OMICs Course Introduction
Ehsan Pishva, MD, PhD. Maastricht University
Assistant Professor | Dementia Systems Biology
Department of Psychiatry and Neuropsychology

This lecture provides an introduction to omics, tracing the evolution from genome-wide association studies (GWAS) to a broader multi-omics perspective. It highlights the limitations of single-layer analyses and the growing need to integrate diverse omics modalities such as transcriptomics, epigenomics, proteomics, and metabolomics. Emphasis is placed on the importance of cell type specificity for understanding biological mechanisms and disease processes. The lecture also explores recent technological innovations that enable high-resolution, large-scale data generation. Finally, it discusses analytical advances, including computational and machine learning approaches, that facilitate integrative and interpretable multi-omics research.

 

Transcriptomics
Dr. rer. nat. Daniela Esser
UKSH (Kiel)

The transcriptomic session provides an introduction to the analyses of RNA-Seq data (scSeq and bulk). Participants will learn the principles of transcriptomic analysis workflows, including experimental design, data generation, preprocessing, and downstream analysis. Emphasis will be placed on quality control, normalization, and data visualization. By the end of the course, attendees will be equipped to critically interpret transcriptomic results in scientific publications or in their own experiments, communicate effectively with bioinformaticians, recognize common pitfalls, and design experiments. 

Proteomics 
Ap.Prof. Priv.Doz. DI Dr. Klaus Kratochwill 
Head of Core Facility Proteomics

The proteomics session introduces participants to the principles and practical workflows of proteomics data analysis, with a focus on mass spectrometry–based approaches. The session will consist of a general introduction and insights into processing label-free proteomics data, including quality control, normalization, and differential expression analysis using established computational tools. Emphasis is placed on critical evaluation of analytical choices, such as handling missing data and controlling false discovery rates, particularly in the context of limited statistical power. 

Lipidomics
Dr. Daan van Kruining, Maastricht University

Lipids form a highly diverse group of biomolecules with essential roles in cellular structure, signalling, and metabolism, and their systematic profiling by mass spectrometry has become an increasingly valuable approach in biomedical research. This session covers a general introduction to lipidomics and the analytical workflow from mass spectrometry data to biological interpretation, guiding participants through key steps including data cleaning, normalisation, and quality control. Particular attention is given to the structural complexity of the lipidome and how this shapes analytical decisions, from handling missing values to performing meaningful comparisons at both the individual lipid and lipid class level using established computational tools.
 

Statistics & Integration of Multi-OMICS
Dr. rer. nat. Daniela Esser
UKSH (Kiel)

The session on Statistics & Integration of Multi-OMICS data introduces practical approaches for the integration of multi-omics data, including transcriptomics, proteomics, metabolomics, and other high-dimensional datasets. Participants will gain a basic understanding of the challenges and opportunities associated with combining heterogeneous data types, as well as key strategies for data preprocessing and integration. The course will highlight important statistical considerations, particularly the differences between analyzing large-scale datasets and small sample cohorts, including issues such as overfitting, multiple testing, and limited statistical power. By the end of the course, attendees will be better prepared to design and interpret multi-omics studies.