PyWGCNA

PyWGCNA performs weighted gene co-expression network analysis (WGCNA) on large RNA-seq datasets to identify co-expression modules and enable comparative and functional interpretation of gene modules.

Key Features:

• Performance: Faster implementation compared to the R WGCNA implementation for processing large RNA-seq datasets.
• Module Identification: Identifies co-expression modules as clusters of highly correlated genes across samples.
• Comparative Analysis: Compares modules between multiple networks and supports simultaneous analysis of multiple datasets to detect shared or unique signatures.
• Functional Enrichment Analysis: Performs downstream enrichment using Gene Ontology (GO), KEGG, and REACTOME databases.
• Inter-module Interaction Analysis: Analyzes inter-module relationships with a focus on protein-protein interactions.
• External Comparison: Compares co-expression modules with external gene lists, such as marker genes from single-cell studies.

Scientific Applications:

• Genomic co-expression analysis: Identification and interpretation of gene co-expression networks in genomics research.
• MODEL-AD (5xFAD) brain RNA-seq: Application to brain bulk RNA-seq datasets from MODEL-AD (5xFAD) to uncover genotype-associated modules.
• Comparative signature detection: Detection of shared or unique co-expression signatures across different conditions or experimental setups.
• Integration with single-cell data: Contextualization of bulk co-expression modules using single-cell marker gene lists.

Methodology:

Constructs a network from gene expression data, identifies clusters/modules of highly correlated genes, performs functional enrichment using GO, KEGG, and REACTOME, analyzes inter-module protein-protein interactions, compares modules across multiple networks/datasets, and compares modules to external gene lists such as single-cell marker genes.