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

Document Type

Article

Publication Date

5-13-2022

Publication Title

Bioinformatics (Oxford, England)

Keywords

JMG, Alleles, Allelic Imbalance, Bayes Theorem, Computer Simulation, Models, Statistical, Software

JAX Source

Bioinformatics 2022 May 13; 38(10:2773-2780

Volume

38

Issue

10

First Page

2773

Last Page

2780

ISSN

1367-4811

PMID

35561168

DOI

https://doi.org/10.1093/bioinformatics/btac212

Abstract

MOTIVATION: Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected.

RESULTS: We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower Root Mean Square Error (RMSE) of allelic ratio estimates than existing methods. In real data, airpart identified differential allelic imbalance patterns across cell states and could be used to define trends of AI signal over spatial or time axes.

AVAILABILITY AND IMPLEMENTATION: The airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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