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

被引：19

作者：

Wu, Zhijin ^{[1
]}

Wu, Hao ^{[2
]}

机构：

[1] Brown Univ, Dept Biostat, Providence, RI 02806 USA

[2] Emory Univ, Rollins Sch Publ Hlth, Dept Biostat & Bioinformat, 1518 Clifton Rd NE, Atlanta, GA 30322 USA

来源：

GENOME BIOLOGY | 2020年 / 21卷 / 01期

关键词：

Gene expression; Single cell RNA-seq; Clustering; STEM; EXPRESSION; LANDSCAPE; CANCER;

D O I：

10.1186/s13059-020-02027-x

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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.

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页数：14

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