[BioC] removeBatchEffect options: design and covariates

Rao,Xiayu XRao at mdanderson.org
Wed Aug 6 18:55:55 CEST 2014

Thanks a lot! I think you are right. I will do that. But what if the Batch itself was treated as a random effect in the model ??   Can I put it in batch=Batch in the removeBatchEffect command? Sorry but I promise this is the last question.

Thank you so much!


-----Original Message-----
From: Ryan [mailto:rct at thompsonclan.org] 
Sent: Wednesday, August 06, 2014 11:45 AM
To: Rao,Xiayu
Cc: bioconductor at r-project.org
Subject: Re: [BioC] removeBatchEffect options: design and covariates

Well, I'm not as familiar with random effects analysis, but the normal way to use duplicateCorrelation is to pass corfit$consensus as the correlation argument to lmFit. The help page for removeBatchEffect states that any additional arguments are passed to lmFit, so I think I would simply do likewise and pass the same correlation argument to removeBatchEffect.

On Wed Aug  6 08:19:33 2014, Rao,Xiayu wrote:
> Hi, Ryan
> Thank you for your input! One more quick follow-up question, considering your example of specifying design=model.matrix(~Condition), and batch=Batch, what if I also have a random effect in my limma design? do I need to put that variable(subject as below) anywhere in the removeBatchEffect command or just ignore it?
> design <- model.matrix(~Condition + Batch)
> duplicateCorrelation(y,design,block=targets$subject)
> Thanks,
> Xiayu
> -----Original Message-----
> From: Ryan C. Thompson [mailto:rct at thompsonclan.org]
> Sent: Tuesday, August 05, 2014 5:18 PM
> To: Rao,Xiayu
> Cc: bioconductor at r-project.org
> Subject: Re: [BioC] removeBatchEffect options: design and covariates
> Hello,
> When calling removeBatchEffect, you should use the same design that you used for limma, but with with batch effect term removed from the design. Then you would pass the batch effect factor as the batch argument instead. So, if the design matrix that you used for limma was constructed as:
> model.matrix(~Condition + Batch),
> then for removeBatchEffect, you would use design=model.matrix(~Condition), and batch=Batch. In other words, you take the batch effect out of your model design and pass it as the batch argument instead.
> -Ryan
> On Tue 05 Aug 2014 03:12:26 PM PDT, Rao,Xiayu wrote:
>> Hello,
>> I want to use removeBatchEffect() on the expression data (Elist) prior to drawing a heatmap based on the expression of sig diff genes. Those sig diff genes were generated from limma linear modelling, with the batch factor already included in the linear model.
>> I saw people use removeBatchEffect(y, batch=batch) and removeBatchEffect(y, batch=batch, design=design). I  would very much like to know in what condition I should include the design matrix, and when to also include covariates ???  Any comments would be very appreciated.  Thank you in advance!
>> removeBatchEffect(x, batch=NULL, covariates=NULL, 
>> design=matrix(1,ncol(x),1), ...)
>> Thanks,
>> Xiayu
>> 	[[alternative HTML version deleted]]
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