ImageDoubler: image-based doublet identification in single-cell sequencing
Abstract Single-cell sequencing provides detailed insights into individual cell behaviors within complex systems based on the assumption that each cell is uniquely isolated. However, doublets—where two or more cells are sequenced together—disrupt this assumption and can lead to potential data misint...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55434-0 |
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| _version_ | 1850282068341161984 |
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| author | Kaiwen Deng Xinya Xu Manqi Zhou Hongyang Li Evan T. Keller Gregory Shelley Annie Lu Lana Garmire Yuanfang Guan |
| author_facet | Kaiwen Deng Xinya Xu Manqi Zhou Hongyang Li Evan T. Keller Gregory Shelley Annie Lu Lana Garmire Yuanfang Guan |
| author_sort | Kaiwen Deng |
| collection | DOAJ |
| description | Abstract Single-cell sequencing provides detailed insights into individual cell behaviors within complex systems based on the assumption that each cell is uniquely isolated. However, doublets—where two or more cells are sequenced together—disrupt this assumption and can lead to potential data misinterpretations. Traditional doublet detection methods primarily rely on simulated genomic data, which may be less effective in homogeneous cell populations and can introduce biases from experimental processes. Therefore, we introduce ImageDoubler in this study, an innovative image-based model that identifies doublets and missing samples leveraging the Fluidigm single-cell sequencing image data. Our approach showcases a notable doublet detection efficacy, achieving a rate up to 93.87% and registering a minimum improvement of 33.1% in F1 scores compared to existing genomic-based methods. This advancement highlights the potential of using imaging to glean insight into developing doublet detection algorithms and exposes the limitations inherent in current genomic-based techniques. |
| format | Article |
| id | doaj-art-349c4a728da94fcfa01f47259af5efc7 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-349c4a728da94fcfa01f47259af5efc72025-08-20T01:48:07ZengNature PortfolioNature Communications2041-17232025-01-0116111410.1038/s41467-024-55434-0ImageDoubler: image-based doublet identification in single-cell sequencingKaiwen Deng0Xinya Xu1Manqi Zhou2Hongyang Li3Evan T. Keller4Gregory Shelley5Annie Lu6Lana Garmire7Yuanfang Guan8Gilbert S. Omenn Department of Computational Medicine & Bioinformatics, University of MichiganCollege of Literature, Science, and the Arts, University of MichiganGilbert S. Omenn Department of Computational Medicine & Bioinformatics, University of MichiganGilbert S. Omenn Department of Computational Medicine & Bioinformatics, University of MichiganDepartment of Urology, University of MichiganDepartment of Urology, University of MichiganGilbert S. Omenn Department of Computational Medicine & Bioinformatics, University of MichiganGilbert S. Omenn Department of Computational Medicine & Bioinformatics, University of MichiganGilbert S. Omenn Department of Computational Medicine & Bioinformatics, University of MichiganAbstract Single-cell sequencing provides detailed insights into individual cell behaviors within complex systems based on the assumption that each cell is uniquely isolated. However, doublets—where two or more cells are sequenced together—disrupt this assumption and can lead to potential data misinterpretations. Traditional doublet detection methods primarily rely on simulated genomic data, which may be less effective in homogeneous cell populations and can introduce biases from experimental processes. Therefore, we introduce ImageDoubler in this study, an innovative image-based model that identifies doublets and missing samples leveraging the Fluidigm single-cell sequencing image data. Our approach showcases a notable doublet detection efficacy, achieving a rate up to 93.87% and registering a minimum improvement of 33.1% in F1 scores compared to existing genomic-based methods. This advancement highlights the potential of using imaging to glean insight into developing doublet detection algorithms and exposes the limitations inherent in current genomic-based techniques.https://doi.org/10.1038/s41467-024-55434-0 |
| spellingShingle | Kaiwen Deng Xinya Xu Manqi Zhou Hongyang Li Evan T. Keller Gregory Shelley Annie Lu Lana Garmire Yuanfang Guan ImageDoubler: image-based doublet identification in single-cell sequencing Nature Communications |
| title | ImageDoubler: image-based doublet identification in single-cell sequencing |
| title_full | ImageDoubler: image-based doublet identification in single-cell sequencing |
| title_fullStr | ImageDoubler: image-based doublet identification in single-cell sequencing |
| title_full_unstemmed | ImageDoubler: image-based doublet identification in single-cell sequencing |
| title_short | ImageDoubler: image-based doublet identification in single-cell sequencing |
| title_sort | imagedoubler image based doublet identification in single cell sequencing |
| url | https://doi.org/10.1038/s41467-024-55434-0 |
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