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Multi-sample non-negative spatial factorization.

Yi Wang1, Kyla Woyshner2, Chaichontat Sriworarat3

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

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|July 15, 2024
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Summary
This summary is machine-generated.

We developed multi-sample non-negative spatial factorization (mNSF), a new method for analyzing spatial transcriptomics data from multiple samples. mNSF effectively identifies biological factors and functions, 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 may require 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 non-negative spatial factorization (NSF) to accommodate multiple samples.

Main Methods:

  • mNSF is an alignment-free framework that extends NSF to multi-sample datasets.
  • It incorporates sample-specific spatial correlation modeling.
  • The method extracts low-dimensional data representations from spatial transcriptomics data.

Main Results:

  • mNSF effectively identifies true biological factors and shared anatomical regions across samples.
  • It also reveals region-specific biological functions.
  • Performance is comparable to alignment-based methods when alignment is feasible and enables analysis when it is not.

Conclusions:

  • mNSF is a robust method for analyzing spatially resolved transcriptomics data across multiple samples.
  • The alignment-free nature of mNSF expands its applicability to diverse biological scenarios.
  • This framework facilitates a deeper understanding of biological variation in spatial transcriptomics studies.