PhD Course Microbiome & Metabolomic Data Analysis

NUTRIM PhD Course Microbiome & Metabolomic Data Analysis

July 1-2, 2024 (9.00-17.00 h.)

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.  

WHAT YOU WILL GAIN FROM THIS COURSE:

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

AUDIENCE AND REQUIREMENTS

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:

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

FACULTY

Dept. of Pharmacology and Toxicology

Dr. Agnieszka Smolinska & Drs. Michael 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