[BioC] sample size for microarray experiments having 2 factors with one random effect
Naomi Altman
naomi at stat.psu.edu
Fri Apr 24 04:40:36 CEST 2009
This appears to be a randomized complete block design. The way I
would compute the sample size is:
Use a routine that computes sample size for a randomized complete block design.
If you are planning to use log2 expression, then enter "1" as the
size of the difference you want to detect. That
corresponds to 2-fold.
Using someone else's data for the same experiment, compute the sd for
each gene (e.g. using Limma). Use the 70th or
80th percentile of SD as the SD for computing sample size. (This
will be somewhat anti-conservative, but software for RCB
sample size will not include EBayes computations which boost the
power for any sample size, which is like decreasing the SD.)
Then just enter the p-value and power that you want. Again, you
might want to consider using a smaller p-value to adjust for multiple
comparisons.
If so, you could look at the q-value versus p-value plot for the data
you used to compute SD, and pick the p-value corresponding to your
desired q-value.
The number of replicates in any experiment should be at least
3. (Those of you working in the medical field will think this is
ridiculously small, but in underfunded
areas of biology we are happy if we have funds for more than 3 reps.)
There is also software from uab.edu called PowerAtlas. I haven't
looked recently, but I think it is primarily for completely randomized designs.
--Naomi
At 10:09 PM 4/23/2009, shirley zhang wrote:
>Dear list,
>
>I have the following affymetrix microarray experiment:
>
>2 fixed effects, each factor has two levels
>1 random effect (patient)
>
>Can anybody tell me how to calculate the sample size for it?
>
>Thanks,
>Shirley
>
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Naomi S. Altman 814-865-3791 (voice)
Associate Professor
Dept. of Statistics 814-863-7114 (fax)
Penn State University 814-865-1348 (Statistics)
University Park, PA 16802-2111
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