scHyperLink: Revealing Cell-Type-Specific Gene Regulation with Hypergraph Neural Networks.

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables gene expression analysis at high resolution.
• Gene Regulatory Networks (GRNs) model interactions between transcription factors (TFs) and target genes.
• Existing Graph Neural Networks (GNNs) struggle with higher-order dependencies in GRNs.

Purpose of the Study:

• To develop a novel framework for accurate GRN reconstruction.
• To address the limitations of GNNs in capturing multi-way gene regulatory relationships.
• To enhance gene interaction inference by modeling higher-order dependencies.

Main Methods:

• Introduced scHyperLink, a framework utilizing hypergraphs and Hypergraph Neural Networks (HGNNs).
• Represented multi-gene regulatory relationships using hyperedges within a hypergraph structure.
• Integrated experimentally derived interaction graphs with dynamically learned hyperedges.

Main Results:

• scHyperLink demonstrated superior accuracy in GRN reconstruction compared to state-of-the-art methods.
• Achieved higher performance on cell-type-specific benchmark datasets, especially in sparse data conditions.
• Validated biological relevance through interpretability analyses and demonstrated scalability to tissue-level analyses.

Conclusions:

• Hypergraph-based modeling effectively captures higher-order dependencies in GRNs.
• scHyperLink offers a more accurate and scalable approach for GRN reconstruction from scRNA-seq data.
• The framework provides biologically relevant insights and is reproducible.