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spammR: an R package designed for analysis and integration of spatial multi-omic measurements.

Yannick Mahlich1, Harkirat Sohi1, Paul Piehowski2

  • 1Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.

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Summary

A new R package, spammR, offers tools for spatial omics data analysis, particularly for mass spectrometry. It addresses challenges like small sample sizes and missing data, enabling data-driven insights from spatial multi-omics measurements.

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Area of Science:

  • Spatial omics and multi-omics integration
  • Computational biology and bioinformatics
  • Mass spectrometry-based omics analysis

Background:

  • Spatial omics technologies rapidly advance, enabling high-resolution measurement of molecular data.
  • Current analytical tools primarily support spatial transcriptomics, limiting applications for other omics types.
  • Mass spectrometry-based spatial omics datasets present unique challenges, including small sample sizes, spatial sparsity, and missing data.

Purpose of the Study:

  • To introduce spammR, an R package for the end-to-end analysis of spatial omics data.
  • To specifically address the analytical challenges of mass spectrometry-derived spatial omics datasets.
  • To provide a data-driven approach for analyzing spatial multi-omics measurements without prior knowledge of specific genes or proteins.

Main Methods:

  • Development of the spammR R package for spatial omics data analysis.
  • Implementation of methods tailored for small sample sizes, spatial sparsity, and missing data common in mass spectrometry.
  • Focus on a fully data-driven analytical framework.

Main Results:

  • spammR enables comprehensive analysis of spatial omics data, particularly from mass spectrometry.
  • The package effectively handles datasets with limited samples and significant missing values.
  • It facilitates a data-driven exploration of spatial multi-omics patterns.

Conclusions:

  • spammR provides a valuable computational tool for advancing mass spectrometry-based spatial omics research.
  • The package democratizes the analysis of challenging spatial multi-omics datasets.
  • It supports novel discoveries by enabling robust, data-driven spatial analysis.