//Property: name
"name": "Gene Set Analysis"

//Property: abstract
"abstract": "This course covers gene set analysis methods for interpreting differential gene expression results in RNA-seq data. It focuses on two complementary approaches: Over-Representation Analysis (ORA), which tests whether specific gene sets are over-represented among filtered differentially expressed genes, and Gene Set Enrichment Analysis (GSEA), which evaluates whether predefined gene sets show statistically significant enrichment at the extremes of a ranked gene list. Practical implementation uses R packages (clusterProfiler, enrichplot) with Arabidopsis thaliana data and Gene Ontology databases."

//Property: author
"author":  [
  {
    "@type": "Person",
    "name": "Thibault DAYRIS"
  },{ 
    "@type": "Person",
    "name": "Jean-Pascal MENEBOO"
  },{ 
    "@type": "Person",
    "name": "Audrey ONFROY"
  }
]

//Property: description
"description": "This course covers gene set analysis methods for interpreting differential gene expression results in RNA-seq data. It focuses on two complementary approaches: Over-Representation Analysis (ORA), which tests whether specific gene sets are over-represented among filtered differentially expressed genes, and Gene Set Enrichment Analysis (GSEA), which evaluates whether predefined gene sets show statistically significant enrichment at the extremes of a ranked gene list. Practical implementation uses R packages (clusterProfiler, enrichplot) with Arabidopsis thaliana data and Gene Ontology databases."

//Property: audience
"audience": {
    "@type": "Audience",
    "audienceType": "beginner in high-throughput sequencing data analysis, starting at graduate level" 
}

//Property: educationalLevel
"educationalLevel": "Beginner"

//Property: inLanguage
"inLanguage": ["en-US"]
  
//Property: teaches
"teaches": [
    "The student will be able to distinguish between ORA and GSEA methodologies and select the appropriate approach for their biological question",
    "The student will be able to prepare gene lists from differential expression results, including proper gene identifier cleaning and filtering strategies",
    "The student will be able to perform over-representation analysis using clusterProfiler with appropriate gene set databases and statistical parameters",
    "The student will be able to execute GSEA with properly ranked and weighted gene lists, interpreting enrichment scores and normalized enrichment scores",
    "The student will be able to visualize and interpret gene set analysis results using barplots, dotplots, GSEA curves, and understand their biological significance"
]

//Property: keywords
"keywords": "Over-Representation Analysis (ORA), Gene Set Enrichment Analysis (GSEA), Differential gene expression, database"

//Property: learningResourceType (presentation or handout)
"learningResourceType": ["presentation"]

//Property: license
// "https://creativecommons.org/licenses/by-sa/4.0/"
// "https://mit-license.org/"
"license": ["https://creativecommons.org/licenses/by-sa/4.0/"]

//Property: dateModified
"dateModified": "2025-11-17T0:00:00+00:00"