•
Day 1: Introduction to statistical reasoning, GSEA and R (9:30-17:30)
– Lecture: "So you have a list of thousand gene names: why do we do HT analyses?"
– Hands-on guide: data preparation for gene set enrichment analysis
– Guided self-study: using R for data loading and basic statistical calculations
– Individual project work: loading data for the individual project
– Lecture: "Statistics gone wrong: basics of statistical problems in HT applications"
• Day 2: A functional analysis primer (9:30-17:30)
– Lecture: "Methods of pathway and functional analysis in gene set enrichment analyses"
– Hands-on guide: gene set enrichment techniques in R and other frameworks
– Guided self-study: comparing results of different gene set enrichment techniques
– Individual project work: biological interpretation of the results
– Lecture: "Common mistakes in functional analysis of HT data"
• Day 3: Getting the gene sets, building your own modules (9:30-17:30)
– Lecture: "Gene expression, co-expression and correlations"
– Hands-on guide: making your own modules
– Guided self-study: gathering gene sets for GSEA
– Individual project work
• Day 4: Beyond simple GSEA (9:30-17:30)
– Lecture: "Introduction to multivariate approaches and ML techniques"
– Hands-on guide: Practical guide to multivariate and ML techniques in R
– Hands-on guide: Using GSEA in single cell RNASeq
– Individual project work
– Lecture: "How to know when you are done?"
• Day 5: Evaluation of individual project (9:30-17:30)
– Guided self-study: problems from your research
– Individual work
– Student's presentations
– Discussion round
– Lecture: "Course wrap-up: where to go from here?"