Optimal transport improves cell-cell similarity inference in single-cell omics data.

ABSTRACT:

Motivation

High-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification and characterization of cellular heterogeneity are typically achieved through unsupervised clustering, which crucially relies on a similarity metric.

Results

We here propose the use of Optimal Transport (OT) as a cell-cell similarity metric for single-cell omics data. OT defines distances to compare high-dimensional data represented as probability distributions. To speed up computations and cope with the high dimensionality of single-cell data, we consider the entropic regularization of the classical OT distance. We then extensively benchmark OT against state-of-the-a

SUBMITTER: Huizing GJ 

PROVIDER: S-EPMC9004651 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature