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Spatial transcriptomics iterative hierarchical clustering (stIHC): A novel method for identifying spatial gene

Catherine Higgins1, Jingyi Jessica Li2, Michelle Carey1

  • 1School of Mathematics and Statistics University College Dublin Dublin Ireland.

Quantitative Biology (Beijing, China)
|February 12, 2026
PubMed
Summary
This summary is machine-generated.

A new method, spatial transcriptomics iterative hierarchical clustering (stIHC), effectively clusters spatially variable genes (SVGs) into co-expression modules. This approach enhances the detection of unique spatial expression patterns in tissues, improving our understanding of gene functionality.

Keywords:
functional data analysisfunctionally related genesgene co‐expression modulesspatial transcriptomicsspatially variable genes

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Spatial transcriptomics (ST) technologies enable simultaneous measurement of RNA expression and spatial information in tissues.
  • Understanding spatial gene expression patterns is crucial for tissue organization and gene functionality insights.
  • Current clustering methods for spatially variable genes (SVGs) struggle to identify rare or unique spatial expression patterns.

Purpose of the Study:

  • To introduce a novel method, spatial transcriptomics iterative hierarchical clustering (stIHC), for clustering SVGs into co-expression modules.
  • To improve the detection of unique and rare spatial expression patterns within tissues.
  • To provide a robust tool for analyzing spatial gene expression and tissue structure.

Main Methods:

  • Development of the spatial transcriptomics iterative hierarchical clustering (stIHC) algorithm.
  • Application and validation of stIHC on simulated datasets.
  • Testing stIHC on spatial transcriptomics datasets from 10x Visium, 10x Xenium, and Spatial Transcriptomics technologies.

Main Results:

  • stIHC demonstrated superior performance compared to existing methods like SPARK, SPARK-X, MERINGUE, and SpatialDE in clustering SVGs.
  • Gene ontology enrichment analysis confirmed shared biological functions among genes within stIHC-derived modules.
  • The method proved robust across different ST technologies with varying gene counts and spatial resolutions.

Conclusions:

  • stIHC is a powerful new tool for identifying co-expression modules of spatially variable genes.
  • The method accurately captures functional relationships between genes based on their spatial expression patterns.
  • stIHC advances the analysis of spatial gene expression, offering deeper insights into the functional organization of complex tissues.