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. |
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| 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 |
| 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] |
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| Βάση Δεδομένων: | Complementary Index |
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