Covariate-moderated Empirical Bayes Matrix Factorization
Keywords: Matrix factorization, Empirical Bayes, spatial transcriptomics, statistical genetics, non-negative factorization
TL;DR: We introduce a novel matrix factorization method that can leverage row or colum information to guide the factorization (e.g. images or graphs) our work is going well beyond the current covariate-guided factorization methods.
Abstract: Matrix factorization is a fundamental method in statistics and machine learning for inferring and summarizing structure in multivariate data. Modern data sets often come with ``side information'' of various forms (images, text, graphs) that can be leveraged to improve estimation of the underlying structure. However, existing methods that leverage side information are limited in the types of data they can incorporate and rely on specific parametric models. Here, we introduce a novel method for this problem, covariate-moderated empirical Bayes matrix factorization (cEBMF). cEBMF is a modular framework that accepts any type of side information that is processable by a neural network. The cEBMF framework can also accommodate different constraints and assumptions about the factors through the use of different priors and takes an empirical Bayes approach to adapt the priors to the data. We demonstrate the benefits of cEBMF in simulations and in an analysis of spatial transcriptomics data.
Supplementary Material: zip
Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
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Submission Number: 9812
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