Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.

中文翻译：

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基于多项式模型的单细胞RNA-Seq特征选择和降维

单细胞 RNA 测序 (scRNA-Seq) 分析单个细胞的基因表达。最近的 scRNA-Seq 数据集已纳入独特的分子标识符 (UMI)。使用负控制，我们显示 UMI 计数遵循多项抽样，不存在零通胀。当前的标准化程序（例如每百万计数的对数和高度可变基因的特征选择）会在降维中产生错误的变异性。我们提出了简单的多项式方法，包括非正态分布的广义主成分分析（GLM-PCA）以及使用偏差的特征选择。这些方法优于使用地面实况数据集进行下游聚类评估的当前实践。