Hi everybody, I have a question regarding comparing results from a t-test and limma. I compared the p values obtained from both algorithms using 100 samples in each condition. Given the large number of observations my expectation was to see high correlation between the p values. As shown below, I ran the same code in three different conditions for the mean and standard deviation. The mean and standard deviation loaded from the google docs files are from a real micro array experiment. 1. mean and std from microarray exeperiment: no correlation between p values 2. constant fold change and std from microarray exeperiment: very small correlation 3. constant fold change and uniform std from 0.01 to 0.2: high correlation I understand that limma uses information across genes, but shouldn't this information be weighed with the number of observations for each condition? I put the source code here, since on the mailing list backslashes disappear. https://drive.google.com/file/d/0B__nP63GoFhMZEFUbjNYTlFJWm8/edit?usp= sharing Thank you, Giovanni -- output of sessionInfo(): R version 2.15.2 (2012-10-26) Platform: x86_64-w64-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=English_United States.1252 [2] LC_CTYPE=English_United States.1252 [3] LC_MONETARY=English_United States.1252 [4] LC_NUMERIC=C [5] LC_TIME=English_United States.1252 attached base packages: [1] grDevices datasets tcltk splines graphics utils stats [8] grid methods base other attached packages: [1] limma_3.14.4 genefilter_1.40.0 Biobase_2.18.0 BiocGenerics_0.4.0 [5] RCurl_1.95-4.1 bitops_1.0-6 reshape2_1.2.2 Hmisc_3.14-3 [9] Formula_1.1-1 survival_2.37-7 lattice_0.20-29 loaded via a namespace (and not attached): [1] annotate_1.36.0 AnnotationDbi_1.20.7 cluster_1.15.2 [4] DBI_0.2-7 IRanges_1.16.6 latticeExtra_0.6-26 [7] parallel_2.15.2 plyr_1.8 RColorBrewer_1.0-5 [10] RSQLite_0.11.4 stats4_2.15.2 stringr_0.6.2 [13] XML_3.98-1.1 xtable_1.7-3 -- Sent via the guest posting facility at bioconductor.org.