Inferring cellular heterogeneity of associations from single cell genomics.

Abstract

Cell-to-cell variation has uncovered associations between cellular phenotypes. However, it remains challenging to address the cellular diversity of such associations. Here, we do not rely on the conventional assumption that the same association holds throughout the entire cell population. Instead, we assume that associations may exist in a certain subset of the cells. We developed CEllular Niche Association (CENA) to reliably predict pairwise associations together with the cell subsets in which the associations are detected. CENA does not rely on predefined subsets but only requires that the cells of each predicted subset would share a certain characteristic state. CENA may therefore reveal dynamic modulation of dependencies along cellular trajectories of temporally evolving states. Using simulated data, we show the advantage of CENA over existing methods and its scalability to a large number of cells. Application of CENA to real biological data demonstrates dynamic changes in associations that would be otherwise masked. CENA is available as an R package at Github: https://github.com/mayalevy/CENA and is accompanied by a complete set of documentations and instructions. iritgv@gmail.com. Supplementary data are available at Bioinformatics online.