ScLinear predicts protein abundance at single-cell resolution
Abstract Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-024-05958-4 |
| _version_ | 1827326497266860032 |
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| author | Daniel Hanhart Federico Gossi Maria Anna Rapsomaniki Marianna Kruithof-de Julio Panagiotis Chouvardas |
| author_facet | Daniel Hanhart Federico Gossi Maria Anna Rapsomaniki Marianna Kruithof-de Julio Panagiotis Chouvardas |
| author_sort | Daniel Hanhart |
| collection | DOAJ |
| description | Abstract Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy. ScLinear is interpretable and accurately generalizes in unseen single-cell and spatial transcriptomics data. Importantly, we offer a critical view in using complex algorithms ignoring simpler, faster, and more efficient approaches. |
| first_indexed | 2024-03-07T14:45:29Z |
| format | Article |
| id | doaj.art-339131cd997d4828bfb77af969194e71 |
| institution | Directory Open Access Journal |
| issn | 2399-3642 |
| language | English |
| last_indexed | 2024-03-07T14:45:29Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj.art-339131cd997d4828bfb77af969194e712024-03-05T19:59:18ZengNature PortfolioCommunications Biology2399-36422024-03-01711710.1038/s42003-024-05958-4ScLinear predicts protein abundance at single-cell resolutionDaniel Hanhart0Federico Gossi1Maria Anna Rapsomaniki2Marianna Kruithof-de Julio3Panagiotis Chouvardas4Urology Research Laboratory, Department for BioMedical Research, University of BernUrology Research Laboratory, Department for BioMedical Research, University of BernIBM Research EuropeUrology Research Laboratory, Department for BioMedical Research, University of BernUrology Research Laboratory, Department for BioMedical Research, University of BernAbstract Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy. ScLinear is interpretable and accurately generalizes in unseen single-cell and spatial transcriptomics data. Importantly, we offer a critical view in using complex algorithms ignoring simpler, faster, and more efficient approaches.https://doi.org/10.1038/s42003-024-05958-4 |
| spellingShingle | Daniel Hanhart Federico Gossi Maria Anna Rapsomaniki Marianna Kruithof-de Julio Panagiotis Chouvardas ScLinear predicts protein abundance at single-cell resolution Communications Biology |
| title | ScLinear predicts protein abundance at single-cell resolution |
| title_full | ScLinear predicts protein abundance at single-cell resolution |
| title_fullStr | ScLinear predicts protein abundance at single-cell resolution |
| title_full_unstemmed | ScLinear predicts protein abundance at single-cell resolution |
| title_short | ScLinear predicts protein abundance at single-cell resolution |
| title_sort | sclinear predicts protein abundance at single cell resolution |
| url | https://doi.org/10.1038/s42003-024-05958-4 |
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