ArticleBioinformatics (Oxford, England)2022
Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 22 citations in OpenAlex.
- FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data.Cell genomics · 2026Article
- Experimental and computational methods for allelic imbalance analysis from single-nucleus RNA-seq data.Genome biology · 2026Article
- A two-phase clustering procedure based on allele specific expression.BMC bioinformatics · 2026Article
- DAESC + : high-performance, integrated software for single-cell allele-specific expression data.BMC bioinformatics · 2026Article
- ASPEN: Robust detection of allelic dynamics in single cell RNA-seq.PLoS computational biology · 2025Article
- ASPEN: Robust detection of allelic dynamics in single cell RNA-seq.bioRxiv : the preprint server for biology · 2025Article
- Computational methods for allele-specific expression in single cells.Trends in genetics : TIG · 2024Review
- Detection of allele-specific expression in spatial transcriptomics with spASE.Genome biology · 2024Article
- Single-cell genomics and regulatory networks for 388 human brains.Science (New York, N.Y.) · 2024Article
- Disentangling sex-dependent effects of APOE on diverse trajectories of cognitive decline in Alzheimer's disease.NeuroImage · 2024Article
- Single-cell genomics and regulatory networks for 388 human brains.bioRxiv : the preprint server for biology · 2024Article
- Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects.Nature communications · 2023Article
- Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
6 authors at 5 institutions in 1 country.
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Abstract
motivationAllelic 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.
resultsWe 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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