Table 1 

Top hypotheses by score and corresponding pvalues on an example dataset 

Rank 
Hypothesis Name 
Correct 
Incorrect 
Score 
Ternary Dot Product p 
Causal Graph p 


1 
Response to Hypoxia+ 
48 
9 
37 
2 × 10^{12} 
< 0.001 
2 
Dexamethasone+ 
20 
4 
16 
6 × 10^{6} 
< 0.001 
3 
Hydrocortisone+ 
17 
4 
13 
1 × 10^{8} 
< 0.001 
4 
PGR+ 
12 
1 
11 
6 × 10^{8} 
< 0.001 
5 
SRF+ 
10 
0 
10 
3 × 10^{5} 
< 0.001 
6 
KLF4+ 
9 
0 
9 
3 × 10^{6} 
< 0.001 
7 
NR3C1+ 
12 
4 
8 
7 × 10^{4} 
< 0.001 
7 
Glucocorticoid+ 
12 
4 
8 
8 × 10^{5} 
< 0.001 
7 
CCND1+ 
9 
1 
8 
3 × 10^{4} 
< 0.001 
7 
Triamcinolone acetonide+ 
8 
0 
8 
9 × 10^{7} 
< 0.001 
... 
... 
... 
... 
... 
... 
... 
17 
NRF2+ 
9 
4 
5 
0.18 
0.07 


Top hypotheses by score in an example experimental dataset of dexamethasonestimulated chondrocytes (GEO accession GSE7683 [21]). Each hypothesis is scored by the difference between the numbers of correct and incorrect predictions. Significance is assessed by the Ternary Dot Product and Causal Graph Randomization pvalues discussed in the text; the latter numbers are estimates based on 1000 runs of graph randomization and for this reason are always a multiple of 0.001. When no randomized graph with a better score for the given hypothesis is detected, we indicate that as "p < 0.001." Note that hypotheses with the same numbers of correct and incorrect predictions do not necessarily have the same pvalues because the significance calculation takes into account the full contingency table for each hypothesis; some hypotheses result in more predicted regulations than others. 

Chindelevitch et al. BMC Bioinformatics 2012 13:35 doi:10.1186/147121051335 