Bayesian Density-Density Regression with Application to Cell-Cell Communications

Abstract

We introduce a scalable framework for regressing multivariate distributions onto multivariate distributions, motivated by the application of inferring cell-cell communication from population-scale single-cell data. The observed data consist of pairs of multivariate distributions for ligands from one cell type and corresponding receptors from another. For each ordered pair e=(l,r) of cell types (l r) and each sample i = 1, , n, we observe a pair of distributions (F_ei, G_ei) of gene expressions for ligands and receptors of cell types l and r, respectively. The aim is to set up a regression of receptor distributions G_ei given ligand distributions F_ei. A key challenge is that these distributions reside in distinct spaces of differing dimensions. We formulate the regression of multivariate densities on multivariate densities using a generalized Bayes framework with the sliced Wasserstein distance between fitted and observed distributions. Finally, we use inference under such regressions to define a directed graph for cell-cell communications.

Tasks

Reproductions