Hi everybody, I have a question regarding comparing limma and the t-test. 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 [[alternative HTML version deleted]]