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

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