PDF(Pubmed)
Abstract：
Single-cell RNA sequencing (scRNA-seq) is a robust method for studying gene expression at the single-cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution-agnostic deep learning model for accurately recovering missing sing-cell gene expression from multiple platforms. Bis is an optimal transport-based autoencoder model that can capture the intricate distribution of scRNA-seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA-seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor-matched peripheral blood dataset, revealing developmental characteristics of cytokine-induced natural killer cells within a head and neck squamous cell carcinoma microenvironment.
摘要：
单细胞RNA测序（scRNA-seq）是一种在单细胞水平上研究基因表达的强大方法。但是精确定量遗传物质通常受到有限的mRNA捕获的阻碍，导致许多缺少表达式值。现有的插补方法依赖于严格的数据假设，限制了它们更广泛的应用，缺乏可靠的监督，导致信号恢复有偏差。为了应对这些挑战，作者开发了Bis，一种分布不可知的深度学习模型，用于从多个平台准确恢复缺失的sing-cell基因表达。Bis是一种基于传输的最佳自动编码器模型，可以捕获scRNA-seq数据的复杂分布，同时通过正则化细胞嵌入空间来解决特征稀疏性。此外,他们提出了一个使用大量RNA-seq数据来指导重建并确保表达一致性的模块。实验结果表明，Bis在模拟和真实数据集上的表现优于其他模型，在各种下游分析中展示优势，包括批量效应去除，聚类，差异表达分析,和轨迹推断。此外，Bis成功恢复了肿瘤匹配外周血数据集中稀有细胞亚群的基因表达水平，揭示了头颈部鳞状细胞癌微环境中细胞因子诱导的自然杀伤细胞的发育特征。
