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Updated: Feb 7, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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MANTIS: Analytics toolkit for spatial metabolomics with matching spatial transcriptomics data.

Yu Hao1, Yeojin Kim1, Bhavay Aggarwal1

  • 1The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

Biorxiv : the Preprint Server for Biology
|February 6, 2026
PubMed
Summary
This summary is machine-generated.

MANTIS is a new tool that analyzes spatial metabolomics and spatial transcriptomics data together. It reveals metabolite patterns and gene-metabolite links, improving our understanding of tissue function.

Keywords:
gene-metabolite associationspatial metabolomicsspatial statisticsspatial transcriptomics

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Area of Science:

  • Multi-omics spatial analysis
  • Computational biology
  • Biotechnology

Background:

  • Spatial Metabolomics (SM) and Spatial Transcriptomics (ST) are powerful technologies for analyzing tissue function.
  • Integrating SM and ST data enhances understanding of metabolic and gene expression patterns.
  • Current tools offer limited capabilities for exploring relationships between SM and ST data.

Purpose of the Study:

  • To introduce MANTIS, a computational tool for analyzing paired SM and ST data.
  • To discover metabolite spatial distribution patterns and gene-metabolite relationships.
  • To provide a unified framework for spatial omics data analysis with rigorous statistical testing.

Main Methods:

  • MANTIS analyzes aligned SM+ST profiles, optionally with spatial domain or cell type information.
  • It employs specialized permutation tests to assess statistical significance, controlling for spatial autocorrelation.
  • Utilizes spatial cross-correlation and partial correlation statistics for quantifying gene-metabolite associations.

Main Results:

  • MANTIS successfully identified metabolite spatial patterns and gene-metabolite relationships across diverse datasets.
  • The tool demonstrated superior specificity in detecting patterns and associations compared to existing methods.
  • MANTIS effectively disentangles various sources contributing to spatial patterns and correlations.

Conclusions:

  • MANTIS is the first toolkit to integrate spatial metabolomics, spatial transcriptomics, cell type, and spatial domain information.
  • It provides a robust framework for hypothesis testing in spatial omics research.
  • The tool enhances the discovery of biological insights from multi-omics spatial data.