01 Jul 02 Jul
09:00 - 17:00

NUTRIM Workshop: Multi-omics functional data analysis

Overview of Workshop

Registration has been Closed!!

Does your research involve microbiome and/or metabolomics data, are you planning to work with such data or interested in expanding your horizons? Our comprehensive 2-day course consisting of in depth lectures and hands-on analytical sessions is designed to equip you with the knowledge and practical skills needed to navigate and analyze such complex biological datasets.  


Better understanding of common concepts: Grasp the fundamental ideas behind analyzing metabolomic and microbiome data and the involved variables Hands-on experience with R programming: Dive into practical exercises to develop your R programming skills while working with actual data, to create reusable workflows for your own data. Master preprocessing techniques: Learn commonly used preprocessing steps, including transformation, normalization, and scaling, and understand their crucial role in downstream analysis. Dimension reduction and visualization: Explore basic dimension reduction techniques and visualization methods tailored for metabolomic and microbiome data In-depth data interpretation: Explore the interpretation of metabolomic and microbiome data, identifying biologically relevant patterns, relationships, and trends, as well as exploring richness and diversity analysis. Statistical proficiency: Gain knowledge on statistical methods commonly used for analyzing metabolomics and microbiome datasets, from univariate to multivariate analysis techniques such as PERMANOVA and ASCA. Metabolic pathway analysis: Learn how to interpret metabolomic data within the context of metabolic pathways. Using pathway enrichment analysis, network visualization, and pathway mapping for functional analysis



The course is open to all early-career scientists within the Faculty of Health, Medicine, and Life Sciences (FHML) of Maastricht University. The course does not require prior experience in programming in R nor microbiome or metabolomics data analysis. To benefit most from this course, we will:

send preparatory instructions on installation of required software in due course
send some links to short introductory videos for participants new to R
require that participants bring their own computer

Dept. of Pharmacology and Toxicology

Dr. Agnieszka Smolinska & Drs. Michal Skawinski

Dept. of Bioinformatics (BiGCaT)

Dr. Susan Coort & Drs.Denise Slenter

Dept. of Medical Microbiology, Infectious Diseases and Infection Prevention

Prof. dr. John Penders & Drs. David Barnett



  • David Barnett, Medical Microbiology, metagenomics data
    • Expertises:
      • Infant/Child gut microbiome and child health
      • Data analysis and visualization with R (including Shiny app development)
      • Molecular Epidemiology
      • See GitHub profile for software projects, including microViz, an R package for microbiome data analysis and visualization.
  • Denise Slenter, Bioinformatics, metabolomics funcational data analysis
    • Expertises:
      •  Inherited Metabolic Disorders, lipidomics
      • Targeted and untargeted functional metabolomics analysis (pathways and networks)
      • Bio data curation for non-programmers (community curation)
  • Dr. Agnieszka Smolinska & Drs. Michal Skawinski,  Pharmacology and Toxicology
    • Expertise:
      • Pre-processing of metabolomics data
      • Machine learning of metabolomics data; data fusion and integration of metabolomics with other data types

Costs and registration:

Registration for this course is mandatory and free of charge; a small fee is required if a registration is cancelled too late. Registration is open now.

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