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@umassmed.edu Worcester, MA 01655 USA ------------------------------ Message: 10 Date: Wed, 8 Oct 2003 12:02:36 +0200 (CEST) From: edoardo missiaglia <edo_missiaglia@yahoo.it> Subject: [BioC] time-course experiments To: bioconductor@stat.math.ethz.ch Message-ID: <20031008100236.12628.qmail@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