I am looking to identify differentially expressed genes from five different cell types with five, five, four, four, and three affymetrix replicates each. (I will be using gcrma as preprocessing step) I seem to remember a paper that showed that T-tests did not predict known spiked-in changes better than simple fold change until one had 8 or more replicates. Is there data with known spiked in datasets (or a consensus) that limma or sam/siggenes performs better than straight fold change with such low replicate analysis? Sincerely, John Luckey

Try the Local Pooled error (LPE) test on Bioconductor. Reference: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformati cs/research/LPE.pdf -Nitin On Sat, 22 May 2004 14:17:19 -0400 "Luckey, John" <john.luckey@joslin.harvard.edu> wrote: > >I am looking to identify differentially expressed genes >from five different cell types with five, five, four, >four, and three affymetrix replicates each. (I will be >using gcrma as preprocessing step) > >I seem to remember a paper that showed that T-tests did >not predict known spiked-in changes better than simple >fold change until one had 8 or more replicates. > >Is there data with known spiked in datasets (or a >consensus) that limma or sam/siggenes performs better >than straight fold change with such low replicate >analysis? > >Sincerely, >John Luckey > >_______________________________________________ >Bioconductor mailing list >Bioconductor@stat.math.ethz.ch >https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor >