🤖 AI Summary
Conventional single-cell RNA-seq (scRNA-seq) analyses relying solely on summary statistics (e.g., mean, variance) suffer from low sensitivity in detecting expression distribution differences and poor biological interpretability. Method: We propose a model-agnostic, tailored exponential family (SEF) density modeling framework that enables nonparametric estimation, visualization, and rigorous hypothesis testing of gene expression probability densities—both at the individual-cell and population levels—without assuming a specific parametric distribution. Leveraging asymptotically consistent covariance estimation and SEF-based statistical inference, our approach improves error control and statistical power. Contribution/Results: In comprehensive simulations across diverse scenarios, our method outperforms existing approaches. Applied to systemic lupus erythematosus (SLE) scRNA-seq data, it successfully identifies critical differentially expressed genes and functionally coherent gene sets missed by pseudo-bulk methods, establishing a novel paradigm for comparing single-cell expression distributions.
📝 Abstract
Recent advances in high-resolution sequencing have paved the way for population-scale analysis in single-cell RNA-sequencing (scRNA-seq) data. scRNA-seq data, in particular, have proven to be extremely powerful in profiling a variety of outcomes such as disease and aging. The abundance of scRNA-seq data makes it possible to model each individual's gene expression as a probability density across cells, offering a richer representation than summary statistics such as means or variances, and allowing for more nuanced group comparisons. To this end, we propose a model-agnostic framework for density estimation and inference based on specially designed exponential families~(SEF), which accommodates diverse underlying models without requiring prior specifications. The proposed method enables estimation and visualization for both individual-specific and group-level gene expression densities, as well as conducting formal hypothesis testing for expression density difference across groups of interest. It relies on relaxed assumptions with established asymptotic properties and a consistent covariance estimator for valid inference. Through simulation under various scenarios, the SEF-based approach demonstrates good error control and improved statistical power over competing methods,including pseudo-bulk tests and moment estimators. Application to a population-scale scRNA-seq dataset from patients with systemic lupus erythematosus identified genes and gene sets that are missed from pseudo-bulk based tests.