[BioC] ANOVA, SAM and Limma

[Baker, Stephen]

Naomi,
I'm a little confused by your posting. Let me quote parts of your email
and then ask for clarification:

 
>...  I did not replicate my simulations,...
 
            Does this mean you had only one one simulation?


>1. Gene-by-gene ANOVA is not as good as limma and SAM.

            What is meant by "good"?

            I thought what you did in limma was gene-by-gene ANOVA? 
 

>3. 2 replicates does not give you a whole lot of power, even when you 
>"borrow strength" by using all the genes. Most of the differentially 
>expressing genes were not "discovered".

            Is this meant for all methods?

>SAM's q-value estimate is more conservative, 
>but both are somewhat conservative.  Most of the differences in results

>appear to be differences in the estimated q-values, which were computed

>from the p-values in limma and directly from the permutations in SAM. 

          Aren't q-values a form of FDR and hence a function of the
prevalence of true results?

          Aren't the p-values from limma from ANOVA which are "uniformly
most powerful" if assumptions hold? Since q-values are based on p-values
your result would be consistent with theory.


One thing I find confusing is when a program/package name is cited
instead of the specific statistical method applied. This may seem a
minor point but it is insufficient when programs or packages have
multiple options that could be used to do the same analysis. 

-.- -.. .---- .--. ..-.
Stephen P. Baker, MScPH, PhD (ABD)         (508) 856-2625
Sr. Biostatistician- IS Bioinformatics Unit
Lecturer in Biostatistics                  (775) 254-4885 fax
Graduate School of Biomedical Sciences
University of Massachusetts Medical School, Worcester
55 Lake Avenue North stephen.baker at umassmed.edu
Worcester, MA 01655 USA