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

Liu Q, Herring CA, Sheng Q, Ping J, Simmons AJ, Chen B, Banerjee A, Li W, Gu G, Coffey RJ, Shyr Y, Lau KS
PLoS Biol. 2018 16 (10): e2006687

PMID: 30346945 · PMCID: PMC6211764 · DOI:10.1371/journal.pbio.2006687

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.

MeSH Terms (19)

Animals Brain CD4-Positive T-Lymphocytes CD8-Positive T-Lymphocytes Cluster Analysis Computer Simulation Databases, Nucleic Acid Gene Expression Profiling Humans Intestinal Mucosa Mice Mice, Inbred C57BL Models, Biological Neoplasms, Experimental Oligodendroglia Sequence Analysis, RNA Single-Cell Analysis Software Workflow

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