Hi Xiao; I think maybe you want to identify differentially expressed genes under treatment (a,b,ab) compared to untreated group, across all time points. Say X=log(R/G) (a vector of 6 as the log ratio of red and green intensities across 6 common time.) H0: X=0 vs Ha: X!=0 If you don't have replications, ANOVA is not possible if you treat time as a factor. There are some publications on this topic, just search pubmed with key words "time course" Fangxin > Hi Xiao, > > It seems in a design such as this with Treatment and Time, there are a lot > of > ways to define 'differential expression'. You say you're looking for > differentially expressed genes with respect to treatment(a,b,ab). So, > does > this mean you're not interested in any time effects(or any > Treatment-by-Time > interactions)? Maybe you're looking for genes where the three expression > profiles over time are parallel for the different treatments, but one of > these > profiles is 'far away' from the other two? > > Rhonda > > > >> hi,everybody >> I have a *time course* data set about a CML cell line treated by two >> drugs >> and their combination.The experiment was performed on cDNA microarray >> platform.The green channel of all the arrays are common,the untreated >> cell.Here follows the experiment design: >> >> a_0hr,a_3hr,a_8hr,a_12hr,a_24hr,a_48hr,a_72hr, >> b_3hr,b_8hr,b_12hr,b_24hr,b_48hr,b_72hr, >> ab_3hr,ab_8hr,ab_12hr,ab_24hr,ab_48hr,ab_72hr. >> >> A total of 19 *cDNA microarrays*.a_0hr means* *drug *a *treament *0 >> hours >> vs. control. *And a_3hrs means drug a treatment 3 hours vs. control.So >> for >> drug *b *and their combination *ab*(drug a and drug b added together).My >> goal is to identify the three sets of genes,the genes differentially >> expressed by drug a,the genes by drug b ,and their combination. >> i am thinking about *ANOVA* ,but i am not sure whether it is correct. >> Any comments,suggestions?Any R/bioconductor packages can be used?Thanks >> in >> advance >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > > -- Fangxin Hong, Ph.D. Plant Biology Laboratory The Salk Institute 10010 N. Torrey Pines Rd. La Jolla, CA 92037 E-mail: fhong@salk.edu