Hi All, I am working with the Affy data set for which the max fold change is around 1.7. Most of the probesets have a fold change between 1.2 and 1.5. I have done the standard data analysis procedure reccommanded by many people in Bioconductor (starting with RMA extraction, difference between 2-groups, t-test, p-val & corrected p-val etc). After doing this, I have got only 20 significant genes, which has no biological significance with the experiment. Now, I am thinking of trying other measures instead of difference between groups. What kinds of measures I should try? Is there any method addressing the issue of detecting low abundance probesets in Bioconductor? Thanks in Advance. Regards, Sharon

On 12/1/05 8:50 AM, "Sharon Anbu" <sharonanandhi at="" gmail.com=""> wrote: > Hi All, > > I am working with the Affy data set for which the max fold change is > around 1.7. Most of the probesets have a fold change between 1.2 and > 1.5. I have done the standard data analysis procedure reccommanded by > many people in Bioconductor (starting with RMA extraction, difference > between 2-groups, t-test, p-val & corrected p-val etc). After doing > this, I have got only 20 significant genes, which has no biological > significance with the experiment. How many arrays have you run? If you know that you are looking for fold changes of 1.2-1.7 (I'm not sure how you know which genes are true and showing these changes, but...), you will likely need a stable system (little biological variability within groups) or a "relatively" large n. You could do a simple power calculation to determine what your power is to detect a difference between your two groups given a "typical" variance and with different numbers of slides. If you have good power with the number of slides that you are using, then you may be seeing true negative results. If not, then increasing the number of arrays might be a good bet. All that said, if you have a set of genes that you are interested in, you could go directly from "fold-change" on the array (ignore statistics such as p-value altogether) and move directly to validation with PCR-based methods. Sean

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