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维克托:通过最优回归对细胞类型注释进行验证和检查。

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

作者信息

Chang Chia-Jung, Hsu Chih-Yuan, Liu Qi, Shyr Yu

机构信息

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA.

Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37203, USA.

出版信息

Comput Struct Biotechnol J. 2024 Sep 2;23:3270-3280. doi: 10.1016/j.csbj.2024.08.028. eCollection 2024 Dec.

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.

摘要

单细胞RNA测序为探索细胞生物学中固有的异质性和动态性提供了前所未有的机会。数据分析中的一个关键步骤涉及细胞的自动注释。尽管已经开发了许多用于自动细胞注释的工具,但评估预测注释的可靠性仍然具有挑战性,特别是对于罕见和未知的细胞类型。在这里,我们介绍了VICTOR:通过最优回归进行细胞类型注释的验证和检查。VICTOR旨在通过具有最优阈值的弹性网络正则化回归来衡量细胞注释的可信度。我们证明,VICTOR在识别不准确注释方面表现良好,在各种单细胞数据集(包括平台内、跨平台、跨研究和跨组学设置)的诊断能力上超过了现有方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20e7/11408377/5f5dde0eb081/gr1.jpg

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