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

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 due to low counts. Here, we present ASPEN, a statistical method for modeling allelic mean and variance in single-cell transcriptomic data from F1 hybrids. ASPEN combines a sensitive mapping pipeline and adaptive shrinkage to distinguish allelic imbalance and 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, stochastic monoallelic expression, and significant deviations in allelic variance. These results reveal reduced variance in essential pathways and increased variance at neurodevelopmental and immune genes.Author summary: One powerful way to study regulation is to examine hybrids between two inbred species, where each gene has two distinct parental copies, or alleles. Measuring how much each allele is expressed in single cells allows us to detect cis-regulatory differences that are undetectable in bulk data. However, single-cell measurements are very sparse, making it challenging to separate true biological signals from noise. We developed ASPEN, a statistical framework that stabilises these noisy measurements to more reliably estimate allelic expression. ASPEN improves the detection of allelic imbalance compared with existing methods and reveals diverse patterns of allelic use. These include transient activation of one allele, random monoallelic expression, and shifts in allele usage between cell populations. By capturing these forms of variation, ASPEN provides a clearer view of the dynamic regulatory mechanisms that shape gene expression.