[R-sig-ME] need help with mixed effects model
bates at stat.wisc.edu
Sun Feb 24 20:45:42 CET 2008
On Fri, Feb 22, 2008 at 11:57 AM, Mark W Kimpel <mwkimpel at gmail.com> wrote:
> This is my first foray into in mixed models and, while awaiting the
> arrival of:
> Extending the Linear Model with R: Generalized Linear, Mixed Effects
> and Nonparametric Regression Models
> Mixed Effects Models in S and S-Plus
> I am in need to some advice.
> I would like to look at gene-gene correlations within a multi-factorial,
> mixed effects experiment. Here are the factors, with levels:
> Gene Expression: 2 different genes per Animal, continuous variable
> Animals: 6 per Strain
> Tissues: 3 per animal
> Strain: 2
> I thus have 6*3*2 = 36 samples
> I do not care, for this analysis, about differences between Tissues,
> Strains, or Animals, in fact, I want to control for them while examining
> the correlation of expression of the two genes. In other words, I want
> look at something very much like the Pearson correlation coefficient
> controlled for these other factors.
> I guess the first question I should ask is: "is a mixed model the way to
> go, and, if not, what would be the correct approach?"
Perhaps. How do you plan to incorporate the two genes?
> Assuming mixed models will work, as I see it through my newbie eyes,
> Tissue and strain are fixed effects and animals are random effects.
If you were interested in just 1 gene than I would say that this looks
like a good approach. I'm just not sure what to do about the multiple
> Any suggestions for an approach and a model?
The model specification (assuming that each animal has a distinct
number) would be something like
gene1 ~ Tissue * Strain + (1|Animal)
In your earlier message to the Bioconductor list you had a
specification that looked like
gene1 ~ gene2 + ...
which makes me a little queasy because you are assuming that gene2 is
"known" relative to the variability in gene1 and most of the time that
is not a reasonable approach.
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