During this three-day intensive course with embedded workshops, you will widen your scientific knowledge and develop your skills in omics technologies. You will also expand and strengthen your professional network in this area..

After an opening session that sets the stage for the importance of omics technologies from the bench to the clinic, we will take an overview of these technologies. After the opening session, three concurrent workshops will give you the opportunity to delve into metabolomics, proteomics or transcriptomics. These workshops will be tailored for this target audience and will provide hands-on activities in addition to short lectures. Each workshop will be delivered in three parts (day 1, day 2, and day 3 of the course). Read more about the workshop options and report your preferences on your application form. Each workshop can accommodate 8-10 participants.

Workshop A – Metabolomics

This workshop aims to deliver a comprehensive, practice-oriented introduction to metabolomics and lipidomics analyses. Participants are guided through the full analytical and data-science workflow, from study design to biological interpretation, working hands-on with real raw LC-MS data f in every session. It emphasizes key decisions that ensure data quality, interpretability, and scientific impact in LC/MS-based studies, combining short lectures with extensive hands-on activities. The workshop runs for three days.

Day 1: A Focus on Quality

Participants are introduced to the conceptual and practical foundations of metabolomics and lipidomics study design, including formulation of biological questions and hypotheses, experimental and cohort design, sample randomization, batch structure, and strategic use of quality control samples. Key principles of sample preparation, LC/MS analysis, and data acquisition are covered, with emphasis on technical variation, signal drift, common analytical pitfalls, and their critical impact on downstream analyses.  Participants will throughout the course, work with raw LC-MS data, performing feature extraction using MZmine, followed by filtering, annotation, normalization, transformation, missing value handling, noise reduction while preserving biological signal, and thorough quality assessment to produce high-quality datasets.

Day 2 – Differential Expression

Building directly on the cleaned and quality-controlled raw data from Day 1, participants perform explorative data analyses and statistical testing commonly used in metabolomics, including principal component analysis, hierarchical clustering, univariate and multivariate analyses. The focus lies on differential abundance testing, strategies for handling remaining technical artifacts, and interpreting results in the context of the original study design and biological hypotheses, all using the real-life LC-MS dataset.

Day 3 – Enrichment and Data presentation

Continuing with the same real raw-to-processed LC-MS data, the final part focuses on data visualization, biological interpretation, and effective communication of results. Participants explore metabolite and lipid annotation, pathway-level enrichment analysis, integration into broader biological frameworks, and common visualization strategies, such as principal component analysis, volcano plots, and heatmaps. Best practices for reporting, reproducibility, and transparent analysis are integrated throughout.

Learning Objectives

By the end of this workshop, participants will be able to:

• Design robust metabolomics/lipidomics studies aligned with biological questions
• Extract and process high-quality LC/MS data from real raw files through hands-on practice, managing technical variation, normalization, and filtering
• Apply common data analysis tools and interpret statistical analyses with emphasis on differential abundance
• Visualize results, perform pathway enrichment, and communicate findings in a reproducible, biologically meaningful way
• This workshop offers a unique, end-to-end experience with genuine raw data, seamlessly bridging theory and real-world application in metabolomics and lipidomics research.

Workshop B – Single Cell Sequencing and Transcriptomics
Day 1: A Focus on Quality

This hands‑on workshop introduces participants to the essential steps required to transform raw 10x Genomics single‑cell RNA‑seq data into high‑quality, analysis‑ready datasets. Working directly with Cell Ranger and CellBender outputs, participants will learn how to evaluate data quality, interpret key QC metrics, and apply appropriate filtering strategies. Through comparison of high‑ and low‑quality datasets, the workshop highlights how technical issues such as ambient RNA, doublets, and low‑viability cells manifest in practice and how they influence downstream analysis.

Participants will perform normalization, identify variable genes, and run dimensionality reduction and basic clustering using Seurat. We also introduce the principles behind batch effects and when batch correction may be required. The workshop is structured around guided R Markdown notebooks with multiple checkpoints to support different experience levels.

By the end of the session, participants will understand the rationale behind QC filtering, be able to recognize common technical artifacts, and know how early preprocessing decisions shape downstream analyses.

Day 2 – Differential Expression Analysis in Single-Cell RNA-seq: From Cell-Level Tests to Pseudobulk Strategies

This workshop introduces participants to differential expression analysis (DEA) in single-cell RNA-sequencing data using Seurat and complementary bulk RNA-seq frameworks such as DESeq2. We will explore how to identify cluster markers, compare biological conditions within cell populations, and account for experimental covariates. Participants will learn the conceptual differences between cell-level and pseudobulk approaches, including their assumptions, strengths, and limitations. Hands-on exercises using pre-prepared R Markdown workflows will guide attendees through practical analyses and interpretation. By the end, participants will be better equipped to design, perform, and critically assess DEA in single-cell studies.

Day 3 –Enrichment and Data presentation in Single-Cell RNA-seq: Detection of Biological Pathways and Data Visualization.

This workshop introduces core strategies for visualizing, interpreting, and presenting single‑cell RNA‑seq (scRNA‑seq) results using Seurat and complementary R packages. Participants will learn how to generate and explain PCA/tSNE/UMAP projections, create publication‑ready plots (UMAPs, heatmaps, dot/feature plots), and move from gene lists to biological functions using gene set enrichment analysis (e.g., clusterProfiler). We cover ontology‑based visualization (GO/KEGG) and single‑cell–specific tasks such as cell type annotation (manual and automated with SingleR) and per‑cluster pathway activity. We also introduce trajectory analysis, showing how pseudotime methods can reveal continuous cellular transitions and how to interpret these dynamics within single‑cell embeddings. Hands‑on R Markdown workflows will guide participants through best practices and reproducible reporting. By the end of the workshop, participants will be able to confidently produce informative visualizations, perform functional enrichment analyses, annotate cell populations, and communicate single‑cell results in a clear and biologically meaningful way.

Workshop C – Proteomics

Proteomics has become a central technology for understanding biological systems, disease mechanisms, and molecular responses to interventions. However, successful proteomics studies require careful integration of experimental design, sample preparation, mass spectrometry acquisition strategies, and robust data analysis.

This workshop introduces the conceptual and practical foundations of modern proteomics workflows, from sample preparation to biological interpretation. Participants will learn about different proteomics strategies, including global proteomics, post-translational modification analysis, secretomics, and spatial proteomics, and how to select appropriate approaches for different biological questions.

The workshop will also introduce core principles of proteomics data analysis, including differential abundance testing, visualization, and pathway enrichment. In the final session, participants will apply these concepts in a group exercise where they design a complete proteomics experiment to address a biological question.

Day 1: From Biological Samples to Mass Spectrometry

This session introduces the foundations of proteomics workflows, focusing on how biological samples are converted into measurable peptide signals using LC-MS/MS. Participants will learn the key steps involved in sample preparation, protein digestion, peptide separation, and mass spectrometry acquisition. Emphasis will be placed on experimental design considerations that influence data quality and reproducibility.

Topics

• Overview of proteomics technologies
• Sample preparation workflows
• Protein extraction and digestion
• LC-MS/MS principles
• Data-dependent vs data-independent acquisition
• Importance of experimental design and biological replication

Learning Goals

Participants will be able to:

• Understand the core steps of a proteomics workflow
• Recognize how sample preparation affects proteomics data quality
• Understand basic principles of LC-MS/MS
• Identify key experimental design considerations before starting a proteomics study

Day 2: Proteomics Strategies and Quality Control

This session focuses on different proteomics strategies and how they address distinct biological questions. Participants will learn about approaches such as global proteomics, PTM analysis, secretomics, spatial proteomics, and single-cell or single-fiber proteomics. The session will also introduce essential quality control metrics and experimental considerations that ensure reliable data generation.

Topics

• Global proteomics
• PTM proteomics (e.g., phosphoproteomics)
• Secretomics
• Spatial proteomics
• Single-fiber / single-cell proteomics
• Experimental controls and technical replicates
• Quality control metrics and batch effects

Learning Goals

Participants will be able to:

• Match biological questions to appropriate proteomics approaches
• Understand advantages and limitations of different proteomics strategies
• Recognize key quality control metrics in proteomics experiments
• Identify common sources of technical variability in proteomics datasets

Day 3: Data Analysis, Visualization, and Biological Interpretation

This session introduces core computational approaches used to analyze proteomics data. Participants will learn principles of differential abundance testing, common data visualizations, and functional enrichment analysis. The session concludes with a group exercise where participants design a proteomics experiment and present their strategy.

Topics

• Data normalization and preprocessing
• Differential abundance analysis (e.g., limma)
• Visualization in R (volcano plots, heatmaps, UpSet plots)
• Gene set enrichment analysis
• Integration with other omics datasets
• Experimental design group exercise

Learning Goals

Participants will be able to:

• Understand basic statistical frameworks used in proteomics
• Interpret common visualizations used in proteomics studies
• Apply pathway enrichment analysis to proteomics datasets
• Conceptually integrate proteomics with other omics layers
• Design a proteomics experiment addressing a biological question

The course will then conclude with group presentations from the workshops, two short talks on data management, and an inspirational closing keynote talk.

As we believe that interactivity improves learning outcomes, we expect you to be actively involved in the workshops and discussions with other participants and speakers throughout the event.

Speakers:

• Andreas Abildskov Thomsen, Postdoc, University of Southern Denmark (DK)
• Atul Shahaji Deshmukh, Assistant Professor, University of Copenhagen (DK)
• Clara Drachmann, Bioinformatician, University of Copenhagen (DK)
• Jesper Havelund, Metabolomics Researcher, University of Southern Denmark (DK)
• Lars Roed Ingerslev, Staff Scientist, University of Copenhagen (DK)
• Laura Johanne Grønholt, Academic Research Officer, University of Copenhagen (DK)
• Mohamed Hassan, PhD Student, Aarhus University (DK)
• Nigel Kilty Kurgan, Postdoc, University of Copenhagen (DK)
• Pauline M. Møller, Postdoc, University of Southern Denmark (DK)
• Pavel Shliaha, Advanced Research Fellow, Imperial College London (UK)
• Roger Moreno Justicia, Postdoc, University of Copenhagen (DK)
• Sikander Hayat, Associate Professor, RWTH Aachen (DE)
• Tugce Karaderi, Group Leader, Associate Professor, University of Copenhagen (DK)

Organisers:

• Hongling Liu, PhD Student, University of Copenhagen (DK)
• Joanna Kalucka, Associate Professor, Aarhus University (DK)
• Morten Dall, Platform Manager, University of Copenhagen (DK)
• Morten Svarer Hansen, Clinical Researcher, University Hospital of Southern Denmark, Esbjerg (DK)
• Nils Færgeman, Professor, University of Southern Denmark (DK)

We welcome early-career researchers (PhD students, postdocs and similar) in basic, translational, clinical or interdisciplinary research in fields related to endocrinology and metabolism. Early-career researchers from other fields are also welcome to apply.

Participants are expected to be able to code in Python or R , and bring a laptop with pre-specified packages installed. You do not need to be familiar with analyses of omics data sets, but you must be comfortable enough with coding to be able to take instructions.

To apply for a seat, you must submit a motivational statement (why do you want to take this course) and answer a self-assessment of Python or R programming skills. If you are selected for participation, you are expected to participate actively in the talks, workshops and group work, contributing to a vibrant learning environment.

After the registration deadline, all applicants will be informed whether or not they have been allocated a seat. Notification will be by email from DDEA no later than the end of April 2026.

Due to the limited number of seats, you are not guaranteed a seat until you receive an e-mail with a confirmation from DDEA. Seats in the course will be prioritised based on motivational statement and level of programming skills. S

Several seats are reserved for DDEA grant recipients.

We welcome applicants from research institutions outside of Denmark, as we always reserve some seats for PhD students from outside of Denmark.

DDEA reserves its right to select participants based on the defined requirements.

Dinner registration
DDEA organizes networking dinners on 2 June and 3 June. Participation in the dinners is free of charge. Please sign up for the dinner upon registration, and indicate whether you have any dietary requirements.

Accommodation
DDEA offers accommodation (check-in on 2 June, check-out on 4 June) for participants living outside of the Horsens area. Please sign up for accommodation when you register for the event. You will be informed about your overnight accommodation by the DDEA after the registration deadline.

Museum tour
DDEA will organize a group tour of the Horsens Prison Museum. Going on the tour is optional. Please tick this box if you would like to sign up for the tour (registration for the museum tour is binding).

Certificate of Attendance
A course certificate of attendance and participation can be issued upon request at the end of the course. Full participation in the course is required to receive the certificate. Apply to your PhD school for ECTS credits with the certificate and course programme.

Latest cancellation date & no-show fee
Please note that it is free of charge to participate in the event, however DDEA will charge a no-show fee of 1500 DKK if you do not show up and have not unregistered from the event by 10 May 2026.