Biclustering of time series data using factor graphs

Matteo Denitto, Alessandro Farinelli, Manuele Bicego

Abstract: Biclustering regards the simultaneous clustering of both rows and columns of a given data matrix. A specific application scenario for biclustering techniques concerns the analysis of gene expression time-series data, wherein columns dataset are temporally related. In this context, biclustering solutions should involve subset of genes sharing 'similar' behaviours among consecutive experimental conditions. Due to the intrinsic spatial constraint required by time-series dataset, current Factor Graph (FG) based approaches cannot be applied. In this paper we introduce Time-Series constraints forcing biclustering solution to have contiguous columns. We optimize the model by using the Max-Sum algorithm, whose message update rules have been derived exploiting The Higher Order Potentials (THOP). The proposed method has been assessed on a real world dataset and the retrieved biclusters show that it can provide accurate and biologically relevant solutions.