Quantitative assessment of cell population diversity in single-cell landscapes

Qi Liu, Charles A. Herring, Quanhu Sheng, Jie Ping, Alan J. Simmons, Bob Chen, Amrita Banerjee, Wei Li, Guoqiang Gu, Robert J. Coffey, Yu Shyr, Ken S. Lau

研究成果: Article

7 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號e2006687
期刊PLoS biology
16
發行號10
DOIs
出版狀態Published - 2018 十月

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)

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    Liu, Q., Herring, C. A., Sheng, Q., Ping, J., Simmons, A. J., Chen, B., Banerjee, A., Li, W., Gu, G., Coffey, R. J., Shyr, Y., & Lau, K. S. (2018). Quantitative assessment of cell population diversity in single-cell landscapes. PLoS biology, 16(10), [e2006687]. https://doi.org/10.1371/journal.pbio.2006687