Work

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

Year: 2021

Type: article

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.

Source: Genome biology

DOI: 10.1186/s13059-021-02445-5

Authors: Siyao Liu, Aatish Thennavan, Joseph P. Garay, J. S. Marron, Charles M. Perou

Institutions: University of North Carolina at Chapel Hill, UNC Lineberger Comprehensive Cancer Center, Oregon Health & Science University

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $4,890

Funders: Breast Cancer Research Foundation, Susan G. Komen, National Institutes of Health, National Cancer Institute