C-S.B.06: TopOmics: Topic Modelling for all -Omics
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Topic models have emerged as a popular paradigm to analyse and interpret complex single-cell and spatial data. Yet, current implementations are usually data-type specific and rely on different modelling and estimation approaches, hindering usability and interoperability. In this work we introduce TopOmics, a library to perform efficient and flexible topic modeling with any combination of -omics data, including novel spatial multi-omic data. The framework leverages standard libraries of the Python ecosystem, guaranteeing seamless integration with existing pipelines, and shows competitive performance against state-of-the-art methods while preserving interpretability. We provide several examples of TopOmics on diverse data sets, demonstrating the usefulness of a unified framework for modern bioinformatic analyses.
Co-authors: Nour El Kazwini, Guido Sanguinetti
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