Academic Journal
ParTIpy: a scalable framework for archetypal analysis and Pareto task inference.
| Τίτλος: | ParTIpy: a scalable framework for archetypal analysis and Pareto task inference. |
|---|---|
| Συγγραφείς: | Schäfer, Philipp Sven Lars, Zimmermann, Leoni, Burmedi, Paul L, Walfisch, Avia, Goldenberg, Noa, Yonassi, Shira, Tamar, Einat Shaer, Adler, Miri, Tanevski, Jovan, Ramirez Flores, Ricardo O, Saez-Rodriguez, Julio |
| Πηγή: | Molecular Systems Biology; Aug2026, Vol. 22 Issue 8, p1252-1269, 18p |
| Θεματικοί όροι: | Archetypes, Gene expression, Python programming language, Resource allocation, Multi-objective optimization, Scalability, Biological databases |
| Περίληψη: | Trade-offs between different tasks are pervasive across scales in biological systems. For example, cells cannot perform all possible functions simultaneously; instead they allocate limited resources to specialize in subsets of tasks by activating specific gene expression programs. Pareto Task Inference (ParTI) is a framework for analyzing biological trade-offs grounded in multi-objective optimality. However, existing software for ParTI neither scales to large datasets nor integrates well with standard data analysis workflows. To address this gap, we developed ParTIpy (https://pypi.org/project/partipy), an open-source Python package that leverages optimization and coreset methods to scale archetypal analysis, the core algorithm underlying ParTI, to millions of cells. By providing tools to characterize archetypes and comprehensive documentation (https://partipy.readthedocs.io), ParTIpy integrates seamlessly into existing analysis workflows, especially for single-cell data. We demonstrate how ParTIpy can be used to study intra-cell-type gene expression variability through the lens of task allocation, offering a principled alternative to methods that impose discrete cell state classifications on inherently continuous variation. Synopsis: ParTIpy is a scalable, open-source Python framework for archetypal analysis that enables Pareto Task Inference at million-cell scale and provides an accessible, interoperable workflow for modeling intra-cell-type gene-expression variability as continuous task allocation rather than discrete cell states. ParTIpy is a highly scalable open-source Python framework for archetypal analysis, enabling Pareto Task Inference at million-cell scale with integrated model selection and stability diagnostics. Comprehensive documentation, tutorials, and scverse interoperability make Pareto Task Inference accessible to the broader single-cell community and beyond. Integrated tooling supports archetype characterization via enrichment analysis, ligand–receptor crosstalk inference, and projection of archetypal programs onto external reference datasets (e.g., spatial data). Establishes a workflow for cross-condition analysis of continuous shifts in cellular task allocation without imposing discrete cell-state boundaries. ParTIpy is a scalable, open-source Python framework for archetypal analysis that enables Pareto Task Inference at million-cell scale and provides an accessible, interoperable workflow for modeling intra-cell-type gene-expression variability as continuous task allocation rather than discrete cell states. [ABSTRACT FROM AUTHOR] |
| Copyright of Molecular Systems Biology is the property of Springer Nature 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.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: ParTIpy: a scalable framework for archetypal analysis and Pareto task inference. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Schäfer%2C+Philipp+Sven+Lars%22">Schäfer, Philipp Sven Lars</searchLink><br /><searchLink fieldCode="AR" term="%22Zimmermann%2C+Leoni%22">Zimmermann, Leoni</searchLink><br /><searchLink fieldCode="AR" term="%22Burmedi%2C+Paul+L%22">Burmedi, Paul L</searchLink><br /><searchLink fieldCode="AR" term="%22Walfisch%2C+Avia%22">Walfisch, Avia</searchLink><br /><searchLink fieldCode="AR" term="%22Goldenberg%2C+Noa%22">Goldenberg, Noa</searchLink><br /><searchLink fieldCode="AR" term="%22Yonassi%2C+Shira%22">Yonassi, Shira</searchLink><br /><searchLink fieldCode="AR" term="%22Tamar%2C+Einat+Shaer%22">Tamar, Einat Shaer</searchLink><br /><searchLink fieldCode="AR" term="%22Adler%2C+Miri%22">Adler, Miri</searchLink><br /><searchLink fieldCode="AR" term="%22Tanevski%2C+Jovan%22">Tanevski, Jovan</searchLink><br /><searchLink fieldCode="AR" term="%22Ramirez+Flores%2C+Ricardo+O%22">Ramirez Flores, Ricardo O</searchLink><br /><searchLink fieldCode="AR" term="%22Saez-Rodriguez%2C+Julio%22">Saez-Rodriguez, Julio</searchLink> – Name: TitleSource Label: Source Group: Src Data: Molecular Systems Biology; Aug2026, Vol. 22 Issue 8, p1252-1269, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Archetypes%22">Archetypes</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+expression%22">Gene expression</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Scalability%22">Scalability</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+databases%22">Biological databases</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Trade-offs between different tasks are pervasive across scales in biological systems. For example, cells cannot perform all possible functions simultaneously; instead they allocate limited resources to specialize in subsets of tasks by activating specific gene expression programs. Pareto Task Inference (ParTI) is a framework for analyzing biological trade-offs grounded in multi-objective optimality. However, existing software for ParTI neither scales to large datasets nor integrates well with standard data analysis workflows. To address this gap, we developed ParTIpy (https://pypi.org/project/partipy), an open-source Python package that leverages optimization and coreset methods to scale archetypal analysis, the core algorithm underlying ParTI, to millions of cells. By providing tools to characterize archetypes and comprehensive documentation (https://partipy.readthedocs.io), ParTIpy integrates seamlessly into existing analysis workflows, especially for single-cell data. We demonstrate how ParTIpy can be used to study intra-cell-type gene expression variability through the lens of task allocation, offering a principled alternative to methods that impose discrete cell state classifications on inherently continuous variation. Synopsis: ParTIpy is a scalable, open-source Python framework for archetypal analysis that enables Pareto Task Inference at million-cell scale and provides an accessible, interoperable workflow for modeling intra-cell-type gene-expression variability as continuous task allocation rather than discrete cell states. ParTIpy is a highly scalable open-source Python framework for archetypal analysis, enabling Pareto Task Inference at million-cell scale with integrated model selection and stability diagnostics. Comprehensive documentation, tutorials, and scverse interoperability make Pareto Task Inference accessible to the broader single-cell community and beyond. Integrated tooling supports archetype characterization via enrichment analysis, ligand–receptor crosstalk inference, and projection of archetypal programs onto external reference datasets (e.g., spatial data). Establishes a workflow for cross-condition analysis of continuous shifts in cellular task allocation without imposing discrete cell-state boundaries. ParTIpy is a scalable, open-source Python framework for archetypal analysis that enables Pareto Task Inference at million-cell scale and provides an accessible, interoperable workflow for modeling intra-cell-type gene-expression variability as continuous task allocation rather than discrete cell states. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Molecular Systems Biology is the property of Springer Nature 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: 10.1038/s44320-026-00209-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1252 Subjects: – SubjectFull: Archetypes Type: general – SubjectFull: Gene expression Type: general – SubjectFull: Python programming language Type: general – SubjectFull: Resource allocation Type: general – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Scalability Type: general – SubjectFull: Biological databases Type: general Titles: – TitleFull: ParTIpy: a scalable framework for archetypal analysis and Pareto task inference. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Schäfer, Philipp Sven Lars – PersonEntity: Name: NameFull: Zimmermann, Leoni – PersonEntity: Name: NameFull: Burmedi, Paul L – PersonEntity: Name: NameFull: Walfisch, Avia – PersonEntity: Name: NameFull: Goldenberg, Noa – PersonEntity: Name: NameFull: Yonassi, Shira – PersonEntity: Name: NameFull: Tamar, Einat Shaer – PersonEntity: Name: NameFull: Adler, Miri – PersonEntity: Name: NameFull: Tanevski, Jovan – PersonEntity: Name: NameFull: Ramirez Flores, Ricardo O – PersonEntity: Name: NameFull: Saez-Rodriguez, Julio IsPartOfRelationships: – BibEntity: Dates: – D: 03 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 17444292 Numbering: – Type: volume Value: 22 – Type: issue Value: 8 Titles: – TitleFull: Molecular Systems Biology Type: main |
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