Exploring and mitigating shortcomings in single-cell differential expression analysis with a new statistical paradigm

Differential expression analysis is pivotal in single-cell transcriptomics for unraveling cell-type-specific responses to stimuli. While numerous methods are available to identify differentially expressed genes in single-cell data, recent evaluations of both single-cell-specific methods and methods adapted from bulk studies have revealed significant shortcomings in performance. In this paper, we dissect the four major challenges in single-cell differential expression analysis: excessive zeros, normalization, donor effects, and cumulative biases. These "curses" underscore the limitations and conceptual pitfalls in existing workflows.

To address the limitations of current single-cell differential expression analysis methods, we propose GLIMES, a statistical framework that leverages UMI counts and zero proportions within a generalized Poisson/Binomial mixed-effects model to account for batch effects and within-sample variation. We rigorously benchmarked GLIMES against six existing differential expression methods using three case studies and simulations across different experimental scenarios, including comparisons across cell types, tissue regions, and cell states. Our results demonstrate that GLIMES is more adaptable to diverse experimental designs in single-cell studies and effectively mitigates key shortcomings of current approaches, particularly those related to normalization procedures. By preserving biologically meaningful signals, GLIMES offers improved performance in detecting differentially expressed genes.

By using absolute RNA expression rather than relative abundance, GLIMES improves sensitivity, reduces false discoveries, and enhances biological interpretability. This paradigm shift challenges existing workflows and highlights the need for careful consideration of normalization strategies, ultimately paving the way for more accurate and robust single-cell transcriptomic analyses.