Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.

中文翻译：

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没有唯一分子标识符的单细胞 RNA-seq 读数计数的分位数标准化


单细胞 RNA-seq (scRNA-seq) 分析单个细胞的基因表达。独特的分子标识符 (UMI) 可消除聚合酶链式反应产生的重复读数，而聚合酶链反应是主要的噪音来源。对于缺乏 UMI 的 scRNA-seq 数据，我们提出了准 UMI：将读数计数标准化为从 UMI 数据集经验得出的复合泊松分布。当应用于同时具有读数和 UMI 的真实数据集时，准 UMI 标准化比竞争方法具有更高的准确性。使用准 UMI 可以将专为 UMI 数据设计的方法应用于非 UMI scRNA-seq 数据集。