TL;DR
stIHC is a new iterative hierarchical clustering method that effectively identifies spatial gene co-expression modules in spatial transcriptomics data, outperforming existing approaches and revealing biologically relevant gene groups.
Contribution
The paper introduces stIHC, a novel clustering method that improves detection of spatial gene modules, especially rare patterns, across multiple spatial transcriptomics technologies.
Findings
stIHC outperforms existing clustering methods in simulations and real datasets.
Gene modules identified by stIHC show consistent biological functions.
Method is robust across different technologies and resolutions.
Abstract
Recent advancements in spatial transcriptomics technologies allow researchers to simultaneously measure RNA expression levels for hundreds to thousands of genes while preserving spatial information within tissues, providing critical insights into spatial gene expression patterns, tissue organization, and gene functionality. However, existing methods for clustering spatially variable genes (SVGs) into co-expression modules often fail to detect rare or unique spatial expression patterns. To address this, we present spatial transcriptomics iterative hierarchical clustering (stIHC), a novel method for clustering SVGs into co-expression modules, representing groups of genes with shared spatial expression patterns. Through three simulations and applications to spatial transcriptomics datasets from technologies such as 10x Visium, 10x Xenium, and Spatial Transcriptomics, stIHC outperforms…
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