Li Bo, Jin Ke, Ou-Yang Le, Yan Hong, Zhang Xiao-Fei
IEEE/ACM Trans Comput Biol Bioinform. 2023 Mar-Apr;20(2):1445-1456. doi: 10.1109/TCBB.2022.3170587. Epub 2023 Apr 3.
The single-cell RNA sequencing (scRNA-seq) technique begins a new era by revealing gene expression patterns at single-cell resolution, enabling studies of heterogeneity and transcriptome dynamics of complex tissues at single-cell resolution. However, existing large proportion of dropout events may hinder downstream analyses. Thus imputation of dropout events is an important step in analyzing scRNA-seq data. We develop scTSSR2, a new imputation method that combines matrix decomposition with the previously developed two-side sparse self-representation, leading to fast two-side sparse self-representation to impute dropout events in scRNA-seq data. The comparisons of computational speed and memory usage among different imputation methods show that scTSSR2 has distinct advantages in terms of computational speed and memory usage. Comprehensive downstream experiments show that scTSSR2 outperforms the state-of-the-art imputation methods. A user-friendly R package scTSSR2 is developed to denoise the scRNA-seq data to improve the data quality.
单细胞RNA测序（scRNA-seq）技术通过在单细胞分辨率下揭示基因表达模式，开启了一个新时代，使得在单细胞分辨率下研究复杂组织的异质性和转录组动态成为可能。然而，现有的大量缺失事件可能会阻碍下游分析。因此，缺失事件的插补是分析scRNA-seq数据的重要一步。我们开发了scTSSR2，这是一种新的插补方法，它将矩阵分解与先前开发的双边稀疏自表示相结合，从而实现快速双边稀疏自表示，以插补scRNA-seq数据中的缺失事件。不同插补方法之间的计算速度和内存使用情况比较表明，scTSSR2在计算速度和内存使用方面具有明显优势。全面的下游实验表明，scTSSR2优于现有最先进的插补方法。我们开发了一个用户友好的R包scTSSR2，用于对scRNA-seq数据进行去噪，以提高数据质量。
