Hi, all, In the documentation of the Multtest R package, the raw p values were generated based on the standard Gaussian distribution. Can someone explain to me why it should be like that? What situations can it be applicable to? The p values based on the standard Gaussian distribution are much smaller than those based on the t-distributions. The consequence of this is more genes will be picked up as significant. How can this be explained? Your inputs will be very appreciated. Have a great day, Puhong Gao, PhD Dept. of Developmental Psychobiology NYSPI/Columbia University 1051 Riverside Drive New York, NY 10032 (212) 543-5710 [[alternate HTML version deleted]]

We used Gaussian p-values simply as an example. You can use the function mt.maxT to obtained permutation unadjusted p-values as well as adjusted p-values. In general, the Gaussian p-values should be adequate when you have fairly large sample sizes in both groups, so that AVERAGES in both groups are approximately normally distributed. The assumptions behind t-distribution p-values are more restrictive: it is assumed that INDIVIDUAL expression measures have a Gaussian distribution. This is not very realistic. You can find a discussion of t and z-tests in pretty much any introductory statistics textbook. I like "Statistics" by Freedman, Pisani and Purves. Best regards, Sandrine ---------------------------------------------------------------------- --------- Sandrine Dudoit, Ph.D. E-mail: sandrine@stat.berkeley.edu Assistant Professor Tel: (510) 643-1108 Division of Biostatistics Fax: (510) 643-5163 School of Public Health http://www.stat.berkeley.edu/~sandrine University of California, Berkeley 140 Earl Warren Hall, #7360 Berkeley, CA 94720-7360 ---------------------------------------------------------------------- --------- On Tue, 11 Feb 2003, Puhong Gao wrote: > Hi, all, > > In the documentation of the Multtest R package, the raw p values were generated based on the standard Gaussian distribution. Can someone explain to me why it should be like that? What situations can it be applicable to? The p values based on the standard Gaussian distribution are much smaller than those based on the t-distributions. The consequence of this is more genes will be picked up as significant. How can this be explained? Your inputs will be very appreciated. > > Have a great day, > > Puhong Gao, PhD > Dept. of Developmental Psychobiology > NYSPI/Columbia University > 1051 Riverside Drive > New York, NY 10032 > (212) 543-5710 > > [[alternate HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > http://www.stat.math.ethz.ch/mailman/listinfo/bioconductor >

Sandrine, Thanks for your quick response. That is what I want to know. It is not realistic to use the permutation method in our analysis since we only have 4 replicates in each experimental condition. And it is not appropriate to use the Gaussian approximation either. Best, Puhong