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MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance.

Seyednami Niyakan1, Jianting Sheng2, Yuliang Cao2

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

Patterns (New York, N.Y.)
|May 27, 2024
PubMed
Summary

We developed MUSTANG, a spatial transcriptomics analysis framework for multi-sample cellular deconvolution. This method enhances biological insights by integrating cross-sample similarity and spatial gene expression patterns.

Keywords:
Bayesian modelingcellular deconvolutiongene expressionmulti-sample analysisspatial transcriptomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatially resolved transcriptomics offers high-resolution transcriptional profiling.
  • Understanding cellular composition in tissues is crucial for biological insights.

Purpose of the Study:

  • To introduce MUSTANG, a novel framework for multi-sample spatial transcriptomics data analysis.
  • To enable accurate cellular deconvolution in spatial transcriptomics datasets.

Main Methods:

  • MUSTANG integrates cross-sample expression-based similarity.
  • It incorporates spatial correlation in gene expression patterns within samples.
  • The framework performs multi-sample spatial transcriptomics spot cellular deconvolution.

Main Results:

  • MUSTANG demonstrated effectiveness on a semi-synthetic dataset.
  • Validation was performed using three real-world spatial transcriptomics datasets.
  • The framework successfully revealed biological insights in cellular characterization.

Conclusions:

  • MUSTANG provides a robust approach for analyzing multi-sample spatial transcriptomics data.
  • The framework enhances the cellular deconvolution process.
  • MUSTANG facilitates deeper biological understanding from tissue sample analysis.