The aim of the course is to cover some of the fundamental aspects of metabolomics from the “data analyst” point of view. We will  cover all the key aspects which have to be considered to set-up a successful metabolomics investigation, from the practical issues related to study/analytical design to data pre-processing and statistical analysis. The course will be delivered relying on a mixture of lectures, computer-based practical sections, and group discussions.

Familiarity with R will be assumed. A full day of the course will be however devoted to a fast introduction to data carpentry and visualization in R. A basic experience in metabolomics will be welcomed.

The objective of the course is to make participants familiar with the analysis analysis of metabolomic data (targeted and untargeted) in R. The course will also constitute an excellent primer to the application of univariate and multivariate statistics to complex datasets.

Tuesday (9:30 AM – 1:30 PM Berlin time)

Introduction to Metabolomics

• What is metabolomics? (targeted and untargeted)

• Why metabolomics?

• Study design considerations

• Analytical chemistry in metabolomics

• Integrating data analysis and data collection

• Data sharing and reproducibility

• Group activity: Design your study

Wednesday (9:30 AM – 1:30 PM Berlin time)

From Zero to R

• Introduction to R and RStudio

• Visualizing your data

• Data carpentry (practical session)

• Multivariate visualization using PCA

Thursday (9:30 AM – 1:30 PM Berlin time)

Untargeted Metabolomics

• Preprocessing of MS-based untargeted metabolomics data

• The pre-processing workflow

• Hands-on: Data preprocessing in R with xcms

• Quality assessment: Are my data OK?

• Handling missing values and imputation

• From features to compounds: Annotation

Friday (9:30 AM – 1:30 PM Berlin time)

Analyzing a Metabolomics Data Matrix

• Univariate approach: Introduction to statistical testing and modeling

• Multiple testing correction

• Multivariate approaches: PCA, PLS, Random Forest

"The course was very interesting, with plenty of interaction both among us students and with Pietro. Pietro explained everything clearly, making complex concepts easier to grasp. He didn’t just focus on the mechanical functioning of individual scripts, but also delved into the fundamental principles of how to evaluate and handle data even before analyzing it. I can say that I came out of this course feeling more enriched and more confident, having filled many of the gaps I had beforehand."

"Yes, Pietro is a wonderful teacher and it is a pleasure to listen to him, the course is engaging."

1 - Missing Data in R - ONLINE, 22-24 April

2 - Multivariate data analysis with R & vegan - ONLINE, 4-7 May

3 - Machine Learning for Multi-Omics Integration - ONLINE, 21-23 September

Cancellation Policy:

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.