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bioRxiv · 10.1101/2025.04.16.649227

Studying cis-regulatory heterogeneity in single-cells at allelic resolution

Petrova, V.·Niu, M.·Vierbuchen, T.·Wong, E. S.
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Abstract

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.

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Petrova, V., Niu, M., Vierbuchen, T., Wong, E. S.. 2025-04-17. Studying cis-regulatory heterogeneity in single-cells at allelic resolution. https://doi.org/10.1101/2025.04.16.649227

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