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bioRxiv · 10.64898/2026.05.26.727810

TopOmics: Topic Modelling for All Omics

Sanguinetti, G.·El Kazwini, N.·Caretti, F.
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Abstract

AO_SCPLOWBSTRACTC_SCPLOWTopic 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 at scale. 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, including a novel topic model for spatial multi-omic data, and an analysis of a very large VisiumHD data set.

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Sanguinetti, G., El Kazwini, N., Caretti, F.. 2026-05-29. TopOmics: Topic Modelling for All Omics. https://doi.org/10.64898/2026.05.26.727810

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