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?"