hdWGCNA

hdWGCNA performs weighted gene co-expression network analysis on high-dimensional transcriptomics datasets to identify gene modules and characterize systems-level interactions in single-cell RNA sequencing, spatial transcriptomics, and long-read isoform-level data.

Key Features:

• Network Inference and Module Identification: Constructs co-expression networks using WGCNA to identify robust modules of interconnected genes across multi-scale cellular and spatial hierarchies.
• Integration with Biological Knowledge Sources: Integrates identified gene modules with various biological knowledge sources to provide contextual biological insights.
• Compatibility with Seurat Objects: Accepts input data formatted as Seurat objects for single-cell and spatial transcriptomics analyses.
• Scalability and Versatility: Scales to large datasets, including datasets with nearly 1 million cells, and supports isoform-level network analysis using long-read single-cell data.
• Comprehensive Analytical Functions: Provides functions for gene module identification, gene enrichment analysis, statistical testing, and data visualization.
• Application in Disease Research: Has been applied to identify disease-relevant co-expression modules in autism spectrum disorder and Alzheimer's disease.

Scientific Applications:

• Systems Biology: Analyzing systems-level gene interactions and network architecture in complex tissues.
• Single-Cell Genomics: Detecting co-expression modules within single-cell RNA sequencing datasets.
• Spatial Transcriptomics: Characterizing spatial patterns of gene co-expression in tissue using spatial transcriptomics data.
• Disease and Translational Research: Identifying co-expression modules linked to diseases such as autism spectrum disorder and Alzheimer's disease to support precision medicine and biomarker discovery.

Methodology:

Requires input as Seurat objects; utilizes WGCNA methodology to construct gene co-expression networks; identifies interconnected gene modules and performs gene enrichment analysis; includes tools for visualizing network structures and performing statistical tests.