Enrichment Analysis

Enrichment analysis is a powerful way to interpret large-scale omics data by asking whether certain biological pathways, processes, or functional categories are statistically over-represented in your dataset. Instead of looking at each gene, protein, or metabolite individually, enrichment highlights the biological context—helping you see which systems are most affected in your experiment.

What Enrichment Analysis Enables in Omics Studio

• Discover biological meaning
  Identify pathways and processes that are significantly enriched among the features of interest, making it easier to understand complex experimental results.

• Filter and focus your analysis
  Apply thresholds (e.g., p-values, fold change) or use your own curated lists to narrow the analysis to the most relevant entities.

• Compare across groups
  Explore whether different experimental conditions or groups activate distinct biological pathways.

• Visualize results interactively
  View enrichment results in both tabular form and as intuitive visualizations like bar charts and dot plots, with filters and settings to tailor the view.

Approaches Available in Omics Studio

• Over-Representation Analysis (ORA): Tests whether specific categories are enriched among significantly changed features.
• Gene Set Enrichment Analysis (GSEA): Evaluates whether predefined sets of genes are enriched across an entire ranked list, capturing more subtle changes.

Summary

Together, these enrichment tools help you move from lists of identifiers to actionable biological insights.