MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data

• Main Authors: Siyao Liu, Aatish Thennavan, Joseph P. Garay, J. S. Marron, Charles M. Perou
• Format: Article
• Language: English
• Published: BMC 2021-08-01
• Series: Genome Biology
• Subjects: Single-cell RNA-seq; Clustering; Multi-scale; Multi-resolution; Genomics; Reproducibility
• Online Access: https://doi.org/10.1186/s13059-021-02445-5

Abstract Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present.