Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, 100872, China.
Nat Commun. 2020 May 11;11(1):2338. doi: 10.1038/s41467-020-15851-3.
Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function. Through iterative self-learning, DESC gradually removes batch effects, as long as technical differences across batches are smaller than true biological variations. As a soft clustering algorithm, cluster assignment probabilities from DESC are biologically interpretable and can reveal both discrete and pseudotemporal structure of cells. Comprehensive evaluations show that DESC offers a proper balance of clustering accuracy and stability, has a small footprint on memory, does not explicitly require batch information for batch effect removal, and can utilize GPU when available. As the scale of single-cell studies continues to grow, we believe DESC will offer a valuable tool for biomedical researchers to disentangle complex cellular heterogeneity.
单细胞 RNA 测序 (scRNA-seq) 可以通过无监督聚类来描述细胞类型和状态，但越来越多的细胞和批次效应带来了计算挑战。我们提出了 DESC，这是一种无监督的深度嵌入算法，通过迭代优化聚类目标函数对 scRNA-seq 数据进行聚类。通过迭代式的自我学习，只要批次间的技术差异小于真实的生物学差异，DESC 就可以逐渐去除批次效应。作为一种软聚类算法，DESC 的聚类分配概率具有生物学解释性，并且可以揭示细胞的离散和伪时间结构。综合评估表明，DESC 在聚类准确性和稳定性之间提供了适当的平衡，对内存的占用较小，不需要显式的批次信息来去除批次效应，并且在可用时可以利用 GPU。随着单细胞研究规模的不断扩大，我们相信 DESC 将为生物医学研究人员提供一种有价值的工具，用于解析复杂的细胞异质性。
