College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China.
Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
Cell Rep Methods. 2023 Jan 5;3(1):100382. doi: 10.1016/j.crmeth.2022.100382. eCollection 2023 Jan 23.
Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.
单细胞 RNA 测序 (scRNA-seq) 是一种革命性的技术，可以确定单个细胞的精确基因表达，并鉴定细胞异质性和亚群。然而，scRNA-seq 的技术限制导致数据异质且稀疏。在这里，我们提出了 autoCell，这是一种用于 scRNA-seq 缺失值插补和特征提取的深度学习方法。autoCell 是一种变分自动编码器网络，它结合了图嵌入和概率深度高斯混合模型，以推断高维、稀疏 scRNA-seq 数据的分布。我们在模拟数据集和生物学相关的 scRNA-seq 上验证了 autoCell。我们表明，autoCell 的插值提高了现有工具在识别人类植入前胚胎细胞发育轨迹方面的性能。我们确定了与疾病相关的星形胶质细胞 (DAAs)，并使用阿尔茨海默病作为典型示例，重建了涉及细胞间通讯的 DAA 特异性分子网络和配体-受体相互作用。autoCell 为 scRNA-seq 数据的端到端分析提供了一个工具箱，包括可视化、聚类、插补和疾病特异性基因网络识别。
