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<p>Spatially resolved multimodal data enable the exploration of transcriptional, proteomic and metabolic regulation, yet analytical tools to integrate these spatial omics modalities, particularly spatial metabolomics, remain limited. We developed SpaMTP, an end-to-end framework that implements functions within a common Seurat architecture. It introduces analyses for metabolite annotation, joint clustering, enrichment tests, spatial alignment, multimodal integration, visualization and seamless software interoperability. Its utility is demonstrated across different biological systems.</p>

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SpaMTP: integrative statistical analysis and visualization of spatial metabolomics and transcriptomics data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月31日

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  • Andrew Causer,
  • Tianyao Lu,
  • Jurgen Kriel,
  • Joel J. D. Moffet,
  • Christopher C. J. Fitzgerald,
  • Andrew Newman,
  • Hani Vu,
  • Xiao Tan,
  • Tuan Vo,
  • Cedric Cui,
  • Vinod K. Narayana,
  • James R. Whittle,
  • Sarah A. Best,
  • Saskia Freytag,
  • Quan Nguyen

摘要

Spatially resolved multimodal data enable the exploration of transcriptional, proteomic and metabolic regulation, yet analytical tools to integrate these spatial omics modalities, particularly spatial metabolomics, remain limited. We developed SpaMTP, an end-to-end framework that implements functions within a common Seurat architecture. It introduces analyses for metabolite annotation, joint clustering, enrichment tests, spatial alignment, multimodal integration, visualization and seamless software interoperability. Its utility is demonstrated across different biological systems.