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Enrichment Analysis

Updated: Sep 19, 2023

Enrichment analysis, a cornerstone of modern bioinformatics, offers a dynamic approach to unlocking hidden biological insights within vast genomic datasets. This method capitalizes on the idea that genes, proteins, or other molecular entities with common biological functions are often co-regulated or interact in specific pathways. By exploring the collective behavior of these functional groups, enrichment analysis helps researchers decipher the functional relevance of genes in a given context.


Enriched GO terms, mmu04110


KEGG pathway, mmu04110


Technical Insight:

  • Gene List Compilation: Begin with a list of genes that possess a specific trait or show differential expression under certain experimental conditions.

  • Gene Set Referencing: Cross-reference these genes with predefined gene sets representing distinct biological pathways, processes, or functions.

  • Statistical Tests: Employ statistical tests such as hypergeometric or Fisher's exact tests to assess whether the observed number of genes within a set deviates significantly from random chance.

  • Significance Assessment: Calculate p-values or enrichment scores to quantify the significance of pathway associations.

  • Multiple Testing Correction: Apply correction methods, such as the Bonferroni or Benjamini-Hochberg procedure, to account for multiple hypothesis testing.

  • Pathway Ranking: Rank pathways based on their significance, aiding in the identification of the most biologically relevant ones.

  • Visual Representation: Display results using visually informative plots, such as bar charts or heatmaps, to convey enriched pathways and their respective significance levels.

  • Functional Interpretation: Translate statistical results into biological insights by understanding which pathways are overrepresented and their potential implications in the studied context.


Enrichment analysis, main pipeline steps

Unveiling Biological Insights:

Enrichment analysis acts as a guide, helping researchers navigate intricate biological information. It identifies key pathways and functions that matter most in your data. These insights offer hypotheses for focused experiments, validating predictions and speeding up discoveries. This method is beneficial when dealing with large data sets, highlighting important biological processes that can lead to a better understanding. Enrichment analysis clarifies the complex world of genomics by focusing on these essential elements.



Example barplot (AllEnricher)

Example bubble plot (AllEnricher)

 

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