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基于坐标卷积神经网络的单细胞RNA测序的自动细胞类型识别方法

Shuang Xu¹, Wen Yan¹, Renchu Guan²

¹Department of Anesthesiology, The Second Hospital of Jilin University, Changchun 130041, China.

Computational biology and chemistry

|January 24, 2026

概括

我们介绍BP-Coord,这是一种用于单细胞RNA测序 (scRNA-seq) 数据中细胞类型识别的新型深度学习方法. 通过位置信息,BP-Coord 增强了卷积神经网络 (CNN),在准确性和稳定性方面超过了现有的方法.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

细胞类型识别对于单细胞RNA测序 (scRNA-seq) 数据分析至关重要.
支持向量机 (SVM) 分类器是有效的,但面临的可扩展性挑战随着数据量的增加.
卷积神经网络 (CNN) 显示出希望,但在scRNA-seq数据中与翻译不变性作斗争.

研究的目的:

开发一种新型的深度学习方法,用于在scRNA-seq数据中准确和强大的细胞类型识别.
为了解决用于scRNA-seq分析的CNN中翻译不变性的局限性.
为了提高自动细胞类型分类的性能.

主要方法:

拟议的BP-Coord方法将坐标信息集成到CNN中.
采用双立方插位上采样和CoordConv层来增强空间意识.
在五个公开的scRNA-seq基准数据集上训练和评估模型.

主要成果:

BP-Coord 始终优于包括 SVM,SuperCT 和 scGAC 在内的最先进的方法.
在大规模的PBMC数据集上实现了高达3.5%的精度改进.
在不平衡和小样本数据集上表现出卓越的稳定性.

结论:

将显式位置编码纳入CNN是自动细胞类型识别的有效方法.
BP-Coord为scRNA-seq数据分析的传统方法提供了一个有希望的替代方案.
该方法显示了在大型和复杂的生物数据集中推进细胞类型分类的巨大潜力.

关键词:

自动细胞类型识别自动识别在 CoordConv 神经网络中.预测算法预测算法单细胞RNA序列的一个单细胞RNA序列.

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