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

F. William Townes, Stephanie C. Hicks, Martin J. Aryee, Rafael A. Irizarry

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number295
JournalGenome biology
Volume20
Issue number1
DOIs
StatePublished - Dec 23 2019

Keywords

  • Dimension reduction
  • GLM-PCA
  • Gene expression
  • Principal component analysis
  • RNA-Seq
  • Single cell
  • Variable genes

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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