School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
Cell Rep Methods. 2021 Dec 20;2(1):100133. doi: 10.1016/j.crmeth.2021.100133. eCollection 2022 Jan 24.
Single-cell RNA sequencing (scRNA-seq) offers opportunities to study gene expression of tens of thousands of single cells simultaneously, to investigate cell-to-cell variation, and to reconstruct cell-type-specific gene regulatory networks. Recovering dropout events in a sparse gene expression matrix for scRNA-seq data is a long-standing matrix completion problem. In this article, we introduce Bfimpute, a Bayesian factorization imputation algorithm that reconstructs two latent gene and cell matrices to impute the final gene expression matrix within each cell group, with or without the aid of cell type labels or bulk data. Bfimpute achieves better accuracy than ten other publicly notable scRNA-seq imputation methods on simulated and real scRNA-seq data, as measured by several different evaluation metrics. Bfimpute can also flexibly integrate any gene- or cell-related information that users provide to increase performance.
单细胞 RNA 测序 (scRNA-seq) 提供了同时研究成千上万单个细胞基因表达、调查细胞间变异和重建细胞类型特异性基因调控网络的机会。在 scRNA-seq 数据的稀疏基因表达矩阵中恢复缺失值是一个长期存在的矩阵补全问题。在本文中，我们介绍了 Bfimpute，这是一种贝叶斯分解推断算法，它重建两个潜在的基因和细胞矩阵，以在每个细胞组内推断最终的基因表达矩阵，无论是否有细胞类型标签或批量数据的帮助。Bfimpute 在模拟和真实 scRNA-seq 数据上的表现优于其他十种著名的 scRNA-seq 推断方法，这是通过几种不同的评估指标衡量的。Bfimpute 还可以灵活地集成用户提供的任何与基因或细胞相关的信息，以提高性能。
