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
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.