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.
- Fang, J; Chan, C; Owzar, K; Wang, L; Qin, D; Li, Q-J; Xie, J
- December 27, 2022
Volume / Issue
- 23 / 1
Start / End Page
- 269 -
Pubmed Central ID
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)