MLP

MLP interprets differential gene expression results by integrating gene-level p-values with curated pathways and ontologies to produce pathway-level significance scores for microarray and RNA-seq datasets.

Key Features:

• Pathway-oriented framework: Integrates gene-level p-values with structured biological knowledge to enable pathway-level interpretation.
• Supported annotation resources: Extracts pathways and terms from Gene Ontology Biological Process (GOBP), Molecular Function (GOMF), Cellular Component (GOCC), KEGG, and Reactome.
• P-value aggregation: Aggregates gene-wise p-values into pathway-level significance scores.
• Functional enrichment mapping: Maps gene-level significance measures onto curated pathways and ontologies for enrichment analysis.
• Transcriptomic data support: Operates on large microarray and RNA-seq datasets.
• Visualization and reporting outputs: Produces tables of enriched pathways, barplots of pathway significance, and Gene Ontology graph displays.

Scientific Applications:

• Interpretation of differential expression: Provides biologically interpretable summaries of differential gene expression analyses.
• Functional enrichment analysis: Identifies biological processes, molecular functions, and cellular components most perturbed in an experiment.
• Pathway-level summarization: Enables identification of mechanistic pathway perturbations from high-dimensional transcriptomic data.

Methodology:

Integrates gene-level p-values with structured biological knowledge by mapping gene-wise significance onto curated pathways and ontologies and aggregating gene-wise p-values into pathway-level significance scores, with pathway extraction from GOBP, GOMF, GOCC, KEGG, and Reactome for microarray and RNA-seq datasets.