Continuous cell-state density inference and applications for single-cell data

Dominik Jenz Otto, Manu Setty, Brennan Dury

Keywords: single cell, cell differentiation, density, Gaussian process, Bayesian inference

TL;DR: Fast estimation of a differentiable cell-state density function from single-cell data

Abstract: Single-cell sequencing continuous to advance our understanding of cell biology, and critical cellular processes such as cell-differentiation. It has been natural to interpret the data as discrete measurements of individual cells and using k-nearest-neighbor graphs to represent the whole population has been a successful computational strategy. However, growing resolution and abundance of single-cell assays and interest to computationally decipher continuous cellular processes call for a likewise continuous representations of the cell populations. This encompasses not only the discrete observed states but instead a likelihood of occurrence for all possible cell states enabling even more specialize methods to model this continuity. To this end we have developed scDensity, an algorithm that leverages diffusion-map representation, nearest-neighbor distributions, and Gaussian processes to infer a differentiable function of the cell-state density representing the whole population. scDensity outperforms existing approaches for single-cell density estimations in accuracy, robustness, and resolution for RNA and ATAC modalities. scDensity is computationally efficient and scales to atlas-size single cell datasets. The resulting density function can comprehensibly represent entire cell populations and enable multiple novel downstream applications. This advancement could serve as a new paradigm of single-cell analysis.