MACA: marker-based automatic cell-type annotation for single-cell expression data

• Published in: Bioinformatics 2022-03, Vol.38 (6), p.1756-1760
• Main Authors: Xu, Yang, Baumgart, Simon J, Stegmann, Christian M, Hayat, Sikander
• Format: Article
• Language: English
• Subjects: Databases, Factual; Gene Expression Profiling; Humans; Lepidium; RNA-Seq; Single-Cell Analysis; Software
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btab840

Abstract Summary Accurately identifying cell types is a critical step in single-cell sequencing analyses. Here, we present marker-based automatic cell-type annotation (MACA), a new tool for annotating single-cell transcriptomics datasets. We developed MACA by testing four cell-type scoring methods with two public cell-marker databases as reference in six single-cell studies. MACA compares favorably to four existing marker-based cell-type annotation methods in terms of accuracy and speed. We show that MACA can annotate a large single-nuclei RNA-seq study in minutes on human hearts with ∼290K cells. MACA scales easily to large datasets and can broadly help experts to annotate cell types in single-cell transcriptomics datasets, and we envision MACA provides a new opportunity for integration and standardization of cell-type annotation across multiple datasets. Availability and implementation MACA is written in python and released under GNU General Public License v3.0. The source code is available at https://github.com/ImXman/MACA. Supplementary information Supplementary data are available at Bioinformatics online.