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.

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