multiWGCNA

multiWGCNA constructs weighted gene co-expression networks across spatial or temporal conditions to identify and compare gene modules associated with traits in multi-dimensional expression datasets.

Key Features:

• WGCNA foundation: Builds on weighted gene co-expression network analysis (WGCNA) principles to model biological pathways and transcriptional signatures.
• R package: Implemented as an R package.
• Multi-dimensional network construction: Accommodates datasets with spatial or temporal traits to construct comprehensive networks across conditions.
• Condition-specific networks: Generates combined networks and separate networks for each condition to analyze module preservation and alteration.
• Module mapping and analysis: Maps gene modules between and across designs, computes module–trait correlations, and assesses module preservation.
• Disease research applications: Applied to resolve neurotoxic astrocyte transcriptional programs in mice with experimental autoimmune encephalitis and to track temporal evolution of pathological modules in models of tau pathology.

Scientific Applications:

• Complex disease studies: Analyze co-expression networks where gene expression varies across tissues or over time to investigate disease progression and response.
• Neuroinflammation: Resolve neurotoxic astrocyte transcriptional programs in mice with experimental autoimmune encephalitis.
• Neurodegeneration: Track temporal evolution of pathological modules in models of tau pathology.
• Module preservation and trait association: Assess module preservation and module–trait correlations across spatial or temporal conditions.

Methodology:

Constructs weighted gene co-expression networks using WGCNA, builds combined and condition-specific networks, maps modules across designs, computes module–trait correlations, and assesses module preservation across spatial or temporal conditions.