2025/04/17 by Veronika Petrova, Muqing Niu, Thomas Vierbuchen +1 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Single-cell and spatial transcriptomics #Gene expression and cancer classification #Gene Regulatory Network Analysis
paper · pdf · doi:10.1101/2025.04.16.649227
openalex publication_date 2025/04/17 · openalex created_date 2025/04/24 · openalex updated_date 2026/07/31
Single-cell RNA-seq data from F1 hybrids provides a unique framework for dissecting complex regulatory phenomena, but allelic measurements are limited by technical noise. Here, we present ASPEN, a statistical method for modeling allelic mean and variance in single-cell transcriptomic data from F1 hybrids. ASPEN uses a sensitive mapping pipeline and adaptive shrinkage to distinguish allelic imbalance and variance in single cells. Through extensive simulation based on sparse droplet-based single-cell data, ASPEN demonstrates improved sensitivity and control of false discoveries compared to existing approaches. Applied to mouse brain organoids and T cells, ASPEN identifies genes with incomplete X inactivation, stochastic monoallelic expression, and significant deviations in allelic variance. This reveals reduced variance in essential cellular pathways, and increased variance in neurodevelopmental and immune-specific genes.