Huang Meng, Ye Xiucai, Li Hongmin, Sakurai Tetsuya
Department of Computer Science, University of Tsukuba, Tsukuba, Japan.
Center for Artificial Intelligence Research, University of Tsukuba, Tsukuba, Japan.
Front Genet. 2022 Jul 14;13:952649. doi: 10.3389/fgene.2022.952649. eCollection 2022.
Single-cell RNA-sequencing (scRNA-seq) technologies enable the measurements of gene expressions in individual cells, which is helpful for exploring cancer heterogeneity and precision medicine. However, various technical noises lead to false zero values (missing gene expression values) in scRNA-seq data, termed as dropout events. These zero values complicate the analysis of cell patterns, which affects the high-precision analysis of intra-tumor heterogeneity. Recovering missing gene expression values is still a major obstacle in the scRNA-seq data analysis. In this study, taking the cell heterogeneity into consideration, we develop a novel method, called single cell Gauss-Newton Gene expression Imputation (scGNGI), to impute the scRNA-seq expression matrices by using a low-rank matrix completion. The obtained experimental results on the simulated datasets and real scRNA-seq datasets show that scGNGI can more effectively impute the missing values for scRNA-seq gene expression and improve the down-stream analysis compared to other state-of-the-art methods. Moreover, we show that the proposed method can better preserve gene expression variability among cells. Overall, this study helps explore the complex biological system and precision medicine in scRNA-seq data.
单细胞RNA测序（scRNA-seq）技术能够测量单个细胞中的基因表达，这有助于探索癌症异质性和精准医学。然而，各种技术噪声会导致scRNA-seq数据中出现假零值（缺失基因表达值），即所谓的脱落事件。这些零值使细胞模式分析变得复杂，影响了肿瘤内异质性的高精度分析。恢复缺失的基因表达值仍然是scRNA-seq数据分析中的一个主要障碍。在本研究中，考虑到细胞异质性，我们开发了一种名为单细胞高斯-牛顿基因表达插补（scGNGI）的新方法，通过低秩矩阵补全来插补scRNA-seq表达矩阵。在模拟数据集和真实scRNA-seq数据集上获得的实验结果表明，与其他现有方法相比，scGNGI能够更有效地插补scRNA-seq基因表达的缺失值，并改善下游分析。此外，我们表明所提出的方法能够更好地保留细胞间的基因表达变异性。总体而言，本研究有助于探索scRNA-seq数据中的复杂生物系统和精准医学。
