Feature Selection and Dimension Reduction for Single Cell RNA-Seq Based on a Multinomial Model

• Activity Number: 351 - Statistical Methods for Single-Cell Genomics
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Frederick William Townes* and Martin Aryee and Stephanie Hicks and Rafael Irizarry
• Companies: Harvard Biostatistics and Massachusetts General Hospital and Johns Hopkins Bloomberg School of Public Health and Harvard University
• Keywords: single cell; RNA-Seq; dimension reduction; gene expression; principal components analysis

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 current practice in a downstream clustering assessment using ground-truth datasets.