Table 2.
With length bias | No length bias | ||||||||
---|---|---|---|---|---|---|---|---|---|
β=0.15 | β=0.3 | β=0.15 | |||||||
n de= | 6 | 8 | 10 | 6 | 8 | 10 | 6 | 8 | 10 |
Weighted | 0.36 | 0.48 | 0.77 | 0.81 | 0.95 | 0.98 | 0.61 | 0.93 | 0.96 |
Unweighted | 0.29 | 0.45 | 0.75 | 0.77 | 0.90 | 0.96 | 0.61 | 0.94 | 0.97 |
GOSeq | 0.23 | 0.38 | 0.56 | 0.97 | 1.0 | 1.0 | 0.96 | 1.0 | 1.0 |
CAMERA | 0.01 | 0 | 0 | 0 | 0 | 0 | 0.02 | 0.01 | 0.02 |
Power of identifying set 1 as DE (p-value <0.05) under different β and length bias. The weighted SeqGSA increases the power of the unweighted procedure for detecting set 1. SeqGSA is more powerful with small DE effect (β=0.15), while GOSeq is more powerful with large DE effect (β=0.3). The two procedures of SeqGSA perform similarly under no length bias