GmGM: a fast Gaussian graphical model for multi-modal data

Bailey Andrew, David R Westhead, Luisa Cutillo

Keywords: bigraphical lasso, network inference, gaussian graphical models, multi-omics, single-cell

TL;DR: We produce a type of Gaussian Graphical Model extending tensor-graphical lasso methods that is simultaneously much faster than prior work and generalizes to multi-modal data.

Abstract: This paper introduces the Gaussian multi-Graphical Model, a model to construct sparse graph representations of matrix- and tensor-variate data. We generalize prior work in this area by simultaneously learning this representation across several tensors that share axes, which is necessary to allow the analysis of multimodal datasets such as those encountered in multi-omics. Our algorithm uses only a single eigendecomposition per axis, achieving an order of magnitude speedup over prior work in the ungeneralized case. This allows the use of our methodology on large multi-modal datasets such as single-cell multi-omics data, which was challenging with previous approaches. We validate our model on synthetic data and five real-world datasets.

Submission Number: 12382