Gene Set Enrichment
Gene Set Enrichment - Related Videos
09:35
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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Cited by 6 •
2017
We provide a standardized protocol for the use of gene set enrichment analysis of transcriptomic data to identify an ideal mouse model for translational research. This protocol can be used with DNA microarray and RNA sequencing data and can further be extended to other omics data if data are available.
05:29
Self-standing Electrochemical Set-up to Enrich Anode-respiring Bacteria On-site
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Cited by 1 •
2018
On-site microbial enrichment or in situ cultivation techniques can facilitate the isolation of difficult-to-culture microbial taxa, especially from low-biomass or geochemically extreme environments. Here, we describe an electrochemical set-up without using an external power source to enrich microbial strains that are capable of extracellular electron transport (EET).
03:08
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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2025
The current work describes a protocol for running the Pathway2Targets algorithm, an R script that predicts and prioritizes therapeutic targets based on the profile of intracellular signaling pathways generated by comparing case versus control samples from a bulk RNA-sequencing experiment.
13:32
Laser Capture Microdissection of Enriched Populations of Neurons or Single Neurons for Gene Expression Analysis After Traumatic Brain Injury
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Cited by 22 •
2013
We describe how to use laser capture microdissection (LCM) to obtain enriched populations of hippocampal neurons or single neurons from frozen sections of the injured rat brain for subsequent gene expression analysis using quantitative real time PCR and/or whole-genome microarrays.
01:34
Environmental Enrichment for Rodents
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2026
All animal procedures described here must be conducted in accordance with institutional animal ethics guidelines and approved by IACUC. All procedures must follow the principles of the 3Rs—Replacement, Reduction, and Refinement—and must be performed by trained personnel. Environmental enrichment involves objects and practices that encourage natural behaviors, improve psychological well-being, and promote better welfare in laboratory rodents. Environmental enrichment should be tailored to the...
FAQs
Gene Set Enrichment adds a coordinated view to single-gene analysis. A biologically meaningful signal may appear when several members of the same pathway or functional category shift together, even if no individual gene provides a complete explanation. This makes group-level patterns useful for interpreting complex genomic or transcriptomic results.
Ranking determines where each gene sits in the analysis, often according to a measure such as differential expression. Enrichment is then assessed by asking whether members of a selected gene group concentrate near the top or bottom of that ordered list. The direction and location of this clustering help connect data patterns with biological categories.
Statistical testing evaluates whether the observed concentration of gene-set members is unlikely to reflect the ranked data pattern alone. Because many pathways, functional categories, or ontology groups may be tested, multiple-testing correction is important when judging significance. This step helps distinguish broadly interesting results from patterns that meet a stronger statistical criterion.
Curated pathways, functional categories, and Gene Ontology groups provide different lenses for organizing the same gene-level results. A pathway-oriented set can emphasize biological processes, whereas a functional or ontology grouping can describe genes by shared roles or annotations. Comparing these categories can reveal which biological interpretations are most consistently supported by the data.
A typical analysis starts by assembling genomic or transcriptomic measurements and ranking genes with a relevant measure, such as differential expression. Researchers then evaluate predefined gene groups against that ranking, apply enrichment statistics, and account for multiple testing. Significant patterns can subsequently be interpreted as coordinated biological responses rather than isolated gene changes.
In genetics, the approach can connect observed variants or expression changes with biological processes, regulatory networks, and disease mechanisms. This broader view helps organize molecular observations into biologically interpretable relationships and can expose coordinated patterns that individual-gene inspection might miss. The resulting associations are useful for generating hypotheses for later functional studies.