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mNSF: multi-sample non-negative spatial factorization.

Yi Wang1, Kyla Woyshner2, Chaichontat Sriworarat3

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Genome Biology
|June 2, 2025
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Summary
This summary is machine-generated.

We developed multi-sample non-negative spatial factorization (mNSF), an alignment-free method for analyzing spatial transcriptomics data. This approach effectively identifies biological factors and functions across multiple samples, even when spatial alignment is not possible.

Keywords:
Dimensionality reductionMatrix factorizationMulti-sample analysisSpatial gene expressionSpatial transcriptomics

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Analyzing multi-sample spatial transcriptomics data presents challenges due to biological variation.
  • Existing methods often rely on spatial alignment, which is not always feasible.

Purpose of the Study:

  • To introduce multi-sample non-negative spatial factorization (mNSF), an alignment-free framework for multi-sample spatial transcriptomics analysis.
  • To extend single-sample spatial factorization techniques to accommodate multiple samples while accounting for biological variation.

Main Methods:

  • mNSF framework development, extending single-sample non-negative spatial factorization.
  • Incorporation of sample-specific spatial correlation modeling.
  • Extraction of low-dimensional data representations from multi-sample spatial transcriptomics data.

Main Results:

  • Demonstrated efficacy in simulations and real data analysis for identifying true biological factors.
  • Successfully identified shared anatomical regions and region-specific biological functions across samples.
  • mNSF performance is comparable to alignment-based methods where alignment is feasible.

Conclusions:

  • mNSF provides a robust and versatile method for analyzing spatially resolved transcriptomics data across multiple samples.
  • The alignment-free nature of mNSF enables analysis in scenarios where spatial alignment is unfeasible.
  • mNSF is a promising tool for uncovering biological insights from complex multi-sample spatial transcriptomics datasets.