Genome Biology (Dec 2022)

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

  • Jiyuan Fang,
  • Cliburn Chan,
  • Kouros Owzar,
  • Liuyang Wang,
  • Diyuan Qin,
  • Qi-Jing Li,
  • Jichun Xie

DOI
https://doi.org/10.1186/s13059-022-02825-5
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 28

Abstract

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Abstract 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.

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