[BioC] removeBatchEffect options: design and covariates

[Rao,Xiayu]

Hi, Ryan

That's very clear. Thank you very much for your instructions. Then when should I include covariates, or what kind of covariates I should adjusted for ??  Covariates in the model should already be in the design matrix, right?

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
>
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>
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