scHumanNet

scHumanNet constructs cell-type-specific gene networks (CGNs) from single-cell transcriptome data by integrating the HumanNet reference interactome to identify cell-type-specific gene functions.

Key Features:

• Cell-Type-Specific Gene Networks (CGNs): Constructs CGNs using the HumanNet reference interactome to model gene interactions within individual cell types from single-cell transcriptome data.
• Functional Relevance via network compactness: Leverages network compactness to enhance functional relevance and to deconvolute gene signatures across cell types.
• Gene Prioritization and Differential Hubness Analysis: Prioritizes genes using centrality measures within CGNs and detects differential hubness between disease and healthy conditions.

Scientific Applications:

• Breast Cancer Prognostics: Identified breast cancer prognostic markers associated with T cells by analyzing network compactness across cell types.
• GITR T-cell-specific effects: Revealed T-cell-specific functional effects of GITR linked to breast cancer prognosis.
• Autism Spectrum Disorder gene defects: Highlighted functional defects in autism spectrum disorder genes specific to inhibitory neurons.

Methodology:

Constructs and analyzes CGNs from single-cell transcriptome data, integrates these networks with the HumanNet reference interactome, and applies network compactness and centrality measures to deconvolute gene signatures and identify differential hubness.