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SpaFun: discovering domain-specific spatial expression patterns and new disease-relevant genes using functional

Xi Jiang1,2, Yanghong Guo3, Lei Guo1

  • 1Quantitative Biomedical Research Center, Department of Health Data Science & Biostatistics, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States.

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SpaFun, a new method for spatial transcriptomics, efficiently identifies key genes in large datasets. It improves upon existing techniques by accurately capturing spatial patterns and gene co-expression for better biological insights.

Keywords:
domain-representative gene (DRG)functional principal component analysis (fPCA)spatial expression patternspatially resolved transcriptomics (SRT)spatially variable gene (SVG)

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Existing spatially variable gene detection methods struggle with large-scale spatially resolved transcriptomics data.
  • Limitations include computational inefficiency, reduced statistical power with increasing data size, and difficulty capturing spatial heterogeneity and gene co-expression.

Purpose of the Study:

  • To develop SpaFun, a novel, non-model-based method to overcome limitations in current spatial gene detection techniques.
  • To enhance computational efficiency and statistical power for analyzing large spatially resolved transcriptomics datasets.
  • To accurately identify domain-representative genes while accounting for spatial heterogeneity and co-expression patterns.

Main Methods:

  • SpaFun utilizes functional principal component analysis (fPCA).
  • It is a non-model-based approach designed for scalability.
  • The method was applied to three spatially resolved transcriptomics datasets.

Main Results:

  • SpaFun demonstrated superior computational efficiency and statistical power compared to existing methods.
  • It outperformed state-of-the-art algorithms (DESeq, edgeR, limma, SPARK, CSIDE) in identifying representative genes for tumor regions.
  • SpaFun accurately identified genes specific to spatial domains like tumor, immune, and stroma regions.

Conclusions:

  • SpaFun is an effective tool for identifying domain-representative genes in large-scale spatial transcriptomics data.
  • The method offers improved accuracy and efficiency over existing algorithms.
  • By uncovering novel disease-relevant genes, SpaFun can provide insights into molecular mechanisms and therapeutic strategies.