MultiFacTV: Finding modules from higher-order gene expression profiles with time dimension

摘要

Module detection is an important task in bioinformatics which aims at finding a set of cells/genes that interact together to be responsible for some biological functionalities. In this paper, we propose a novel tensor factorization approach to finding modules from higher-order gene expression profiles with the time dimension, e.g., gene × condition × time data. The main idea is to incorporate a total variation regularization term for the time dimension during the tensor factorization, and then use the factorization results to identify the modules. Experimental results on two real gene × condition × time datasets have shown the effectiveness of the proposed method.