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LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data.

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  • 1School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Shenzhen 518172, China.

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|July 1, 2025
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Summary
This summary is machine-generated.

LEGEND integrates single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomic (SRT) data to identify co-expressed gene groups. This novel method reveals gene co-functionality and spatial patterns, enhancing biological insights.

Keywords:
Co-expressed gene clusteringFeature gene selectionGene co-functionalitySingle-cell RNA sequencingSpatially resolved transcriptomics

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Gene co-expression analysis is crucial for understanding biological functions and disease mechanisms.
  • Existing methods often analyze single-cell RNA sequencing (scRNA-seq) or spatially resolved transcriptomic (SRT) data independently, potentially missing integrated co-functionality signals.
  • There is a need for methods that can leverage both scRNA-seq and SRT data for comprehensive gene co-expression analysis.

Purpose of the Study:

  • To introduce LEGEND (muLtimodal co-Expressed GENes finDer), a novel computational method for integrating scRNA-seq and SRT data.
  • To identify co-expressed gene groups at both cell type and tissue domain levels, capturing nuanced patterns.
  • To demonstrate the utility of LEGEND in exploring gene co-functionality, spatial patterns, and disease-associated gene interactions.

Main Methods:

  • LEGEND employs a hierarchical clustering algorithm to integrate scRNA-seq and SRT data.
  • The algorithm is designed to maximize redundancy within clusters and complementarity between clusters.
  • Enrichment and co-function analyses are used to validate the biological relevance of identified gene clusters.

Main Results:

  • LEGEND successfully identifies biologically relevant co-expressed gene groups by integrating multimodal transcriptomic data.
  • The method reveals nuanced gene co-expression patterns and spatial coherence across cell types and tissue domains.
  • LEGEND demonstrates utility in exploring context-specific gene functions, shifts in gene-gene interactions, and enhancing annotation accuracy for both scRNA-seq and SRT data.

Conclusions:

  • LEGEND provides a powerful approach for integrating scRNA-seq and SRT data to uncover co-functional genes.
  • The method advances our understanding of gene co-expression, spatial transcriptomics, and gene function in biological and pathological contexts.
  • LEGEND facilitates the discovery of novel gene functions and disease-associated gene crosstalk by analyzing integrated transcriptomic data.