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

2021; Oxford University Press; Volume: 38; Issue: 6 Linguagem: Inglês

10.1093/bioinformatics/btab840

1367-4811

Yang Xu, Simon J. Baumgart, Christian M. Stegmann, Sikander Hayat,

Advanced biosensing and bioanalysis techniques

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.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 data are available at Bioinformatics online.