Differential expression of single-cell RNA-seq data using Tweedie models

Authors

Himel Mallick, Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, Rahway, New Jersey, USA.
Suvo Chatterjee, Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA.
Shrabanti Chowdhury, Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Saptarshi Chatterjee, Department of Statistics, Data and Analytics, Eli Lilly & Company, Indianapolis, Indianapolis, Indiana, USA.
Ali Rahnavard, Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.
Stephanie C. Hicks, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

Document Type

Journal Article

Publication Date

6-2-2022

Journal

Statistics in medicine

DOI

10.1002/sim.9430

Keywords

Tweedie distribution; differential expression; exponential dispersion model; generalized linear model; single-cell RNA-sequencing; zero-inflation

Abstract

The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse.

APA Citation

Mallick, Himel; Chatterjee, Suvo; Chowdhury, Shrabanti; Chatterjee, Saptarshi; Rahnavard, Ali; and Hicks, Stephanie C., "Differential expression of single-cell RNA-seq data using Tweedie models" (2022). GW Authored Works. Paper 1163.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/1163

Department

Biostatistics and Bioinformatics