[BioC] Re: Manova nuances

Stephen P. Baker stephen.baker at umassmed.edu
Fri Nov 21 13:08:43 MET 2003

Principle component analyses should reduce your data array to as many
independent components as you have samples, and  for each sample get a score
for each dimension.  These will have the same total information as the
original data.  These can then be analysed separately with univariate anova
but since these are "orthogonal" analyses, multiple comparisons adjustments
would not be needed.
-.- -.. .---- .--. ..-.
Stephen P. Baker, MScPH , PhD(ABD)                      (508) 856-2625
Senior Biostatistician
(775) 254-4885 fax
Academic Computing Services
Lecturer in Biostatistics , Graduate School of Biomedical Sciences
University of Massachusetts Medical School
55 Lake Avenue North                          stephen.baker at umassmed.edu
Worcester, MA 01655  USA
Date: Fri, 21 Nov 2003 00:18:54 -0500
From: "Michael Benjamin" <msb1129 at bellsouth.net>
Subject: [BioC] Manova nuances
To: <bioconductor at stat.math.ethz.ch>
Message-ID: <003401c3afee$f7eff000$7a05fea9 at amd>
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Anybody here using manova?  It's powerful and pretty fast, but I'm
finding that you can't have more variables than samples (limits its
applicability to microarray research).  Is there any way around this?


1200 35

fit<-manova(transeset ~ categories)

There is probably a complicated mathematical truth that underlies this
limitation--if anybody can shed some light, that would be great.

Also, if anyone knows of a quick, free multivariate tool that summarizes
all the tests into a single test statistic, that would be much

Michael Benjamin, MD
Emory University
Winship Cancer Institute

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