[BioC] sample size for microarray experiments having 2 factors with one random effect

[Naomi Altman]

Dear Shirley,
This is called a split plot design.  Status is the whole plot 
factor.  Tissue is the subplot factor.

Sample size computations are harder.  You will need software for 
split plot designs.

--Naomi


At 11:03 PM 4/23/2009, shirley zhang wrote:
>Dear Dr. Altman,
>
>Thanks for your quick response. Is the method you suggested similar to
>what Dr. KEVIN DOBBIN and RICHARD SIMON proposed in Biostatistics
>2005? (http://biostatistics.oxfordjournals.org/cgi/reprint/6/1/27)
>
>Sorry that I did not make my experiment design clear.
>
>There are 2 fixed effects (tissue and status). We got two different
>tissues from the same patient. Patients are grouped into two category
>based on their status. Here we are interested in finding genes
>commonly changed by status across different tissue types.  We are
>suggested to use lme function in nlme package by treating tissue and
>status as fixed effects, and patient as random effect.  Is my
>experiment still a randomized complete block design?
>
>Thanks again for your help,
>Shirley
>
>On Thu, Apr 23, 2009 at 10:40 PM, Naomi Altman <naomi at stat.psu.edu> wrote:
> > 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
> >
> >
>
>
>
>--
>Xiaoling
>
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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