PhD Courses in Denmark

The teaching will be carried out as lectures, exercises, workshops, and participant presentations. During the workshops, participants will form small groups and carry out various data analyses with their own data set or provided study data sets. Participants will meet daily in small groups(2-4 people) and accomplish tasks which culminate into a final hand-in presentation. Furthermore, a number of visiting expert researchers are intensively involved in the course to communicate the most recent advances in the field of metabolomics.

A student who has met the objectives of the course will be able to:

• Describe the basic principles of high-resolution tandem mass spectrometry
• Understand typical mass spectrometry data formats
• Carry out data pre-processing using MZmine
• Understand and apply metabolite identification confidence levels
• Use and understand tools for advanced metabolite identification (e.g. GNPS, FERMO and Sirius+CSI:FingerID)
• Understand and use basic multi- and univariate statistical methods, e.g. PCA, and differential analysis
• Perform a full workflow for basic analysis of a non-targeted metabolomics experiment, including data preprocessing, metabolite annotation, statistical analysis, and biological interpretation
• Understand quality control and quality assurance in metabolomics and exposomics

Day 1: Half a day on basic principles of metabolomics and high-resolution mass spectrometry are introduced. We will focus on data pre-processing the remainder of the time. After a brief overview of available pre-processing methods, participants will be introduced to MZmine and will work on their own or study data. Day 1 is concluded with a social event. Day 2: Focus on (pre)-processing will be continued working more with MZmine. Data library annotation, data evaluation and data integration via FERMO. Day 3: Focus on metabolite identification and advanced metabolome mining tools, including molecular networking through GNPS and in silico molecular structure annotation through Sirius+CSI:FingerID. Participants will test different metabolite annotation strategies on their own or study data at a nearly full-day workshop. The day will be concluded with a social event to allow for further networking. Day 4: Basic statistical methods for metabolomics data analysis, e.g. differential abundance analysis and Principal Component’s Analysis (PCA). FBmn stats guide will be utilized to facilitate online-statistical software. The participants will get hands-on experience from working on their own or study data in a nearly full-day workshop. Day 5: Focus on data visualization. We will introduce Cytoscape, for comprehensive network visualization for metabolomics data, including integration of metabolite annotation and statistics results. The participants will conclude the full analysis pipeline from metabolomics preprocessing to metabolite annotation and visualization of results.