RETRACTED ARTICLE: Detangling PPI networks to uncover functionally meaningful clusters

BMC Systems Biology

Table 8 The performance of Spectral versus Spectral plus DSD at different edge removal thresholds when the input parameter K in all cases is set to 300, but then we discard clusters of size < 3 and split clusters of size > 100

Method	Enriched Clusters	# NEC	% NEC	# NEC S	% NEC S
PPI	234/324 (72.22%)	3082.0	50.54%	2158.0	35.39%
4.5	194/266 (72.93%)	1647.0	27.02%	1330.0	21.82%
5.0	199/309 (64.40%)	3589.0	58.87%	2203.0	36.14%
5.5	189/291 (64.95%)	3765.0	61.76%	2228.0	36.55%
6.0	177/249 (71.08%)	4670.0	76.61%	2490.0	40.85%

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. For every threshold we tested ≥ 5, the percentage of nodes in enriched clusters is better than Spectral run alone for both measures
Bolded values represent the best values achieved for the %NEC and %NEC S statistics comparing the PPI network and different DSD detangling thresholds

ISSN: 1752-0509