Petrova Veronika, Niu Muqing, Vierbuchen Thomas, Wong Emily S
Victor Chang Cardiac Research Institute, Sydney 2010, Australia.
School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney 2033, Australia.
bioRxiv. 2025 Sep 3:2025.04.16.649227. doi: 10.1101/2025.04.16.649227.
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.
来自F1杂交种的单细胞RNA测序数据为剖析复杂的调控现象提供了一个独特的框架，但等位基因测量受到技术噪声的限制。在这里，我们介绍了ASPEN，一种用于对F1杂交种单细胞转录组数据中等位基因均值和方差进行建模的统计方法。ASPEN使用敏感的映射流程和自适应收缩来区分单细胞中的等位基因失衡和方差。通过基于稀疏液滴单细胞数据的广泛模拟，与现有方法相比，ASPEN在灵敏度和错误发现控制方面表现出改进。应用于小鼠脑类器官和T细胞时，ASPEN识别出具有不完全X染色体失活、随机单等位基因表达以及等位基因方差显著偏差的基因。这揭示了基本细胞通路中方差的降低，以及神经发育和免疫特异性基因中方差的增加。
