270239 PR Data Science in Metabolomics in R (2024W)

This course will be taught in English.

Background: Modern LC-HRMS based metabolomics or proteomics studies generate large experimental datasets (several GB of raw data). Especially in hypothesis-generating experimental approaches it is impossible to manually investigate this data or to manually process it in an efficient, controlled and reproducible and thus consistent manner. For this, specific and tailored software tools and programs need to be used. Furthermore, the users of these tools need to know their inner working mechanisms (algorithms) in order to efficiently and reliably evaluate and judge the outcome of such a data evaluation.

Aim: In this lecture the students will get to know the fundamental approaches of untargeted metabolomics or proteomics experiments based on LC-HRMS measurements. Using a large metabolomics dataset, the students will carry out the initial data processing of the raw-data (peak-picking, grouping, optional retention-time alignment, integration of peak areas) with XCMS in the programming language R. The students will gain an understanding of and optimize the parameters and functions of the XCMS software package and be able to visualize and evaluate the detected compounds. Moreover, they will be able to export them into a comprehensive data matrix. Additionally, a rough overview of basic and currently frequently used statistical methods, which can be used to generate new biological hypothesis, will be presented.

Contents: The lecture will cover the following topics:
• Introduction to the programming language R
• Import of LC-HRMS datasets into R
• Algorithms and functions of XCMS and CAMERA
• Explanation of parameters of XCMS
• Semi-automated optimization of the used data processing parameters to improve the analysis
• Export of the detected compounds into a data matrix
• Brief overview of basic and advanced statistical methods using the evaluated dataset
The course is organized into a lecture part as well as practical work in R, where the students will evaluate the dataset themselves.

Methods: This course will be conducted via presentations, practical work either alone or in form of small groups, discussions between the students and student presentations, among others.

Note: The students will use a central R-Installation. Each student needs to bring a laptop, which can access the university’s network.

Prerequisites:
• Confident handling of PCs
• Confidence with MS Office Excel with a special focus on formulas
• Knowledge about LC-HRMS datasets