Single-cell Mayo Map (scMayoMap): an easy-to-use tool for cell type annotation in single-cell RNA-sequencing data analysis

Lu Yang, Yan Er Ng, Haipeng Sun, Ying Li, Lucas C.S. Chini, Nathan K. LeBrasseur, Jun Chen, Xu Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Single-cell RNA-sequencing (scRNA-seq) has become a widely used tool for both basic and translational biomedical research. In scRNA-seq data analysis, cell type annotation is an essential but challenging step. In the past few years, several annotation tools have been developed. These methods require either labeled training/reference datasets, which are not always available, or a list of predefined cell subset markers, which are subject to biases. Thus, a user-friendly and precise annotation tool is still critically needed. Results: We curated a comprehensive cell marker database named scMayoMapDatabase and developed a companion R package scMayoMap, an easy-to-use single-cell annotation tool, to provide fast and accurate cell type annotation. The effectiveness of scMayoMap was demonstrated in 48 independent scRNA-seq datasets across different platforms and tissues. Additionally, the scMayoMapDatabase can be integrated with other tools and further improve their performance. Conclusions: scMayoMap and scMayoMapDatabase will help investigators to define the cell types in their scRNA-seq data in a streamlined and user-friendly way.

Original languageEnglish (US)
Article number223
JournalBMC Biology
Volume21
Issue number1
DOIs
StatePublished - Dec 2023

Keywords

  • Cell type annotation
  • Cell type markers
  • Single-cell RNA-sequencing
  • scMayoMap
  • scMayoMapDatabase

ASJC Scopus subject areas

  • Biotechnology
  • Structural Biology
  • Ecology, Evolution, Behavior and Systematics
  • Physiology
  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences
  • Plant Science
  • Developmental Biology
  • Cell Biology

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