Peter,

I considered re-titling this post however we can leave it as is.


On Mon, May 12, 2014 at 10:48 PM, Peter Langfelder <
peter.langfelder@gmail.com> wrote:

> Hi Michael,
>
> On Mon, May 12, 2014 at 1:23 PM, Michael Breen
> <breenbioinformatics@gmail.com> wrote:
>
> > 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.
>
> This is straightforward.
>
> >
> > 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?
>
> Well, we should first define your analysis goal more precisely. What
> so you have in mind when you say "adjust gene expression for changes
> in cell type composition"?
>
> If you want cell-type specific expression, here's how the authors of
> the paper I referred to earlier do it (and I agree): Gene expression
> of a particular gene is presumably different in each cell type. So the
> total expression of each gene is a sum of cell-type specific
> expressions multiplied by the cell type abundances. This sort of looks
> like a linear model, except that the coefficients multiplying the cell
> type abundances are not constant - the cell type specific gene
> expressions presumably also change between time points and it is these
> changes people are usually after (this is why we need to define your
> analysis goal more precisely).
>
> To isolate the cell-type specific expression changes between time
> points, you would then have to write a separate linear model at each
> time point, figure out the cell-type specific expression as the
> regression coefficient at each time point, then compare them (i.e.,
> the regression coefficients). This of course assumes that at each time
> point you have multiple samples, preferably many more than the number
> of cell types you suspect you have in your sample, and the cell-type
> specific expression of each gene within each cell type at each time
> point can be considered constant.
>
>
I am familiar with this paper and too agree with the methodology of writing
seperate linear modles at each time point and then comparing the regression
coefficents. As it stands, this project has 4 time points (*baseline, pre,
post, 1 hour post*) and at each time point the same 6 subjects. In this
study we have profiled the luekocytes and given the type of experiment, it
would be easy prior to investigation to assume that at our post and 1 hour
post time points there will be large increases in the frequency of NK
cell-types (which is the case after deconvoluting).  Because we are simply
after differentially expressed genes, it is easy to see most of these
resulting genes are associated with the alterations of NK-cells. Therefore,
we would like to remove this effect of NK-cell type frequency (and all
other cell-types with significant variation at the time points), as we know
that gene expression can vary substantially among cell types and the
heterogeneity of our tissue may mask the identification of biologically
important information within less abundant cell-types.

In other words, what I am after is aggregating the regression coefficents
from the various time points of our different cell-types and using these as
continuous variables to adjust for within our DE testing in order to reveal
differences that are not necessarily associated with increases of a
particular cell-type.

Does that make sense?

p.s. Like others, I have enjoyed what you have done with WGCNA.



> If you simply adjust for cell type frequencies, it is not clear to me
> how to interpret the resulting number.
>
> Peter
>


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

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