RETRACTED ARTICLE: Detangling PPI networks to uncover functionally meaningful clusters

BMC Systems Biology

Table 7 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

Method	Enriched Clusters	# NEC	% NEC	# NEC S	% NEC S
PPI	201/225 (89.33%)	5650.0	92.65%	2409.0	39.50%
4.5	185/244 (75.82%)	2190.0	35.93%	1322.0	21.69%
5.0	176/252 (69.84%)	5003.0	82.07%	2100.0	34.45%
5.5	175/251 (69.72%)	4651.0	76.30%	2223.0	36.47%
6.0	168/224 (75.00%)	4997.0	81.97%	2473.0	40.57%

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. In this case, the Spectral algorithm run directly on the PPI network results in a higher %NEC statistic than any of the DSD-preprocessed results. However, without cluster size restrictions %NEC S is the most meaningful statistic, and it is better than Spectral run alone at a DSD threshold of 6.0
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