[BioC] limma & t-test, third time's a charm

[Giovanni Bucci [guest]]

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.