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Table 2 The performance of Walktrap versus Walktrap plus DSD at different edge removal thresholds; We discard clusters of size < 3

From: RETRACTED ARTICLE: Detangling PPI networks to uncover functionally meaningful clusters

Method

Enriched Clusters

# NEC

% NEC

# NEC S

% NEC S

PPI

8/19 (42.11%)

280.0

4.59%

226.0

3.71%

3.5

63/105 (60.00%)

504.0

8.27%

464.0

7.61%

4.0

128/189 (67.72%)

1108.0

18.18%

919.0

15.08%

4.5

207/311 (66.56%)

1951.0

32.00%

1430.0

23.46%

5.0

153/303 (50.50%)

2476.0

40.62%

1531.0

25.11%

5.5

70/164 (42.68%)

2418.0

39.67%

1269.0

20.82%

6.0

43/88 (48.86%)

1398.0

22.93%

837.0

13.73%

  1. NEC= “Nodes in Enriched Clusters”. We calculate %NEC in two settings: %NEC is enrichment in the GO hierarchy with terms above the fifth level filtered out, and %NEC S uses the same filtered GO hierarchy, but then only gives a node credit if there is a match between one of the node’s labels and one of the terms for which there is GO enrichment for the cluster. Walktrap run alone produces a very small number of clusters; because of this only the S statistic is meaningful to compare the DSD versions against unmodified Walktrap. Walktrap with DSD at thresholds between 4.5 and 6 trade a larger number of smaller clusters for a lower percentage of nodes in enriched clusters
  2. Bolded values represent the best values achieved for the %NEC and %NEC S statistics comparing the PPI network and different DSD detangling thresholds