This algorithm is used for identifying conserved functional modules in multiple networks, as described in:
Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks. Peizhuo Wang, Lin Gao, Yuxuan Hu and Feng Li. BMC Bioinformatics, 2018, 19(1): 394.
This algorithm is implemented primarily in Matlab 2015b.
The code takes a series of networks as an input. These networks must be stored in a variable of type cell in matlab. Each network can be represented by the following two types:
- adjacency matrix
- edge list, e.g:
1 2 0.62
1 3 0.88
2 9 0.14
...
These codes are for generating synthetic datasets:
syn_dataset_common.m: Conserved modules have the same size and are common to a given set of networks.syn_dataset_overlap.m: Conserved modules are present only in a subset of networks and they are the overlapping parts of specific modules across different networks.
ConMod.m: The implementation of the ConMod algorithm.
function modulesfinal = ConMod(multiNetworks, N, K, lambda, xita, maxIter)
% INPUT:
% multiNetworks: a cell contains multiple networks, each of which is presented by edgelist format or a full matrix with N nodes
% N: the number of all nodes
% K: the number of hidden factors
% lambda: a vector which contains the parameters for balancing the relative weight among different views
% xita: the parameter for selecting nodes
% maxIter: the maximum number of iterations for multi-view NMF
%
% OUTPUT:
% modulesfinal: a cell which contains the final conserved modules
featureNets.m: Compute two feature matrices which characterize the multiple networks.
function [Strength, Participation] = featureNets(multiNetworks, N)
% INPUT:
% multiNetworks: a cell contains multiple networks, each is presented by a sparse matrix or a full matrix with N nodes
% N: the number of all nodes
%
% OUTPUT:
% Strength : N x N matrix for Connection Strength
% Participation : N x N matrix for Participation Coefficient
multiViewNMF.m: Multi-view non-negative symmetric matrix factorization.
function [H, Hc, objValue] = multiViewNMF(X, K, lambda, maxIter)
% INPUT:
% X: a cell which contains symmetric matrices
% K: the number of hidden factors
% lambda: a vector which contains the parameters for balancing the relative weight among different views
% maxiter: the maximum number of iterations
%
% OUTPUT:
% H: a cell containing factor matrices for all views
% Hc: the result consensus factor matrix
% objValue: the value of objective function
moduleNodesSelection.m: Assigning the module members by a soft node selection procedure and then truing the modules to obtain more accurate results
function modulesFinal = moduleNodesSelection(Hc, xita)
% INPUT:
% Hc: the consensus factor matrix
% xita: the parameter for selecting nodes
%
% OUTPUT:
% modulesFinal: a cell which contains the final result modules
If you have any questions, please contact wangpeizhuo_37@163.com.