[BioC] about two way anova on time and treatment

[Kevin R. Coombes]

Hi,

I usually start this kind of analysis, as you did, with per-gene two-way 
ANOVAs. I then compute p-values for the significance of the complete 
ANOVA (in effect comparing the full model with the null model), and use 
some kind of FDR estimate to set a cutoff on the p-values for 
statistical significance.

With this approach, I only decide on the relative importance of th 
factors for the genes that have already been identified as "interesting" 
in the sense that the model explains some part of the expression data. 
For the interpretation step, you have a couple of choices.  One idea is 
to use Tukey's test for honestly significant difference (again per-gene, 
but without any other additional correction for multiple testing) to see 
which groups differs in mean expression.

One of the things we've tried recently (see Nakamura et al, Clin Cancer 
Res, 2007) is to cluster the significant genes and compute average 
expression profiles for each cluster to see which of the effects are 
driving the expression.

Best,
	Kevin

James Anderson wrote:
> Hi, 
> 
> I have affy data for time and treatment, I did a two way anova for each gene. What's the correct way to evaluate whether time effect is more imporant or treatment effect is more important? I think averaging the variance component of each gene is not a very good way, since not all genes are equally important, in addition, most of the genes are noisy genes. I am thinking of doing a PCA and take the first several components, instead of doing anova on each gene, I can do two way anova on the scores of the first several component, then I can use a weighted average (weight is determined by the amount of variance each PC captured), is this a good way? Can somebody give me some suggestions on this? Thanks. 
> 
> James
> 
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