A multi-task spectral clustering method, to identify common and context-specific aspects of genome architecture. Arboretum-Hi-C is based on a previous multi-task clustering approach, Arboretum, which uses a generative probabilistic model to cluster expression data from multiple species while accounting for the hierarchical relationships among the species as described by a phylogenetic tree. However, instead of expression matrices at each leaf node, we now have Hi-C interaction graphs. Arboretum-Hi-C compares systematically the 3D organization across multiple cell types and species. It combines two clustering strategies: spectral clustering and multi-task clustering. Multi-task clustering is a special case of multi-task learning, where the goal is to solve multiple learning tasks simultaneously.
Identifies gene clusters from Hi-C data. GraphTeams is an open source software intending to assist users in spatial gene clusters and gene sets with functional associations discovering. This application is based on an extension of a model dedicated to the detection of δ-teams with families. This program can handle data from both inter- and intrachromosomal Hi-C maps and can be applied to data related with human and mouse.