Alsaggaf Ibrahim, Buchan Daniel, Wan Cen
School of Computing and Mathematical Sciences, Birkbeck, University of London, London WC1E 7HX, United Kingdom.
Department of Computer Science, University College London, London WC1E 6BT, United Kingdom.
Bioinformatics. 2025 Sep 1;41(9). doi: 10.1093/bioinformatics/btaf437.
Cell-type identification is one of the most important tasks in single-cell RNA Sequencing (scRNA-Seq) analysis. Recent research has revealed contrastive learning's great potential in handling multiple cell-type identification tasks.
In this work, we proposed a novel augmentation-free scRNA-Seq contrastive learning (AF-RCL) algorithm, which simplifies the conventional data augmentation operation and adopts a new contrastive learning loss function. A large-scale empirical evaluation suggests that AF-RCL not only outperformed other contrastive learning-based cell-type identification methods but also obtained state-of-the-art predictive performance compared with other well-known cell-type identification methods. Further analysis also shows AF-RCL's advantages in learning high-quality discriminative feature representations based on scRNA-Seq expression profiles.
The source code is available at https://doi.org/10.6084/m9.figshare.28830311.v1 and at https://github.com/ibrahimsaggaf/AFRCL. The pre-trained AF-RCL encoders can be downloaded from https://doi.org/10.5281/zenodo.15109736, and the scRNA-Seq datasets used in this work can be downloaded from https://doi.org/10.5281/zenodo.8087611.
细胞类型识别是单细胞RNA测序（scRNA-Seq）分析中最重要的任务之一。最近的研究揭示了对比学习在处理多细胞类型识别任务方面的巨大潜力。
在这项工作中，我们提出了一种新颖的无增强scRNA-Seq对比学习（AF-RCL）算法，该算法简化了传统的数据增强操作，并采用了新的对比学习损失函数。大规模实证评估表明，AF-RCL不仅优于其他基于对比学习的细胞类型识别方法，而且与其他知名细胞类型识别方法相比，还获得了当前最优的预测性能。进一步分析还显示了AF-RCL在基于scRNA-Seq表达谱学习高质量判别特征表示方面的优势。
