NAFLD

NAFLD analyzes RNA-seq datasets, including GSE135251, to identify gene co-expression modules, differential expression patterns, and candidate therapeutics associated with non-alcoholic fatty liver disease (NAFLD) progression.

Key Features:

• Gene Co-expression Analysis: Uses RNA-seq datasets (including GSE135251) and WGCNA to detect gene co-expression modules and perform module preservation analysis.
• Module Stability and Reproducibility: Assesses module stability via sampling techniques and evaluates reproducibility across multiple datasets.
• Functional Annotation and Differential Expression Analysis: Performs functional annotation with R gProfiler and tests differential expression between disease states using ANOVA and Student's t-tests, with ROC curve analysis for classification performance.
• Disease Stratification: Identifies modules positively associated with steatosis and fibrosis to stratify NAFL, NASH, and healthy control samples.
• Hub Gene Identification: Highlights significant hub genes such as Aebp1 and Fdft1 linked to fibrosis and steatosis.
• Correlation with Biological Processes: Identifies module correlations with immune response, mitochondrial function, collagen synthesis, and sterol biosynthesis.
• Drug Discovery Potential: Leverages the Connectivity Map to propose candidate drugs for NAFLD treatment based on module signatures.

Scientific Applications:

• NAFLD progression research: Enables identification of reproducible gene co-expression modules and molecular signatures relevant to disease mechanisms.
• Therapeutic target and drug candidate discovery: Supports nomination of hub genes and Connectivity Map–derived compounds as potential therapeutic leads.
• Patient stratification and translational studies: Facilitates stratification of samples across NAFL, NASH, and control groups to inform translational research.

Methodology:

Computational methods explicitly include analysis of RNA-seq datasets (GSE135251), WGCNA with module preservation analysis, sampling-based stability assessment, functional annotation with R gProfiler, differential expression testing via ANOVA and Student's t-tests, ROC curve analysis, and Connectivity Map–based drug proposals.