TWO-SIGMA-G: A New Competitive Gene Set Testing Framework for scRNA-seq Data Accounting for Inter-Gene and Cell-Cell Correlation

Abstract

We propose TWO-SIGMA-G, a competitive gene set test designed for scRNA-seq data. TWO-SIGMA-G uses the mixed-effects regression modelling approach of our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based approach can analyze complex designs while accommodating zero-inflated and overdispersed counts and within-sample cell-cell correlation. TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the set-level, which can inflate type-I error when ignored. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared to other methods designed for bulk and single-cell RNA-seq data. Application to two real datasets of HIV infection in mice and Alzheimer’s disease progression in humans reveal biologically meaningful results. TWO-SIGMA-G is available at https://github.com/edvanburen/twosigma.