Skip to main content
Fig. 1 | BMC Bioinformatics

Fig. 1

From: C3: connect separate connected components to form a succinct disease module

Fig. 1

Connectivity pattern of disease module. a The flow framework of C3 Method. (i) Obtain the human interactome and the DAPs data for model building; (ii) Detect SCCs forming by DAPs; (iii) Quantify connectivity ability of intermediate proteins and interactions; (iv) Develop C3 algorithm; (v) Evaluate the succinctness of a produced disease module. b The detailed flow chart of the C3 algorithm. (i) The SCCs are identified for all seeds (set D) in network \(G(N,E)\), forming a connected components set of SCCs with size \({s}_{0}\). (ii) The connectivity significant p values of candidate proteins (immediate neighbors proteins of D) are calculated and ranked, p value < 0.05 and the lowest protein is added to D, then update the set of SCCs with size \({s}_{0}\). (iii) The connectivity significant p values of candidate interactions (adjacent edges of D) are calculated and ranked, p value < 0.05 and two endpoints proteins of the lowest interaction are added to D, then update the set of SCCs with size \({s}_{0}\). (iv) Steps (i)–(iii) are repeated until there is no protein or interaction with p value < 0.05 can connect at least two SCCs, i.e. \({s}_{0}\) is unchanged, the largest connected component of D is the C3 disease module. c schematic diagram of C3 protein (gray dot) and C3 interaction (thick black line), after adding C3 proteins and C3 interactions, we get the C3 disease module (gray shadow). The yellow shadows are SCCs. Red and white dots represent seeds and candidate proteins respectively, and thin black lines represent candidate interactions. In addition, the fraction of the connected seeds in C3 module divided by the total number of seeds is defined as succinctness

Back to article page