Hi: Does anyone has input on compatibility of "replicate-only" vs. "all- sample" quantile normalizations? I'd assume that "true significant" genes would be picked up by either "replicate-only" or "all-sample" method, though the latter is surely more conservative (by forcing the same distribution across all samples, replicates or not). My analysis though seem to select two distinct lists of genes by two methods. e.g. If I pick top few hundreds from both lists, there'd be little overlap. Would it because my initial pool of genes are large (10,000 or so), or inherently these two methods are two assumptions, and not to be compared? thanks in advance, Hillary [[alternative HTML version deleted]]

H. Han wrote: > Hi: > > Does anyone has input on compatibility of "replicate-only" vs. "all- sample" quantile normalizations? I'd assume that "true significant" genes would be picked up by either "replicate-only" or "all-sample" method, though the latter is surely more conservative (by forcing the same distribution across all samples, replicates or not). My analysis though seem to select two distinct lists of genes by two methods. e.g. If I pick top few hundreds from both lists, there'd be little overlap. Would it because my initial pool of genes are large (10,000 or so), or inherently these two methods are two assumptions, and not to be compared? > The problem with a replicate only normalization is that you may not be accounting for all of the non-biological differences between samples, and this may cause some genes to appear to be differentially expressed when in fact they may not be. Jim > thanks in advance, > > Hillary > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor -- James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109