ASPEN: Robust detection of allelic dynamics in single cell RNA-seq.

Abstract

Single-cell RNA-seq data from F1 hybrids provide a unique framework for dissecting complex regulatory mechanisms, but allelic measurements are limited by technical noise due to low counts. Here, we present ASPEN, a statistical method for modeling allelic mean and variance in single-cell transcriptomic data. ASPEN combines a sensitive mapping pipeline  with a moderated beta-binomial model and adaptive shrinkage to distinguish allelic imbalance and changes to allelic variance in single cells. In both simulated and empirical datasets, ASPEN achieves a ~30% increase in sensitivity over existing approaches for single-cell allelic imbalance detection. Applied to mouse brain organoids and T cells, ASPEN identifies genes with incomplete X inactivation, random monoallelic expression, and significant deviations in allelic variance. These results reveal reduced variance in essential genes, consistent with tight regulatory control, and increased variance at neurodevelopmental and immune loci, indicative of regulatory flexibility.