Statistical tests and statistical learning for omics data (2019-09-11)

Learning objectives:

  • Obtaining an overview of current computational systems biology approaches for omics data analysis
  • Understanding the principles behind data pre-processing, quality control and statistical analysis of large-scale biological datasets
  • Obtaining experience in higher-level omics analysis via pathway and network analyses
  • Learning the basics of machine learning analyses for omics sample clustering and classification


Please register here.


  • Basic working knowledge of R
  • Bring your own laptop and have a recent version of R (3.6 or later) and RStudio (1.2 or later) installed.

Time plan

Wednesday (11.09.2019)

Time Topic
9.15 Coffee & get together
9.45 Lecture
  Welcome & introduction
  Data pre-processing, filtering & quality control
  Feature selection and hypothesis tests
  Statistical meta-analysis of omics data
12.30 Lunch break
13.30 Practical tutorial
  Introduction to the example datasets used for the project
  Software installations on student laptops, retrieving public biomedical datasets
  Hands-on data pre-processing & differential abundance analysis
17.00 End

Thursday (12.09.2019)

Time Topic
9.15 Coffee & get together
9.45 Lecture
  Cellular pathway & network analysis
  Unsupervised machine learning analysis (clustering)
  Supervised machine learning analysis (prediction)
12.30 Lunch break
13.30 Practical tutorial
  Hands-on pathway & network analysis
  Hands-on machine learning analyses
  Conclusion & final discussion
17.00 End


Enrico Glaab with Diana Hendrickx, Armin Rauschenberger and Leon-Charles Tranchevent


University of Luxembourg, Esch-sur-Alzette Rooms will be announced.


Roland Krause