VICTOR: Validation and inspection of cell type annotation through optimal regression

Date:	2 September 2024
PMID:	PMC11408377
Category:	N/A
Authors:	Chia-Jung Chang, Chih-Yuan Hsu, Qi Liu, Yu Shyr
Abstract:

Single-cell RNA sequencing provides unprecedent opportunities to explore the heterogeneity and dynamics inherent in cellular biology. An essential step in the data analysis involves the automatic annotation of cells. Despite development of numerous tools for automated cell annotation, assessing the reliability of predicted annotations remains challenging, particularly for rare and unknown cell types. Here, we introduce VICTOR: Validation and inspection of cell type annotation through optimal regression. VICTOR aims to gauge the confidence of cell annotations by an elastic-net regularized regression with optimal thresholds. We demonstrated that VICTOR performed well in identifying inaccurate annotations, surpassing existing methods in diagnostic ability across various single-cell datasets, including within-platform, cross-platform, cross-studies, and cross-omics settings.

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Acknowledgements:

The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute, or the National Institute of Health.

The Translational and Basic Science Research in Early Lesions (TBEL) Research Consortia is supported and funded by grants from the National Cancer Institute and the National Institutes of Health under the following award numbers:

Project Number:	Awardee Organization
U54CA274374	Fred Hutchinson Cancer Center
U54CA274375	Houston Methodist Research Institute
U54CA274370	Johns Hopkins University
U54CA274371	UT MD Anderson Cancer Center
U54CA274367	Vanderbilt University Medical Center