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Showing 1–2 of 2 results
Advanced filters: Author: Shobana V. Stassen Clear advanced filters

  • The authors present MorphoGenie, an unsupervised model that profiles cell shapes to predict cellular heterogeneity without manual labels. It provides a scalable, interpretable, and generalizable approach for data-driven exploration of cellular heterogeneity across diverse imaging modalities.

    • Rashmi Sreeramachandra Murthy
    • Shobana V. Stassen
    • Kevin K. Tsia
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-20

  • Scalable trajectory inference for multi-omic single cell datasets is challenging in terms of capturing non-tree complex topologies. Here the authors present a method, VIA, that scales to millions of cells across multiple omic modalities using lazy-teleporting random walks.

    • Shobana V. Stassen
    • Gwinky G. K. Yip
    • Kevin K. Tsia
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-18