Vaeda computationally annotates doublets in single-cell RNA sequencing data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36342203", "kind": "science-record", "revision": 4, "content_hash": "sha256:cdc7ca7a52c3faf1d75e124d44c41307222d10fd3a8c7927911bf5c4165211fa", "created_at": "2026-11-01T18:15:47Z", "updated_at": "2026-11-01T22:08:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Vaeda computationally annotates doublets in single-cell RNA sequencing data.", "doi": "10.1093/bioinformatics/btac720", "pmid": "36342203", "authors": ["Hannah Schriever", "Dennis Kostka"], "journal": "Bioinformatics (Oxford, England)", "year": 2023, "abstract": "MOTIVATION: Single-cell RNA sequencing (scRNA-seq) continues to expand our knowledge by facilitating the study of transcriptional heterogeneity at the level of single cells. Despite this technology's utility and success in biomedical research, technical artifacts are present in scRNA-seq data. Doublets/multiplets are a type of artifact that occurs when two or more cells are tagged by the same barcode, and therefore they appear as a single cell. Because this introduces non-existent transcriptional profiles, doublets can bias and mislead downstream analysis. To address this limitation, computational methods to annotate and remove doublets form scRNA-seq datasets are needed.\nRESULTS: We introduce vaeda (Variational Auto-Encoder for Doublet Annotation), a new approach for computational annotation of doublets in scRNA-seq data. Vaeda integrates a variational auto-encoder and Positive-Unlabeled learning to produce doublet scores and binary doublet calls. We apply vaeda, along with seven existing doublet annotation methods, to 16 benchmark datasets and find that vaeda performs competitively in terms of doublet scores and doublet calls. Notably, vaeda outperforms other python-based methods for doublet annotation. Altogether, vaeda is a robust and competitive method for scRNA-seq doublet annotation and may be of particular interest in the context of python-based workflows.\nAVAILABILITY AND IMPLEMENTATION: Vaeda is available at https://github.com/kostkalab/vaeda, and the version used for the results we present here is archived at zenodo (https://doi.org/10.5281/zenodo.7199783).\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "vaeda is unique in that it combines a cluster-aware variational auto-encoder with a PU-Learning approach.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36342203", "locator": "sections[21] \"4 Discussion\" paragraph 2", "quote": "vaeda is unique in that it combines a cluster-aware variational auto-encoder with a PU-Learning approach."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The benchmarking scRNA-seq datasets are available for download on zenodo from the original authors (https://doi.org/10.5281/zenodo.4062232). 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Code to reproduce results reported in this manuscript in addition to doublet scores and doublet calls produced by each method are available on zenodo (https://doi.org/10.5281/zenodo.7199783)."}], "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:36342203"], "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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