On Tue, Jul 20, 2004 at 07:00:37PM +0100, Adaikalavan Ramasamy wrote: > a) Are you interested in the difference in cell lines over times OR > b) are you treating the different cell lines as biological replicates > > Assuming the latter, you have a oneway anova with time as a main factor > and 3 replicates at each time point. That's right. Sorry, my description was not that clear. This is what I did, an ANOVA with time as a main factor, but assuming a correlation structure among observations > > I would suggest you try RMA and GC-RMA on the whole dataset first and > truncating your list later. The truncation at step 2 ignores more than > 90% of the genes and your number of true positives will be quite low. 1) Using all the genes (or most of the genes after a bit of unspecified filtering such as on the lowest expression value across samples and on the CV) brings to such a big number of comparison that after correction none appears to be significant. Nevertheless I could use this as an exploratory approach, i.e. to rank genes. 2) Prefiltering using an "a priori" biological framework would mean (but please correct me if I'm wrong) asking a different question: among those genes related to some biological process I'm interested in, which are actually differentially expressed? Why shall I use RMA? E.g. with a very naive approach (i.e. computing F statistics without considering correlation among observations with arrayMagic = faster!) I get that mas5 values gives more higher F values (a simple qqplot can help). Also the overall analysis doesn't improve using rma. I know of affycomp but I never used it. I'll try. Thanks. Ste > You can use GO tools (I think BioConductor have some packages to handle > these) on the final gene list to see if your favourite pathway is > involved. > > > > On Tue, 2004-07-20 at 18:17, Naomi Altman wrote: > > You appear to have no replicates. Without replication you cannot do any > > statistical analysis such as ANOVA or limma. > > > > --Naomi > > > > At 06:10 PM 7/19/2004 +0000, Stefano Calza wrote: > > >Hi everybody. > > > > > >I'm looking at a small experiment with 12 chips (Affy), from 3 different > > >cell lines measured at 4 different time points (0,2 hours, 8 h, 24 h). > > > > > >1) mas5 expression values > > >2) selected about 1500 genes (out of ~22000) using GO annotations for > > >those BP of possible interest > > >3) selected genes with at least 25% Presence/Calls (I know this is quite > > >arbitrary). > > >4) ANOVA using gls with Compound Symmetry correlation structure > > >5) p value corrected either using p.adjust(...,"fdr") or computing Q values. > > > > > >I actually get few "significant" genes and mostly with low fold- change > > >(relative to time 0) and overall low expression intensities. > > >Any objection about all this and/or any suggestion for improvement? > > > > > >Thanks in advance, > > >Ste > > > > > >_______________________________________________ > > >Bioconductor mailing list > > >Bioconductor@stat.math.ethz.ch > > >https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor > > > > Naomi S. Altman 814-865-3791 (voice) > > Associate Professor > > Bioinformatics Consulting Center > > Dept. of Statistics 814-863-7114 (fax) > > Penn State University 814-865-1348 (Statistics) > > University Park, PA 16802-2111 > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@stat.math.ethz.ch > > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor -- Stefano Calza, Sezione di Statistica Medica Dip. di Scienze Biomediche e Biotecnologie Universit? degli Studi di Brescia - Italy Viale Europa, 11 25123 Brescia email: calza@med.unibs.it Telefono/Phone: +390303717532 Fax: +390303701157