[BioC] RE: time-course experiments (edoardo missiaglia)

[Baker, Stephen]

My apologies for quoting an entire Digest (I hate that too).  Here's my
note with only the right part quoted:

Gary Churchill at the Jackson Labs in Maine has an R program on his
website for performing mixed models ANOVA on microarray data.  The only
problem with this is it uses least squares to fit the model (which would
include a within-subjects factor for the time effect) and would requires
that there are no missing data points and all subjects being measured at
the same time points.  This is because the least squares solution
involves inverting a matrix and missing data would make it not of full
rank.

An alternative approach which wouldn't be done in R would be to use PROC
MIXED in the SAS stats package.  This uses maximum likelihood to fit
mixed models and works well.  If you really want to try to do it in R,
Yudi Pawitan at Dept. of Stats at University of Cork in Ireland has a
book and a set of R programs which would give you a leg up on it:

http://statistics.ucc.ie/staff/yudi/likelihood/index.htm



-.- -.. .---- .--. ..-.
Stephen P. Baker, MScPH, PhD (ABD)            (508) 856-2625
Sr. Biostatistician- Information Services
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


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Message: 10
Date: Wed, 8 Oct 2003 12:02:36 +0200 (CEST)
From: edoardo missiaglia <edo_missiaglia at yahoo.it>
Subject: [BioC] time-course experiments
To: bioconductor at stat.math.ethz.ch
Message-ID: <20031008100236.12628.qmail at web11701.mail.yahoo.com>
Content-Type: text/plain; charset=iso-8859-1

Dear all,

I am now working on some time-course experiments and I
have applied to them some classical statistic methods
to identify genes that change their expression between
time points. However I have read few papers (such as
Peddada et al. Gene selection and clustering for
time-course and dose-response microarray experiments
using order-restricted inference; GUO, X et al
Statistical significance analysis of longitudinal gene expression data;
etc..) where they describe specific methods for the analysis of this
type of data. Unfortunately my background (I am biologist) make
difficult to transform the algorithms reported in these papers in
something usable in R. In the same time, I could not find packages in
bioconductor that face this kind of problems ( there is only GeneTS
written by Korbinian Strimmer, that is useful in a cyclic time-course
experiment). I was wondering if anybody has already developed a package
or some functions usable in R specifically designed for time-course
experiment that consider the particular structure of this data.
Otherwise is there anybody interest in developing something from
scratch? Thank you very much in advance for your help.

Best wishes,

edoardo