Hi James,
   
  I am not 100% sure, but you coul probably use nestedF option in LIMMA. That is, you could create two sets of contrasts corresponding to (i) treatment and (ii) time. nestedF would select for you overall significant genes, according to F-stattistics across all contrasts, and then would also classify the selected genes as significant or not for each contrast. As far as I understand it, nestedF uses iterative procedure declairing a contrast with the highest t-statistics as the most sinificant, then setting t-statistics for the highest contrast equal to the one of the second highest contrast and checking whether the overall F-statistics is still significant. It stops when the overall F-statistics becomes insignificant, but in the process you would get all your contrasts ranked by their relative contributions into the overall gene variablity. You could probably use the same procedure to rank your contrasts and decide which of them are more significant for the selected
 differentially expressed genes. I guess that Gordon Smyth could comment on whether it is a good idea or not really.
   
  With kind regards,
  Lev.
   
   
  James Anderson <janderson_net@yahoo.com> 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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