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

Veronika Petrova1,2, Muqing Niu1,2, Thomas Vierbuchen3,4

  • 1Victor Chang Cardiac Research Institute, Sydney 2010, Australia.

Biorxiv : the Preprint Server for Biology
|September 15, 2025
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Summary

We developed ASPEN, a new method for analyzing single-cell RNA sequencing data in F1 hybrids. ASPEN accurately models allelic expression and variance, improving the study of gene regulation and cellular processes.

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) of F1 hybrids offers insights into gene regulation.
  • Technical noise in scRNA-seq limits accurate allelic measurements.
  • Distinguishing true allelic imbalance from noise is challenging.

Purpose of the Study:

  • To introduce ASPEN, a statistical method for modeling allelic mean and variance in scRNA-seq data from F1 hybrids.
  • To improve the analysis of allelic expression and variance in single cells.
  • To enhance the understanding of gene regulatory mechanisms in F1 hybrid systems.

Main Methods:

  • ASPEN employs a sensitive mapping pipeline and adaptive shrinkage techniques.
  • The method models both allelic mean and variance.
  • Simulations were performed using sparse droplet-based scRNA-seq data.

Main Results:

  • ASPEN shows improved sensitivity and false discovery control compared to existing methods.
  • The method successfully identified genes with incomplete X inactivation and stochastic monoallelic expression.
  • Analysis revealed reduced variance in essential cellular pathways and increased variance in neurodevelopmental and immune genes.

Conclusions:

  • ASPEN provides a robust framework for dissecting allelic regulatory phenomena in F1 hybrid scRNA-seq data.
  • The method enhances the ability to detect subtle allelic expression differences and variance.
  • Findings highlight the role of allelic variance in key biological processes, including development and immunity.