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Ibex: Variational autoencoder for single-cell BCR sequencing

View ORCID ProfileNicholas Borcherding, Bo Sun, David DeNardo, View ORCID ProfileJonathan R. Brestoff
doi: https://doi.org/10.1101/2022.11.09.515787
Nicholas Borcherding
1Department of Pathology and Immunology, Barnes-Jewish Hospital, Washington University in St Louis, MO 63110, USA
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  • For correspondence: borcherding.n@wustl.edu
Bo Sun
2Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
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David DeNardo
3Department of Medicine, Barnes-Jewish Hospital, Washington University in St Louis, MO 63110, USA
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Jonathan R. Brestoff
1Department of Pathology and Immunology, Barnes-Jewish Hospital, Washington University in St Louis, MO 63110, USA
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  • ORCID record for Jonathan R. Brestoff
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Abstract

Summary B cells are critical for adaptive immunity and are governed by the recognition of an antigen by the B cell receptor (BCR), a process that drives a coordinated series of signaling events and modulation of various transcriptional programs. Single-cell RNA sequencing with paired BCR profiling could offer insights into numerous physiological and pathological processes. However, unlike the plethora of single-cell RNA analysis pipelines, computational tools that utilize single-cell BCR sequences for further analyses are not yet well developed. Here we report Ibex, which vectorizes the amino acid sequence of the complementarity-determining region 3 (cdr3) of the immunoglobulin heavy and light chains, allowing for unbiased dimensional reduction of B cells using their BCR repertoire. Ibex is implemented as an R package with integration into both the Seurat and Single-Cell Experiment framework, enabling the incorporation of this new analytic tool into many single-cell sequencing analytic workflows and multimodal experiments.

Availability and Implementation Ibex is available as an R package at https://github.com/ncborcherding/Ibex. Reproducible code and data for the figure appearing in the manuscript are available at https://github.com/ncborcherding/Ibex.manuscript.

Competing Interest Statement

NB is a consultant for Santa Ana Bio, Inc.

Footnotes

  • ↵* Nicholas Borcherding, MD, PhD, Department of Pathology and Immunology, Division of Laboratory and Genomic Medicine, Washington University School of Medicine, St Louis, MO, 63110, United States, Phone: 314-273-1280, Email: borcherding.n{at}wustl.edu, Twitter: @theHumanBorch

  • https://github.com/ncborcherding/Ibex.manuscript

  • https://github.com/ncborcherding/Ibex

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-NC-ND 4.0 International license.
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Posted November 10, 2022.
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Ibex: Variational autoencoder for single-cell BCR sequencing
Nicholas Borcherding, Bo Sun, David DeNardo, Jonathan R. Brestoff
bioRxiv 2022.11.09.515787; doi: https://doi.org/10.1101/2022.11.09.515787
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Ibex: Variational autoencoder for single-cell BCR sequencing
Nicholas Borcherding, Bo Sun, David DeNardo, Jonathan R. Brestoff
bioRxiv 2022.11.09.515787; doi: https://doi.org/10.1101/2022.11.09.515787

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