利用深度学习对人类 265 种细胞类型进行单细胞类型注释。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2024-04-08 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae054
Sherry Dong, Kaiwen Deng, Xiuzhen Huang
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引用次数: 0

摘要

动机在分析单细胞 RNA 测序数据时,标注细胞类型是一项具有挑战性但又必不可少的任务。然而,由于缺乏黄金标准,很难对算法进行公平的评估,而且在基准测试中,过拟合算法可能会受到青睐。为了应对这一挑战,我们开发了一种基于深度学习的单细胞类型预测工具,根据来自约 500 万个细胞的数据,为人类的 265 种不同细胞类型分配细胞类型:在跨数据集评估时,我们的 ROC 曲线下面积(AUC)中位数达到了 0.93。我们发现,不同实验室生成的现有数据库中不一致的标记导致了模型的错误。因此,我们使用细胞本体来校正注释并重新训练模型,结果 AUC 中位数达到了 0.971。我们的研究揭示了当前数据库注释所能达到的精确度的限制因素,并为未来的自动细胞注释方法指出了基于算法的金标准校正的解决方案:代码见:https://github.com/SherrySDong/Hierarchical-Correction-Improves-Automated-Single-cell-Type-Annotation。本研究使用的数据列于补充表 S1,可在 CZI 数据库中检索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Single-cell type annotation with deep learning in 265 cell types for humans.

Motivation: Annotating cell types is a challenging yet essential task in analyzing single-cell RNA sequencing data. However, due to the lack of a gold standard, it is difficult to evaluate the algorithms fairly and an overfitting algorithm may be favored in benchmarks. To address this challenge, we developed a deep learning-based single-cell type prediction tool that assigns the cell type to 265 different cell types for humans, based on data from approximately five million cells.

Results: We achieved a median area under the ROC curve (AUC) of 0.93 when evaluated across datasets. We found that inconsistent labeling in the existing database generated by different labs contributed to the mistakes of the model. Therefore, we used cell ontology to correct the annotations and retrained the model, which resulted in 0.971 median AUC. Our study reveals a limiting factor of the accuracy one may achieve with the current database annotation and points to the solutions towards an algorithm-based correction of the gold standard for future automated cell annotation approaches.

Availability and implementation: The code is available at: https://github.com/SherrySDong/Hierarchical-Correction-Improves-Automated-Single-cell-Type-Annotation. Data used in this study are listed in Supplementary Table S1 and are retrievable at the CZI database.

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CiteScore
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