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Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data

By Sean Simmons, Xian Adiconis, Nathan Haywood, Jacob Parker, Zechuan Lin, Idil Tuncali, Zhixiang Liao, Aziz M. Al'Khafaji, Asa Shin, Karthik Jagadeesh, Kirk Gosik, Michael Gatzen, Jonathan T. Smith, Daniel El Kodsi, PhD, Yuliya Kuras, PhD, Clare Baecher-Allan, Geidy Serrano, PhD, Thomas Beach, Kiran Garimella, Orit Rozenblatt- Rosen, Aviv Regev, Xianjun Dong, PhD, Clemens Scherzer, MD, Joshua Levin, PhD
Published August 19, 2024
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Output Details

Preprint August 19, 2024

Published August 19, 2024

Description
Combining allele-specific expression (ASE) analysis with single-cell RNA-seq can elucidate how genomic variation affects RNA expression at the single-cell level. We explore how experimental and computational choices impact the power of ASE-based methods and develop a suite of single-cell ASE computational tools. With single-nucleus RNA-Seq, we extract more ASE information from reads in intronic than exonic regions. We show how read length can increase power and that hybrid selection improves power to detect ASE in targeted genes. We apply our methods to a Parkinson’s disease cohort and show that ASE analysis has more power than eQTL analysis.
Identifier (DOI)
10.1186/s13059-026-04062-6
Tags
  • Original Research
Discovery Team
Team Scherzer
Program
Collaborative Research Network
Theme
PD Functional Genomics

Meet the Authors

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scASE_py: Python pacakge for loading single cell allele specific data

Python package for single cell allele-specific expression (ASE) output manipulation available at https://github.com/seanken/ASE_pipeline. R package for downstream analysis at https://github.com/seanken/scAlleleExpression.

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Aligning Science Across Parkinson's
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