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High-dimensional transcriptomics data, including single-cell and spatially-resolved measurements, enable the study of gene expression at the resolution of individual cells providing critical insights into tissue organization and cellular interactions. Factor models are commonly used to uncover latent biological structure and reduce dimensionality. In this study, we compare two matrix factorization approaches applied to spatial transcriptomics data.

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Latent Structure Modeling in Spatial Transcriptomics Data: Comparing Two Matrix Factorization Approaches

  • 会议论文

  • 发表日期：

    2025年

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  • Alex Cecchetto,
  • Davide Risso

摘要

High-dimensional transcriptomics data, including single-cell and spatially-resolved measurements, enable the study of gene expression at the resolution of individual cells providing critical insights into tissue organization and cellular interactions. Factor models are commonly used to uncover latent biological structure and reduce dimensionality. In this study, we compare two matrix factorization approaches applied to spatial transcriptomics data.