In our lab, we are using Affy ATH1 chip to study Arabidopsis circadian pattern (time course data). What we found out is, for genes with low intensities, the normalized profile from RMA and GCRMA differ quite a lot. The peak time and pattern of change are so different that you won't believe that two profiles are actually from the same gene. Thus it is no way to draw a conclusion about this gene. However, is there any good way to delete the genes with low intensities beside MAS5.0 call? Bests; Fangxin > I know very little biology but my biologist collaborators are usually > more interested in low signal genes, so you might want to think > carefully before deleting the genes with low correlation. > > Furthermore, if you compared expressions from RMA (or GCRMA) with MAS > 5.0, I believe you might find similar results. i.e. Good correlation > among high signal genes but poor correlation for low signal genes. > > You results might be simply saying that the RMA and GCRMA expression > measures are very similar for high signal genes but they differ for low > signal genes. > > Regards, Adai > > > > On Fri, 2005-03-04 at 13:32 -0800, Fangxin Hong wrote: >> Thank you. Actually I just found this out from one of my tests, genes >> with >> low correlation are all in the low intensity end. I am thinking actually >> this give me clue to delecte those non-expressed genes from further >> study. >> >> This is a hrad evidence that we should filter genes first. >> >> Thanks. >> Fangxin >> >> >> >> > Hi Fangxin, >> > >> > do you expect that 100% of the genes that are assayed by your chips >> are >> > expressed all the time in the system you are investigating? (you never >> > told us which chips and which plant or animal) >> > >> > And if not - say if only 50% of genes are expressed, then the data for >> > the remaining 50% should just be pure noise and there is no reason why >> > intensities from RMA and GCRMA should be correlated. >> > >> > I think you have just learned something about your measurement >> > instrument (and this has little to do with normalization methods). >> > >> > Best wishes >> > Wolfgang >> > >> > Fangxin Hong wrote: >> >> Hi list; >> >> I met a strange problem regarding the normalization methods, >> >> >> >> For an experiment with 24 arrays (time order), I normalized the data >> by >> >> both RMA and GCRMA. Then I tested the correlation between the >> normalized >> >> data for each gene. Surprisingly, I found that about 25% genes with >> >> correlation less than 0.7 between value normalized by RMA and GCRMA, >> and >> >> only less than 50% genes have correlation >0.9. I studies the profile >> of >> >> some genes, they look quite different under two methods. >> >> >> >> >> >> Anybody met this problem before? Which method we should trust? Any >> >> comments/idea is appreciated. Or is it possible that I did something >> >> wrong, I couldn't find it myself. >> > >> > >> > ------------------------------------- >> > Wolfgang Huber >> > European Bioinformatics Institute >> > European Molecular Biology Laboratory >> > Cambridge CB10 1SD >> > England >> > Phone: +44 1223 494642 >> > Fax: +44 1223 494486 >> > Http: www.ebi.ac.uk/huber >> > ------------------------------------- >> > >> > >> >> > > > -- Fangxin Hong, Ph.D. Plant Biology Laboratory The Salk Institute 10010 N. Torrey Pines Rd. La Jolla, CA 92037 E-mail: fhong@salk.edu

Please help: We have done a timecourse experiments (12 time points) using Affy ATH1 array (Arabidopsis) without replication. The normalized expression profiles from RMA and GCRMA don't agree well for some genes, especially for genes with low intensity values. This might means that those genes don't express or express at low level. then I tried MAS5.0 P/A/M call, and delete genes with more than 30% "A" calls across time points. However, some of the remaining genes still have different profiles from RMA and GCRMA ( I use correlation between normalized profiles by RMA and GCRMA as measurement). Since I want to draw conclusion based on expression pattern for each gene, it seems that different normalization methods would change my conclusion, although the truth is only one. Should I trust GCRMA better? Or the disagreement between RAM and GCRMA means that this is no clear pattern for that gene at all, thus only genes for which RMA and GCRMA agree well should be identified. Any suggestion is appreciated. Fangxin > I know very little biology but my biologist collaborators are usually > more interested in low signal genes, so you might want to think > carefully before deleting the genes with low correlation. > > Furthermore, if you compared expressions from RMA (or GCRMA) with MAS > 5.0, I believe you might find similar results. i.e. Good correlation > among high signal genes but poor correlation for low signal genes. > > You results might be simply saying that the RMA and GCRMA expression > measures are very similar for high signal genes but they differ for low > signal genes. > > Regards, Adai > > > > On Fri, 2005-03-04 at 13:32 -0800, Fangxin Hong wrote: >> Thank you. Actually I just found this out from one of my tests, genes >> with >> low correlation are all in the low intensity end. I am thinking actually >> this give me clue to delecte those non-expressed genes from further >> study. >> >> This is a hard evidence that we should filter genes first. >> >> Thanks. >> Fangxin >> >> >> >> > Hi Fangxin, >> > >> > do you expect that 100% of the genes that are assayed by your chips >> are >> > expressed all the time in the system you are investigating? (you never >> > told us which chips and which plant or animal) >> > >> > And if not - say if only 50% of genes are expressed, then the data for >> > the remaining 50% should just be pure noise and there is no reason why >> > intensities from RMA and GCRMA should be correlated. >> > >> > I think you have just learned something about your measurement >> > instrument (and this has little to do with normalization methods). >> > >> > Best wishes >> > Wolfgang >> > >> > Fangxin Hong wrote: >> >> Hi list; >> >> I met a strange problem regarding the normalization methods, >> >> >> >> For an experiment with 24 arrays (time order), I normalized the data >> by >> >> both RMA and GCRMA. Then I tested the correlation between the >> normalized >> >> data for each gene. Surprisingly, I found that about 25% genes with >> >> correlation less than 0.7 between value normalized by RMA and GCRMA, >> and >> >> only less than 50% genes have correlation >0.9. I studies the profile >> of >> >> some genes, they look quite different under two methods. >> >> >> >> >> >> Anybody met this problem before? Which method we should trust? Any >> >> comments/idea is appreciated. Or is it possible that I did something >> >> wrong, I couldn't find it myself. >> > >> > >> > ------------------------------------- >> > Wolfgang Huber >> > European Bioinformatics Institute >> > European Molecular Biology Laboratory >> > Cambridge CB10 1SD >> > England >> > Phone: +44 1223 494642 >> > Fax: +44 1223 494486 >> > Http: www.ebi.ac.uk/huber >> > ------------------------------------- >> > >> > >> >> > > > -- Fangxin Hong, Ph.D. Plant Biology Laboratory The Salk Institute 10010 N. Torrey Pines Rd. La Jolla, CA 92037 E-mail: fhong@salk.edu