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

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35561167", "kind": "science-record", "revision": 3, "content_hash": "sha256:92b01c9e62f36f54ac44048115b5b8b404fc7d609ecbec4195a7b4169d04a6da", "created_at": "2026-11-01T18:14:27Z", "updated_at": "2026-11-01T21:27:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "BFF and cellhashR: analysis tools for accurate demultiplexing of cell hashing data.", "doi": "10.1093/bioinformatics/btac213", "pmid": "35561167", "authors": ["Gregory J Boggy", "G W McElfresh", "Eisa Mahyari", "Abigail B Ventura", "Scott G Hansen", "Louis J Picker", "Benjamin N Bimber"], "journal": "Bioinformatics (Oxford, England)", "year": 2022, "abstract": "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.\nRESULTS: 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.\nAVAILABILITY 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.\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "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.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35561167", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "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."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Supplementary data are available at Bioinformatics online.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35561167", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Supplementary data are available at Bioinformatics online."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35561167"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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