HANDOUTS

  • L1. Introduction to R
  • L2. Distributions and Sampling
  • L3. Tests
  • L4. Linear models
  • L5. Clustering and classification
  • L6. Advanced Topics


    • Short reference card
    Tasks
  • Tasks L1
  • Tasks L2
  • Tasks L3
  • Tasks L4
  • Tasks L5
  • Tasks L6
  • Final Task!!!

    • Colored Scripts in PDF
  • scripts_L1
  • scripts_L2
  • scripts_L3
  • scripts_L4
  • scripts_L5
  • scripts_L6

    • SCRIPTS
  • scripts_L1.r
  • scripts_L2.r
  • scripts_L3.r
  • scripts_L4.r
  • scripts_L5.r
  • scripts_L6.r
  • solutions.zip (to be updated)
    • DATA
    LINKS
  • Official R Webpage
  • One more R Reference Card (by T.Short)
  • Quick-R course
  • Several free books
    •

    Leaded by Petr NAZAROV
    petr.nazarovlih.lu


    Dates: 23, 26, 27 May 2016

    Time: 9:00-18:00 with 1 hour lunch break

    Place: MSA 2.200, Maison du Savoir, Campus Belval, University

    Workload: 30 hours (3 full days) + independent work (report writing)

    Number of Credit Points: 2 ECTS

    Course Objective: This course is devoted to practical aspects of statistical data analysis and oriented at PhD students working with biological data. Participants will learn how to import, visualize and analyse their data using R scripting.

    Learning Outcomes: We shall start with basic R programing, see how to load and transform biological data in R. Next we shall make a short revise of basic statistical methods (descriptive statistics, tests, ANOVA) and learn how to implement this methods in R. Finally advanced topics will be touched in the last 2 sessions including gene expression (microarray and RNASeq) Finally advanced topics will be touched in the last 2 sessions: gene expression (microarray and RNASeq) data analysis, functional enrichment and survival data analysis.

    Session 1: Introduction to programming in R
    Session 2: Basic statistics. Distributions.
    Session 3: Statistical tests.
    Session 4: Linear models (ANOVA, regression)
    Session 5: PCA, clustering, prediction and classification
    Session 6: Expression data analysis. Enrichment analysis. Survival analysis.

    Computers are available in the class, but participants are welcome to bring their laptops with R/Bioconductor installed. RStudio may be a useful tool for participants as well.

    Examination: Report

    Responsible Person: Petr Nazarov (petr.nazarov[at]lih.lu)

    In the case of mistakes or possible copyright violations, please, contact the webmaster. Last update 22-05-2016