Academic Journal
Vaeda computationally annotates doublets in single-cell RNA sequencing data.
| Title: | Vaeda computationally annotates doublets in single-cell RNA sequencing data. |
|---|---|
| Authors: | Schriever, Hannah, Kostka, Dennis |
| Source: | Bioinformatics; Jan2023, Vol. 39 Issue 1, p1-8, 8p |
| Subject Terms: | Protein-protein interactions, Medical research |
| 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. Results 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. Availability 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). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR] |
| Copyright of Bioinformatics is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 161116741 RelevancyScore: 926 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 926.038452148438 |
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| Items | – Name: Title Label: Title Group: Ti Data: Vaeda computationally annotates doublets in single-cell RNA sequencing data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Schriever%2C+Hannah%22">Schriever, Hannah</searchLink><br /><searchLink fieldCode="AR" term="%22Kostka%2C+Dennis%22">Kostka, Dennis</searchLink> – Name: TitleSource Label: Source Group: Src Data: Bioinformatics; Jan2023, Vol. 39 Issue 1, p1-8, 8p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Protein-protein+interactions%22">Protein-protein interactions</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+research%22">Medical research</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. Results 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. Availability 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). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Bioinformatics is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/bioinformatics/btac720 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 1 Subjects: – SubjectFull: Protein-protein interactions Type: general – SubjectFull: Medical research Type: general Titles: – TitleFull: Vaeda computationally annotates doublets in single-cell RNA sequencing data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Schriever, Hannah – PersonEntity: Name: NameFull: Kostka, Dennis IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 13674803 Numbering: – Type: volume Value: 39 – Type: issue Value: 1 Titles: – TitleFull: Bioinformatics Type: main |
| ResultId | 1 |