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 ______________________________________________________________________ http://it.yahoo.com/mail_it/foot/?http://it.mail.yahoo.com/

> 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 believe you are correct that nothing directly confronts time-course experiments. But many existing tools can be used for such data. 1) Data structure. the exprSet class can be used, regarding time elapsed as a component of the phenotype data. the values of the time element induce an order across all samples. 2) Example. The Iyer517 data package is an example based on a celebrated paper on response of human fibroblasts to exposure to serum. 3) Methods. a) Clustering. The repeated observations on each gene can be regarded as multivariate outcomes to be grouped by one's favorite clustering method. b) visualization. MASS parallel coordinate plots are useful; you can use ggobi with Iyer517 for a dynamic display. I have to run now, we can add some material to the Iyer517 vignette to get more precision on these recommendations and add additional ones. PS -- get the order-restricted folks to contribute their software! > 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 > > > ______________________________________________________________________ > > http://it.yahoo.com/mail_it/foot/?http://it.mail.yahoo.com/ > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor >