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Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model.

ABSTRACT: 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.

SUBMITTER: Townes FW

PROVIDER: S-EPMC6927135 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model.

Townes F William FW Hicks Stephanie C SC Aryee Martin J MJ Irizarry Rafael A RA

Genome biology 20191223 1

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 ...[more]

PMID: 31870412

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