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Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets.

Wancen Mu1, Hirak Sarkar2, Avi Srivastava3

  • 1Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA.

Bioinformatics (Oxford, England)
|May 13, 2022
PubMed
Summary

We developed airpart, a new method to detect cell-type-specific allelic imbalance (AI) in single-cell RNA sequencing data. This approach identifies cis-regulatory mechanisms by analyzing AI patterns across different cell states and time or spatial resolutions.

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Area of Science:

  • Genomics
  • Computational Biology
  • Statistical Genetics

Background:

  • Allelic expression analysis reveals cis-regulatory mechanisms driving genetic variation.
  • Measuring allelic imbalance (AI) in bulk data can obscure cell-type-specific (CTS), spatial, or temporal AI signals due to lack of resolution.

Purpose of the Study:

  • To introduce a statistical method, airpart, for identifying differential CTS AI from single-cell RNA-sequencing (scRNA-seq) data.
  • To enable the detection of dynamic AI patterns in spatially or time-resolved datasets.

Main Methods:

  • airpart employs a Generalized Fused Lasso with Binomial likelihood to partition cells by AI signal, accounting for low counts in scRNA-seq data.
  • A hierarchical Bayesian model is utilized for AI statistical inference.
  • The method outputs discrete data partitions, highlighting groups of genes and cells under common cis-genetic regulatory mechanisms.

Main Results:

  • Simulations demonstrated airpart's accuracy in detecting cell type partitions by AI and its lower Root Mean Square Error (RMSE) for allelic ratio estimates compared to existing methods.
  • Real data analysis revealed differential AI patterns across cell states.
  • airpart successfully defined trends of AI signal over spatial or temporal axes.

Conclusions:

  • airpart provides a robust statistical framework for analyzing AI in high-resolution datasets.
  • The method facilitates the discovery of cis-regulatory mechanisms by dissecting cell-type-specific and dynamic AI.
  • airpart is available as an R/Bioconductor package.