removeBatchEffect. Note, however, that for differential expression
testing, you want to use the design matrix that you're already using
and
avoid removeBatchEffect.
For combining batch effect removal with the variance stabilizing
transformation, I believe you would first apply the VST to get
normalized, variance-stabilized logCPM, and then use removeBatchEffect
on the result.
-Ryan
P.S. Please use "Reply All" when you reply to posts in order to ensure
that the whole mailing list sees your reply.
On 08/28/2013 11:08 PM, Gilgi Friedlander wrote:
> Hi Ryan,
>
> Thanks a lot for your reply.
>
> The reason I used estimate dispersions is that I wanted to do also
differential expression analysis.
>
> Regarding the removeBatchEffect - I am not sure I understand how to
define the design and then how to define the batch. Do you happen to
have an example how to use it?
>
> Thanks for the variance-stabilizing transformation tip. Would it be
correct to pass it as the matrix given to removeBatchEffect?
>
> Thanks a lot,
> Gilgi
>
> -----Original Message-----
> From: Ryan C. Thompson [mailto:rct at thompsonclan.org]
> Sent: Wednesday, August 28, 2013 11:02 AM
> To: Gilgi Friedlander
> Cc: bioconductor
> Subject: Re: [BioC] EdgeR: getting CPM values after batch effect
correction
>
> Hi Gilgi,
>
> If you're just using edgeR for the CPM calculation, I believe
there's no need to estimate dispersions; they won't be used in
calculating the CPM values. You may wish to use
normalized.lib.sizes=TRUE in the call to cpm to get CPM values that
take into account the normalization factors computed by
calcNormFactors (otherwise there's no point in doing that either).
>
> Second, according to the help text for removeBatchEffect, you're not
supposed to include the batch effect in the design matrix. The design
matrix should include all the experimental variables, while the batch
variable should indicate the technical batching.
>
> Finally, instead of doing a simple logCPM transform, you might also
try the variance-stabilizing transformation provided by the DESeq
package, which is intended for clustering and machine learning types
of analyses.
>
> -Ryan
>
> On 08/27/2013 10:52 PM, Gilgi Friedlander wrote:
>> Hi Ryan,
>>
>> Thanks a lot for the reply.
>>
>> I followed EdgeR user's manual, and defined a model:
>> y1<-DGEList(counts=countdata1,group=batch)
>> y1<- calcNormFactors( y1 )
>> design1 <- model.matrix(~batch+Treat1)
>>
>> batch has values 1 or 2, according to the batch of the experiment
that was done.
>>
>> Treat has 10 different samples.
>>
>> In order to define a contrast I did the following:
>> y1 <- estimateGLMCommonDisp(y1, design1, verbose=TRUE)#Now we are
>> ready to construct an edgeR specific
>> y1 <- estimateGLMTrendedDisp(y1, design1)
>> y1 <- estimateGLMTagwiseDisp(y1, design1) lrt <-
>> glmLRT(fit,contrast=c(0,0,1,-1,0,0,0,0,0,0,0,0,0,0))
>>
>> I want now to get also the log counts after removal of the batch
effect (for the purpose of clustering of the genes).
>>
>> Is it correct to obtain the batch removed log counts in the
following way:
>>
>> logCPM <- cpm(y1, log=TRUE, prior.count=3)
>>
logCPM_batchRemoved<-removeBatchEffect(logCPM,batch=batch,design=desig
>> n1)
>>
>> Many thanks,
>> Gilgi
>>
>> -----Original Message-----
>> From: Ryan [mailto:rct at thompsonclan.org]
>> Sent: Wednesday, August 28, 2013 2:46 AM
>> To: Gilgi Friedlander
>> Cc: bioconductor at r-project.org
>> Subject: Re: [BioC] EdgeR: getting CPM values after batch effect
>> correction
>>
>> You would have to define batch correction. Are you talking about
fitting a model of the form "~ experimetalVar + batchEffect" and then
subtracting out the batch effect coefficient?
>>
>> On Sun Aug 25 11:11:55 2013, Gilgi Friedlander wrote:
>>> Dear list,
>>>
>>> In edgeR, it possible to get CPM values after batch effect
correction (and after TMM normalization)?
>>>
>>> Thanks a lot!
>>>
>>> [[alternative HTML version deleted]]
>>>
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