Cui Xiaoxu, Wu Renkai, Liu Yinghao, Chen Peizhan, Chang Qing, Liang Pengchen, He Changyu
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Shanghai University of Medicine & Health Sciences, Shanghai, China.
BMC Bioinformatics. 2025 Jan 29;26(1):33. doi: 10.1186/s12859-025-06047-x.
Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology has become increasingly vital. This study explores the application of deep learning to single-cell data clustering, with a particular focus on managing sparse, high-dimensional data.
We propose the SMD deep learning model, which integrates nonlinear dimensionality reduction techniques with a porous dilated attention gate component. Built upon a convolutional autoencoder and informed by the negative binomial distribution, the SMD model efficiently captures essential cell clustering features and dynamically adjusts feature weights. Comprehensive evaluation on both public datasets and proprietary osteosarcoma data highlights the SMD model's efficacy in achieving precise classifications for single-cell data clustering, showcasing its potential for advanced transcriptomic analysis.
This study underscores the potential of deep learning-specifically the SMD model-in advancing single-cell RNA sequencing data analysis. By integrating innovative computational techniques, the SMD model provides a powerful framework for unraveling cellular complexities, enhancing our understanding of biological processes, and elucidating disease mechanisms. The code is available from https://github.com/xiaoxuc/scSMD .
单细胞RNA测序（scRNA-seq）通过为细胞异质性、发育过程和疾病机制提供新的见解，改变了生物学研究。随着scRNA-seq技术的进步，其在现代生物学中的作用变得越来越重要。本研究探索深度学习在单细胞数据聚类中的应用，特别关注处理稀疏、高维数据。
我们提出了SMD深度学习模型，该模型将非线性降维技术与多孔扩张注意力门组件相结合。基于卷积自动编码器构建，并以负二项分布为依据，SMD模型有效地捕获了关键的细胞聚类特征，并动态调整特征权重。对公共数据集和专有骨肉瘤数据的综合评估突出了SMD模型在实现单细胞数据聚类精确分类方面的有效性，展示了其在高级转录组分析中的潜力。
本研究强调了深度学习——特别是SMD模型——在推进单细胞RNA测序数据分析方面的潜力。通过整合创新的计算技术，SMD模型为揭示细胞复杂性、增强我们对生物过程的理解以及阐明疾病机制提供了一个强大的框架。代码可从https://github.com/xiaoxuc/scSMD获取。
