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. AVAILABILITY AND IMPLEMENTATION: cellhashR is available as an R package at https://github.com/BimberLab/cellhashR. cellhashR version 1.0.3 was used for the analyses in this manuscript and is archived on Zenodo at https://www.doi.org/10.5281/zenodo.6402477. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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. AVAILABILITY AND IMPLEMENTATION: cellhashR is available as an R package at https://github.com/BimberLab/cellhashR. cellhashR version 1.0.3 was used for the analyses in this manuscript and is archived on Zenodo at https://www.doi.org/10.5281/zenodo.6402477. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Jellert T Gaublomme; Bo Li; Cristin McCabe; Abigail Knecht; Yiming Yang; Eugene Drokhlyansky; Nicholas Van Wittenberghe; Julia Waldman; Danielle Dionne; Lan Nguyen; Philip L De Jager; Bertrand Yeung; Xinfang Zhao; Naomi Habib; Orit Rozenblatt-Rosen; Aviv Regev Journal: Nat Commun Date: 2019-07-02 Impact factor: 14.919
Authors: Jason A Carter; Jonathan B Preall; Kristina Grigaityte; Stephen J Goldfless; Eric Jeffery; Adrian W Briggs; Francois Vigneault; Gurinder S Atwal Journal: Front Immunol Date: 2019-07-31 Impact factor: 7.561
Authors: Tomislav Ilicic; Jong Kyoung Kim; Aleksandra A Kolodziejczyk; Frederik Otzen Bagger; Davis James McCarthy; John C Marioni; Sarah A Teichmann Journal: Genome Biol Date: 2016-02-17 Impact factor: 13.583