Peter,

Thanks for the clarification of Combat continous variables vs. factors.
That is the information I was missing.

As far as deconvoultion analysis, we currenlty are using Zhong et al.
(2013) DSA method using HaemAtlas to provide a signature matrix (or
cell-type marker list). The method estimates cell proportions from mixed
sample expression data, given a set of markers (HaemAtlas), i.e. features
that are known to be exclusively expressed by a single cell type in the
mixture. Although, these analyses are completely dependent upon your marker
lists.

Now, that I have some reasonable cell-type frequencies I would like to
explore the potential of  either:

A) correcting these cell-type frequences, as within Combat
B) using these frequences as continuous variables in a linear model.

I don't find option B) anymore involved than that. Can you elaborate?

Michael




On Mon, May 12, 2014 at 6:42 PM, Peter Langfelder <
peter.langfelder@gmail.com> wrote:

> Since the true experts are silent, here are my 2 cents...
>
> On Fri, May 9, 2014 at 2:14 PM, Michael Breen
> <breenbioinformatics@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
>



-- 
M.S. Breen
PhD, Bioinformatics and Genomics
Clinical and Experimental Sciences
Univ. of Southampton

	[[alternative HTML version deleted]]

