Quantitative assessment of cell population diversity in single-cell landscapes

• Published in: PLoS biology Vol. 16; no. 10; p. e2006687
• Main Authors: Liu, Qi, Herring, Charles A, Sheng, Quanhu, Ping, Jie, Simmons, Alan J, Chen, Bob, Banerjee, Amrita, Li, Wei, Gu, Guoqiang, Coffey, Robert J, Shyr, Yu, Lau, Ken S
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.10.2018; Public Library of Science (PLoS)
• Subjects: Animals; Bioinformatics; Biology and Life Sciences; Brain - cytology; Brain - metabolism; CD4-Positive T-Lymphocytes - cytology; CD4-Positive T-Lymphocytes - metabolism; CD8-Positive T-Lymphocytes - cytology; CD8-Positive T-Lymphocytes - metabolism; Cluster Analysis; Colorectal cancer; Computer and Information Sciences; Computer applications; Computer Simulation; Databases, Nucleic Acid; Datasets; Developmental biology; Ecology and Environmental Sciences; Funding; Gene expression; Gene Expression Profiling - methods; Gene Expression Profiling - statistics & numerical data; Gene sequencing; Genomes; Humans; Intestinal Mucosa - cytology; Intestinal Mucosa - metabolism; Landscape; Medicine; Medicine and Health Sciences; Methods; Methods and Resources; Mice; Mice, Inbred C57BL; Models, Biological; Neoplasms, Experimental - genetics; Neoplasms, Experimental - pathology; Oligodendroglia - cytology; Oligodendroglia - metabolism; Phenotypes; Physical Sciences; Population; Populations; Research and Analysis Methods; Ribonucleic acid; RNA; Sequence Analysis, RNA - methods; Sequence Analysis, RNA - statistics & numerical data; Shrinkage; Single-Cell Analysis - methods; Single-Cell Analysis - statistics & numerical data; Software; Statistical methods; Tumorigenesis; Workflow; United States > US; Tennessee; Nashville Tennessee
• ISSN: 1545-7885; 1544-9173; 1545-7885
• 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.