Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to determine expression patterns of thousands of individual cells. However, the analysis of scRNA-seq data remains a computational challenge due to the high technical noise such as the presence of dropout events that lead to a large proportion of zeros for expressed genes. Taking into account the cell heterogeneity and the relationship between dropout rate and expected expression level, we present a cell sub-population based bounded low-rank (PBLR) method to impute the dropouts of scRNA-seq data. Through application to both simulated and real scRNA-seq datasets, PBLR is shown to be effective in recovering dropout events, and it can dramatically improve the low-dimensional representation and the recovery of gene‒gene relationships masked by dropout events compared to several state-of-the-art methods. Moreover, PBLR also detects accurate and robust cell sub-populations automatically, shedding light on its flexibility and generality for scRNA-seq data analysis.

中文翻译：

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通过考虑细胞异质性和 dropout 的先前表达来估算单细胞 RNA-seq 数据

单细胞 RNA 测序 (scRNA-seq) 为确定数千个单个细胞的表达模式提供了强大的工具。然而，scRNA-seq 数据的分析仍然是一个计算挑战，因为技术噪声很高，例如存在导致表达基因大部分为零的丢失事件。考虑到细胞异质性以及丢失率与预期表达水平之间的关系，我们提出了一种基于细胞亚群的有界低秩（PBLR）方法来估算 scRNA-seq 数据的丢失。通过应用于模拟和真实的 scRNA-seq 数据集，PBLR 被证明在恢复丢失事件方面是有效的，与几种最先进的方法相比，它可以显着改善低维表示和被丢失事件掩盖的基因-基因关系的恢复。此外，PBLR 还可以自动检测准确和稳健的细胞亚群，揭示其在 scRNA-seq 数据分析中的灵活性和通用性。