//Property: name
"name": "Differential analysis of Bulk RNA-Seq data: design, describe, explore and model"

//Property: abstract
"abstract": "This course covers the complete workflow for differential analysis of bulk RNA-seq data, emphasizing the critical importance of experimental design, data exploration, normalization, and statistical modeling. It addresses key concepts including biological versus technical replicates, confounding effects, paired and unpaired designs, and the prevention of batch effects. The statistical framework focuses on negative binomial modeling using DESeq2 and edgeR, covering normalization methods (library size and composition), dispersion estimation, multiple testing correction (FDR control), and proper interpretation of results through visualization tools like MA-plots, volcano plots, and heatmaps."

//Property: author
"author":  [
  {
    "@type": "Person",
    "name": "Elise JACQUEMET"
  },{ 
    "@type": "Person",
    "name": "Charlotte BERTHELLIER"
  }
]

//Property: description
"description": "This course covers the complete workflow for differential analysis of bulk RNA-seq data, emphasizing the critical importance of experimental design, data exploration, normalization, and statistical modeling. It addresses key concepts including biological versus technical replicates, confounding effects, paired and unpaired designs, and the prevention of batch effects. The statistical framework focuses on negative binomial modeling using DESeq2 and edgeR, covering normalization methods (library size and composition), dispersion estimation, multiple testing correction (FDR control), and proper interpretation of results through visualization tools like MA-plots, volcano plots, and heatmaps."

//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 design appropriate RNA-seq experiments that control for confounding effects, include sufficient biological replicates, and properly randomize technical factors",
    "The student will be able to perform exploratory data analysis using PCA, hierarchical clustering",
    "The student will be able to apply and justify appropriate normalization methods (DESeq2/edgeR) that correct for library size and composition biases",
    "The student will be able to implement negative binomial generalized linear models for differential expression analysis and interpret dispersion estimates",
    "The student will be able to control false discovery rates through multiple testing correction and critically evaluate results using diagnostic plots and statistical metrics"
]

//Property: keywords
"keywords": "experimental design, DESeq2, edgeR, normalization, multiple testing correction"

//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"