Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering

• Type of Material: Article
• Authors: Zhijin Wu, Brown University; Hao Wu, Emory University
• Language: English
• Date: 2020-05-25
• Publisher: BMC Publishing
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s). 2020.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s13059-020-02027-x
• Title of Journal or Parent Work: Genome Biology
• Volume: 21
• Issue: 1
• Start Page: 123
• End Page: 123
• Grant/Funding Information: This work was partially supported by the NIH award R01GM122083, P01NS097206, and Emory University WHSC 2018 Synergy Award for HW, and by R01GM122083, NSF DBI1054905, and P20GM109035 for ZW.
• Keywords: Expression; Landscape; Science & Technology; Single cell RNA-seq; Life Sciences & Biomedicine; Cancer; Clustering; Stem; Gene expression; Biotechnology & Applied Microbiology; Genetics & Heredity
• Research Categories: Biology, Microbiology; Biology, Biostatistics; Health Sciences, Oncology; Biology, Genetics

Abstract

Cell clustering is one of the most common routines in single cell RNA-seq data analyses, for which a number of specialized methods are available. The evaluation of these methods ignores an important biological characteristic that the structure for a population of cells is hierarchical, which could result in misleading evaluation results. In this work, we develop two new metrics that take into account the hierarchical structure of cell types. We illustrate the application of the new metrics in constructed examples as well as several real single cell datasets and show that they provide more biologically plausible results.