RT Journal Article
SR Electronic
T1 ROGUE: an entropy-based universal metric for assessing the purity of single cell population
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 819581
DO 10.1101/819581
A1 Baolin Liu
A1 Chenwei Li
A1 Ziyi Li
A1 Xianwen Ren
A1 Zemin Zhang
YR 2019
UL http://biorxiv.org/content/early/2019/10/27/819581.abstract
AB Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrated that our ROGUE metric is generalizable across datasets, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast and B cell datasets, we identified additional subtypes and demonstrated the application of ROGUE-guided analyses to detect true signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas.