Clustering Deviation Index (CDI): a robust and accurate internal measure for evaluating scRNA-seq data clustering.

Journal Article (Journal Article)

Most single-cell RNA sequencing (scRNA-seq) analyses begin with cell clustering; thus, the clustering accuracy considerably impacts the validity of downstream analyses. In contrast with the abundance of clustering methods, the tools to assess the clustering accuracy are limited. We propose a new Clustering Deviation Index (CDI) that measures the deviation of any clustering label set from the observed single-cell data. We conduct in silico and experimental scRNA-seq studies to show that CDI can select the optimal clustering label set. As a result, CDI also informs the optimal tuning parameters for any given clustering method and the correct number of cluster components.

Full Text

Duke Authors

Cited Authors

  • Fang, J; Chan, C; Owzar, K; Wang, L; Qin, D; Li, Q-J; Xie, J

Published Date

  • December 27, 2022

Published In

Volume / Issue

  • 23 / 1

Start / End Page

  • 269 -

PubMed ID

  • 36575517

Pubmed Central ID

  • PMC9793368

Electronic International Standard Serial Number (EISSN)

  • 1474-760X

Digital Object Identifier (DOI)

  • 10.1186/s13059-022-02825-5


  • eng

Conference Location

  • England