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

View ORCID ProfileWancen Mu, View ORCID ProfileHirak Sarkar, View ORCID ProfileAvi Srivastava, View ORCID ProfileKwangbom Choi, View ORCID ProfileRob Patro, View ORCID ProfileMichael I. Love
doi: https://doi.org/10.1101/2021.10.15.464546
Wancen Mu
1Department of Biostatistics, University of North Carolina-Chapel Hill
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  • For correspondence: wancenmu@gmail.com
Hirak Sarkar
2Department of Computer Science, University of Maryland, College Park, MD
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Avi Srivastava
3New York Genome Center, New York, NY 10013, USA
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Kwangbom Choi
4The Jackson Laboratory, Bar Harbor, ME 04609, USA
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Rob Patro
2Department of Computer Science, University of Maryland, College Park, MD
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Michael I. Love
1Department of Biostatistics, University of North Carolina-Chapel Hill
5Department of Genetics, University of North Carolina-Chapel Hill
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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 (scRNA-seq) data, or 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 RMSE of allelic ratio estimates than existing methods. In real data, airpart identified differential AI patterns across cell states and could be used to define trends of AI signal over spatial or time axes.

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

Competing Interest Statement

R.P. is a co-founder of Ocean Genomics Inc

Footnotes

  • ↵* michaelisaiahlove{at}gmail.com

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 16, 2021.
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Airpart: Interpretable statistical models for analyzing allelic imbalance in single-cell datasets
Wancen Mu, Hirak Sarkar, Avi Srivastava, Kwangbom Choi, Rob Patro, Michael I. Love
bioRxiv 2021.10.15.464546; doi: https://doi.org/10.1101/2021.10.15.464546
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Airpart: Interpretable statistical models for analyzing allelic imbalance in single-cell datasets
Wancen Mu, Hirak Sarkar, Avi Srivastava, Kwangbom Choi, Rob Patro, Michael I. Love
bioRxiv 2021.10.15.464546; doi: https://doi.org/10.1101/2021.10.15.464546

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