4.7 Article

BFF and cellhashR: analysis tools for accurate demultiplexing of cell hashing data

Journal

BIOINFORMATICS
Volume 38, Issue 10, Pages 2791-2801

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btac213

Keywords

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Funding

  1. National Institutes of Health [P51 OD011092, 5UM1 AI124377-05, 5U19 AI128741-05]
  2. Bill and Melinda Gates Foundation [OPP1108533/INV-008046]

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This study presents novel demultiplexing algorithms (BFFcluster and BFFraw) that accurately classify droplets in cell hashing. The application of these algorithms improves the quality of single-cell sequencing analysis, demonstrating accuracy and consistency for both well-behaved and poorly behaved input data.
Motivation: Single-cell sequencing methods provide previously impossible resolution into the transcriptome of individual cells. Cell hashing reduces single-cell sequencing costs by increasing capacity on droplet-based platforms. Cell hashing methods rely on demultiplexing algorithms to accurately classify droplets; however, assumptions underlying these algorithms limit accuracy of demultiplexing, ultimately impacting the quality of single-cell sequencing analyses. Results: We present Bimodal Flexible Fitting (BFF) demultiplexing algorithms BFFcluster and BFFraw, a novel class of algorithms that rely on the single inviolable assumption that barcode count distributions are bimodal. We integrated these and other algorithms into cellhashR, a new R package that provides integrated QC and a single command to execute and compare multiple demultiplexing algorithms. We demonstrate that BFFcluster demultiplexing is both tunable and insensitive to issues with poorly behaved data that can confound other algorithms. Using two well-characterized reference datasets, we demonstrate that demultiplexing with BFF algorithms is accurate and consistent for both well-behaved and poorly behaved input data.

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