Practical Statistics for the Life Sciences

Downloadable poster in PDF

IMPORTANT DATES for this Course
Deadline for applications: Mar 4th 2022
Course date: March 28th - April 1st 2022

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 two strategic research pillars each connected to an omics domain: (single cell) transcriptomics and proteomics and he 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 and as a board member of the Belgian Proteomics Association.

Affiliation: Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, BE

Milan Malfait 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 Bio-engineering at VUB, he obtained an additional master's degree in bioinformatics at ULB. Milan is currently developing fast and scalable methods for differential analysis of single cell RNA-seq data.

Affiliation: Department of Applied Mathematics, Computer Science and Statistics, 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 understanding, reading and communicating on data and data analysis selecting appropriate statistical methods and software tools for analysing different types of data 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.


Detailed Program



The Oeiras Valley support allows us to waive 50% of the course fee to accepted candidates that reside or work in the Oeiras council.

Instituto Gulbenkian de Ciência,

Apartado 14, 2781-901 Oeiras, Portugal

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Last updated: February 9th 2022