Biclustering of time series data using factor graphs

Biclustering regards the simultaneous clustering of both rows and columns of a given data matrix. A specific applica- tion scenario for biclustering techniques concerns the anal- ysis of gene expression time-series data, wherein columns dataset are temporally related. In this context, bicluster- ing solutions should involve subset of genes sharing ‘simi- lar’ behaviours among consecutive experimental conditions. Due to the intrinsic spatial constraint required by time-series dataset, current Factor Graph (FG) based approaches can- not 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 pro- posed method has been assessed on a real world dataset and the retrieved biclusters show that it can provide accurate and biologically relevant solutions.