A tensor-based Markov chain method for module identification from multiple networks

The interactions among different genes, proteins and other small molecules are becoming more and more significant and have been studied intensively nowadays. One general way that helps people understand these interactions is to analyze networks constructed from genes/proteins. In particular, module structure as a common property of most biological networks has drawn much attention of researchers from different fields. In most cases, biological networks can be corrupted by noise in the data and the corruption may cause mis-identification of module structure. Besides, some structure may be destroyed when improper experimental settings are built up. Thus module structure may be unstable when one single network is employed. In this paper, we consider employing multiple networks for consistent module detection in order to reduce the effect of noise and experimental setting. Instead of considering different networks separately, our idea is to combine multiple networks together by building them into tensor structure data. Then given any node as prior label information, tensor-based Markov chains are constructed iteratively for identification of the modules shared by the multiple networks. In addition, the proposed tensor-based Markov chain algorithm is capable of simultaneously evaluating the contribution from each network. It would be useful to measure the consistency of modules in the multiple networks. In the experiments, we test our method on two groups of gene co-expression networks from human beings. We also validate the modules identified by the proposed method.