Practical Statistics for the Life Sciences

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IMPORTANT DATES for this Course
Deadline for applications: January 15th 2020
Course date: January 20th - January 24th 2020

Candidates with adequate profile will be accepted in the next 72 hours after the application until we reach 20 participants.


Lieven Clement is an Associate Professor of Statistical Genomics at Ghent University, where he leads the statOmics Group. He is an expert in developing statistical methods and open source tools for differential omics data analysis. His research efforts resulted in numerous publications on novel methods and tools for, and applied research in omics data analysis. His lab is built around three strategic research pillars each connected to an omics domain: metagenomics, transcriptomics and proteomics and he also has a strong interest in leveraging his expertise to translational research. He also serves as a member of the core team that established a new Master of Science in Bioinformatics at Ghent University, as a board member of the Belgian Proteomics Association, as an expert in genomics projects of the Belgian Health Care Knowledge Center (KCE), and as an Associate Editor for the journal Biometrics (2011-2017).

Affiliation: Ghent University, Ghent, BE

Jeroen Gilis is a PhD student in the statOmics Group at Ghent University, headed by prof. Lieven Clement. After completing a bachelor's and master's degree in biochemistry and biotechnology at KU Leuven, he obtained an additional master's degree in bioinformatics at Ghent University. During his master thesis at the statistical omics group, he has worked on developing novel, scalable tools for studying differential expression in single-cell transcriptomics applications. Jeroen is currently continuing his research as a PhD student in the group.

Affiliation: Ghent University, Ghent, BE

Course description

This intermediate level course is one of our Foundations courses. It covers essential statistical concepts and methods for extracting insights from empirical data in the life sciences. The course positions applied statistics, starting from important aspects of experimental design and data exploration. We then move into statistical modeling and data analysis. We will focus on the link between linear regression and analysis of variance. Together, these methods contribute to the study of General Linear Models. The course also introduces the basics of non-parametric testing, and addresses categorical data analysis and logistic regression.

The concepts and methods are exclusively introduced via case-studies in the life sciences. For every study we elaborate on a concrete research question and then provide a study design, which is followed by data exploration. Next, we will focus on how to model the data and elaborate on the link between model parameters and the subject matter research question.


The case studies will enable the participants to build self-confidence in
  1. understanding, reading and communicating on data and data analysis
  2. selecting appropriate statistical methods and software tools for analysing different types of data
  3. interpreting the result of a statistical data analysis in terms of subject matter research questions and reporting them appropriately.
There is a strong emphasis in reproducible research by extensively using R/Rmarkdown scripts. This approach will enable the participants to weave statistical analyses, code, results and interpretation in webpages and PDF documents so that their entire data analysis workflow is transparent and reproducible. The course materials are designed in R/Rmarkdown, kickstarting the course participants into developing their own scripts.

Course Pre-requisites

This is an intermediate level course. Very basic knowledge in Statistics and minimal skills in R will be needed. Please check your knowledge by running through the tutorial that is provided.

Detailed Program

Instituto Gulbenkian de Ciência,

Apartado 14, 2781-901 Oeiras, Portugal

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Last updated:December 16th 2019