ParTIpy: A Scalable Framework for Archetypal Analysis and Pareto Task Inference

ABSTRACT: 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 c

SUBMITTER: Philipp, Sven Lars Schäfer 

PROVIDER: S-SCDT-10_1038-S44320-026-00209-6 | biostudies-other |

REPOSITORIES: biostudies-other