On Fri, Sep 11, 2009 at 11:20 AM, Sean Davis wrote: > > > On Fri, Sep 11, 2009 at 9:47 AM, Tefina Paloma wrote: > >> To be able to fit the same model to all arrays, an additional >> between-array >> normalization would be necessary, so to make all the arrays really >> comparable >> and I don't want to over-normalize the data either..... >> >> therefore I just thought of an sensible p value adjustment >> >> > You can adjust the entire list of p-values from all lists, if you like, as > an alternative. However, assuming that the arrays are of the same > technology, the probe-level variances should be similar, so you could also > combine the normalized data. I'm not sure what "model" you mean, as each > test is done within a probe and, therefore, would not cross arrays. But I > may have misunderstood what you are trying to do. > > I made a further assumption above, which I should probably make explicit. While the array technology is important in determing the variance, the biologic behavior of the probes on the array contributes, also. If the biologic behavior of probes on one array is expected to be "different" in some way, then the assumption of approximately equal variance will be violated. Then I agree that doing an analysis "within array" is the best way to go. Sean > 2009/9/11 Sean Davis >> >> > >> > >> > On Fri, Sep 11, 2009 at 8:58 AM, Tefina Paloma > >wrote: >> > >> >> Dear all >> >> >> >> unfortunately I did not get any reply on my post, so thats why I am >> asking >> >> again, >> >> assuming that lots of people already came across that problem. >> >> >> >> Working with an array set ( cDNA or any single color platform) just >> means >> >> that the probes you are interested in, are spread out over more than >> one >> >> array >> >> (usually due to space limitations), >> >> So sample samples, but different features. >> >> >> >> But actually that kind of separation of the probes is rather random. >> >> The question arises at which level of the analysis the arrays should be >> >> aggregated. >> >> >> >> I think the normalization and also the model fitting should be done >> >> separately. >> >> >> >> But as we do not only consider contrasts within each array of the array >> >> set, >> >> but at the contrast, >> >> we want to look at the results of all arrays at the same time, the >> >> p-values >> >> must be adjusted somehow for >> >> this array-effect. >> >> >> >> To do this in a "global" manner similar to the "global method" of >> >> decide.tests will probably result in being overly >> >> conservative. >> >> >> >> Any suggestions? >> >> >> >> >> > Why not just normalize each array in the set separately and then combine >> > the normalized data for analysis? I'm not sure I see why the arrays >> would >> > need to be treated independently for analysis, assuming the technology >> was >> > the same for each array in the set. >> > >> > Sean >> > >> > >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > [[alternative HTML version deleted]]