CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions

BMC Bioinformatics

Table 1 Clusters obtained using CASCADE for 3 biological network data sets (the yeast DNA damage response network, Ra-pamycin gene modules network, Rich medium gene modules network).

				Distribution
Data set	Cluster	Size	Density	H	D	U	-Log p	Function
Yeast DDR network	1	49	0.063	18.4	81.6	0.0	0.5	DNA repair
	2	16	0.175	81.3	18.7	0.0	3.6	Cell cycle
	3	9	0.222	44.4	55.5	0.0	3.6	Proteasome
	4	7	0.286	57.1	42.9	0.0	1.7	Metabolism
	5	7	0.286	71.4	28.6	0.0	1.2	Stress response
	6	6	0.333	83.3	16.7	0.0	3.2	Metabolism
Rapamycin gene modules network	1	19	0.198	42.1	47.4	10.5	2.7	Nitrogen/sulfur metabolism
	2	12	0.227	33.3	0.0	66.6	1.1	Pheromone response
	3	9	0.277	77.8	0.0	22.2	5.0	Pheromone response
	4	7	0.285	71.4	28.6	0.0	2.9	AA metabolism/biosynthesis
Rich medium gene modules network	1	54	0.050	64.8	33.3	1.85	14.1	Cell cycle
	2	28	0.111	75.0	14.3	10.7	10.2	Ribosome biogenesis
	3	16	0.179	62.5	12.5	25.0	9.7	Respiration
	4	13	0.222	69.2	30.8	0.0	8.1	Energy/carbohydrate metabolism

The first column is a cluster identifier; the Size column indicates the number of molecular components in each cluster; the Density indicates the percentage of possible biological interactions that are present; the H column indicates the percentage of molecular components concordant with the major function indicated in the last column; the D column indicates the percentage of molecular components discordant with the major function and U column indicates percentage of molecular components not assigned to any function. The -log p values for biological function are shown.

ISSN: 1471-2105