Single-cell RNA sequencing (scRNA-seq) is a burgeoning field where experimental techniques and computational methods have been under rapid evolution in the past six years. These technological advances have allowed biomedical researchers to identify new cell types, delineate cell sub-populations, and infer cell differentiation trajectories in various tissue samples. Among the important features extractable from scRNA-seq data, the predominant ones are individual genes’ expression levels in single cells. Most analyses require a preprocessing step that converts a scRNA-seq dataset into a count matrix, where rows correspond to cells (or genes), columns correspond to genes (or cells), and entries are counts, i.e. a count is the number of sequenced reads or uniquely mapped identifiers (UMIs) mapped to a gene in a cell. Single-cell count matrices are highly sparse; for example, a typical matrix constructed from a droplet-based dataset may have >90% of counts as zeros.

中文翻译：

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一种新的生物信息学工具，用于恢复单细胞 RNA 测序数据中缺失的基因表达

单细胞 RNA 测序 (scRNA-seq) 是一个新兴领域，在过去六年中，实验技术和计算方法一直在快速发展。这些技术进步使生物医学研究人员能够识别新的细胞类型，描绘细胞亚群，并推断各种组织样本中的细胞分化轨迹。在可从 scRNA-seq 数据中提取的重要特征中，主要特征是单个基因在单个细胞中的表达水平。大多数分析需要一个预处理步骤，将 scRNA-seq 数据集转换为计数矩阵，其中行对应于细胞（或基因），列对应于基因（或细胞），条目是计数，即计数是测序的数量读取或映射到细胞中基因的唯一映射标识符 (UMI)。单细胞计数矩阵高度稀疏；例如，从基于液滴的数据集构建的典型矩阵可能有 >90% 的计数为零。