Method of the joint clustering in network and correlation spaces

Network algorithms are often used to analyze and interpret the biological data. One of the widely used approaches is to solve the problem of identifying an active module, where a connected subnetwork of a biological network is selected which best reflects the difference between the two considered biological conditions. In this work this approach is extended to the case of a larger number of biological conditions and the problem of the joint clustering in network and correlation spaces is formulated.
To solve this problem, an iterative method is proposed at takes as the input graph $G$ and matrix $X$, in which the rows correspond to the vertices of the graph. As the output, the algorithm produces a set of subgraphs of the graph $G$ so that each subgraph is connected and the rows corresponding to its vertices have a high pairwise correlation. The efficiency of the method is confirmed by an experimental study on the simulated data.