Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
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DOI：
10.1186/s13059-019-1861-6
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发表时间：
2019-12-23
期刊：
影响因子：
12.3
通讯作者：
Irizarry, Rafael A.
中科院分区：
文献类型：
--
作者：
Townes, F. William;Hicks, Stephanie C.;Irizarry, Rafael A.
单细胞RNA-Seq（scRNA-Seq）分析单个细胞的基因表达。最近的scRNA-Seq数据集已经纳入了独特的分子标识符（UMI）。使用阴性对照，我们显示UMI计数遵循多项式抽样，无零膨胀。目前的标准化程序，如每百万计数的对数和特征选择高度可变的基因产生虚假的变化，在降维。我们提出了简单的多项式方法，包括广义主成分分析（GLM-PCA）的非正态分布，和使用偏差的特征选择。这些方法在使用地面实况数据集的下游聚类评估中优于当前的实践。
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.