TWO-SIGMA-G: a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell–cell correlation

Author:

Van Buren Eric1,Hu Ming2,Cheng Liang345,Wrobel John3,Wilhelmsen Kirk6,Su Lishan347,Li Yun8910,Wu Di810

Affiliation:

1. Department of Biostatistics , Harvard T.H. Chan School of Public Health

2. Department of Quantitative Health Sciences , Lerner Research Institute, Cleveland Clinic Foundation

3. Lineberger Comprehensive Cancer Center , The University of North Carolina at Chapel Hill

4. Department of Microbiology and Immunology , The University of North Carolina at Chapel Hill

5. Frontier Science Center for Immunology and Metabolism , Medical Research Institute, Wuhan University

6. Departments of Genetics and Neurology , Renaissance Computing Institute, University of North Carolina at Chapel Hill

7. Departments of Pharmacology , Microbiology & Immunology University of Maryland School of Medicine

8. Department of Biostatistics , The University of North Carolina at Chapel Hill

9. Department of Genetics , The University of North Carolina at Chapel Hill

10. Department of Computer Science , The University of North Carolina at Chapel Hill

Abstract

Abstract We propose TWO-SIGMA-G, a competitive gene set test for scRNA-seq data. TWO-SIGMA-G uses a mixed-effects regression model based on our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based model provides flexibility and rigor at the gene-level in (1) handling complex experimental designs, (2) accounting for the correlation between biological replicates and (3) accommodating the distribution of scRNA-seq data to improve statistical inference. Moreover, TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the set-level to control the set-level false positive rate. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared with other methods. Application to two datasets identified HIV-associated interferon pathways in xenograft mice and pathways associated with Alzheimer’s disease progression in humans.

Funder

National Institutes of Health

University of North Carolina Computational Medicine Program Award 2020

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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