Dear all,

I used the sam analysis from the siggenes package to analyze differentially
expressed genes, and here is the output:

  Delta    p0   False Called     FDR
1    0.1 0.235 3033.95   8740 0.08144
2    0.2 0.235 2539.17   8596 0.06930
3    0.3 0.235 2108.23   8452 0.05852
4    0.4 0.235 1788.62   8342 0.05030
5    0.5 0.235 1508.60   8226 0.04303
6    0.6 0.235 1261.23   8097 0.03655
7    0.7 0.235 1048.82   7964 0.03090
8    0.8 0.235  879.58   7828 0.02636
9    0.9 0.235  761.43   7719 0.02314
10   1.0 0.235  661.70   7580 0.02048
11   1.1 0.235  590.24   7476 0.01852
12   1.2 0.235  535.54   7407 0.01696


I think I understand the mathematics and computation in SAM, but I am not
quite sure how could I comprehend the results:
The "False" column is supposed to be the false positive identified, and the
"Called" column is the number of genes called to be significantly
differentially expressed, so the FDR is calculated by using False to divid
Called.

I understand the False is probably just from the average of the null
distribution, but it still doesn't make sense:
eg. if we look at the data when Delta is equal 0.2, there are arond
2539.17genes that are false positive, while the number of genes Called
to be
significant is 8596, then how could the FDR rate equals to 0.06930 ?

Am I understand this in a wrong way, could anyone please show me or direct
me to the right direction ?

I greatly appreciate for any help or comment.

Thanks,
Alex-

-- 
Lam C. Tsoi (Alex)
Medical University of South Carolina

	[[alternative HTML version deleted]]

