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PLoS Biol. 2018 Oct 22;16(10):e2006687. doi: 10.1371/journal.pbio.2006687. eCollection 2018 Oct.

Quantitative assessment of cell population diversity in single-cell landscapes.

Liu Q1,2, Herring CA3,4, Sheng Q1,2, Ping J1,2, Simmons AJ3,5, Chen B3,5, Banerjee A3,5, Li W3,6, Gu G5, Coffey RJ3,5,6,7, Shyr Y1,2, Lau KS2,3,4,5.

Author information

1
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
2
Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
3
Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
4
Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
5
Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
6
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
7
Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, Tennessee, United States of America.

Abstract

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution.

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