Table 1

Top hypotheses by score and corresponding p-values 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 dexamethasone-stimulated 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 p-values 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 p-values 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:10.1186/1471-2105-13-35

Open Data