[BioC] Combat Continuous

Peter Langfelder peter.langfelder at gmail.com
Mon May 12 19:42:53 CEST 2014


Since the true experts are silent, here are my 2 cents...

On Fri, May 9, 2014 at 2:14 PM, Michael Breen
<breenbioinformatics at gmail.com> wrote:
> Hi all,
>
> I have time course gene-expression from blood at 4 different time points.
> During the last two time points we see an increase in various different
> cell-type frequencies. We are interested in correcting our gene expression
> matrix with numerous continuous variables, that is estimated cell-type
> frequency. However, I realize that Combat does not correct for continuous
> batches, but rather continuous variables.
>
> In this scenario, I have no batch effects however I am interested in using
> the continuous variables (cell-type frequencies) to correct our gene
> expression matrix.

You may want to read previous work on cell-type specific expression
analysis, for example

Population-specific expression analysis (PSEA) reveals molecular
changes in diseased brain. A. Kuhn, D. Thu, H. J. Waldvogel, R. L. M.
Faull and R. Luthi-Carter. in Nature Methods, vol. 8, num. 11, p.
945-947, 2011.

Estimating gene expression within specific cell populations is more
involved than simply using a linear model in which cell type
frequencies are covariates.

>
> 1. What does the function "numCovs" implement exactly and how does it
> handle continuous variables? What is the result on the gene-expression
> matrix?

numCovs is not a function, it is an argument of the function ComBat.
It lets you specify the columns of the model matrix that are to be
treated as continuous variables rather than factors. Continuous
variables and factors are treated differently in the underlying linear
model; there is one term for each continuous variable, while there is
one term for each level of a factor except the first one.

HTH,

Peter



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