Course catalogue doctoral education - VT24

  • Application can be done between 2023-10-16 and 2023-11-15
Application closed
Title Omics Data Analysis: From Quantitative Data to Biological Information
Course number 3102
Programme 1-Included in several programmes
Language English
Credits 3.0
Date 2023-11-20 -- 2023-12-01
Responsible KI department Department of Oncology-Pathology
Specific entry requirements Prior knowledge of the statistical programming language R is not a requirement, but may prove useful.
Purpose of the course During recent years omics data has become an integral part of many biomedical and clinical research projects. This broad introductory course aims at bridging the gap between classical biomedical research, omics technologies and bioinformatics. The course will enable students to get an introduction to omics technologies and basic knowledge of omics data analysis workflows.
Intended learning outcomes After completed course, the student will be able to:
* Understand the principles and perform the basics of high-throughput technologies (genomics, transcriptomics, proteomics) and the omics data analysis workflow
* Understand the principle aspects of study design, experimental planning and sample selection
* Perform basic quality control of data by use of boxplots, principal component analysis (PCA) etc
* Explain what normalization and other forms of data transformation means and what it does to your data
* Understand the principles of, and be able to apply, basic statistics such as t-test and false discovery rate
* Understand the principles and applications of, and be able to apply, dimensionality reduction methods such as PCA and t-distributed stochastic neighbor embedding (t-SNE) / uniform manifold approximation and projection (UMAP) to omics data
* Use tools for hierarchical clustering, functional enrichment and pathway analysis
* Use tools for gene ontology (GO) annotation/enrichment
* Create informative and clear visualizations of omics data
Contents of the course * The omics data analysis workflow: from quantitative data to biological information (emphasis on analysis of quantitative Omics-data (e.g. proteomics, transcriptomics))
* Introduction to omics technologies and data structures
* Omics experimental design and sample selection
* Introduction to data transformation and normalisation
* Introduction to basic statistics in omics data analysis: significance test/p-values/multiple testing correction/false discovery rate
* Introduction to dimensionality reduction PCA/t-SNE/UMAP
* Introduction to GO and enrichment analysis
* Introduction to correlation analysis and hierarchical clustering
* Introduction to network and pathway analysis
* Introduction to online bioinformatics resources and analysis tools
* Introduction to the R statistical programming language
* Introduction to data visualization
* Literature study with a critical view on how omics data is analyzed in clinical research.
* Current state of the art in omics data analysis is highlighted through case studies, literature studies and demonstrations
Teaching and learning activities The teaching activities for the course will be based on lectures, workshops and data analysis cases. The students will participate in a literature study with discussions in seminar groups as well as an independent data analysis exam project. The students will also be able to download and use some of the software in workshops during the course.
For the final data analysis workshop students may be able to work on their own datasets, or datasets will be provided for them. The R statistical programming language will be used extensively in the course.
Compulsory elements * Attendance on lectures and data analysis demonstrations.
* Attendance to literature study discussion seminar.
* Attendance to examination seminar and hand in the written examination assignments.
* Extra written literature study can be used to compensate absence.
Examination The course assessment is based on two types of assignments: a literature study with a critical view on an omics data analysis subject performed in groups and an individual written omics data analysis project illustrating the different topics covered during the course.
Literature and other teaching material Recommended literature will consist of recent original and review articles and will be provided to students as PDF files at the Canvas learning platform.
Number of students 12 - 24
Selection of students Selection will be based on 1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation), 2) start date of doctoral studies (priority given to earlier start date)
More information The course is given jointly by the doctoral programmes Allergy, immunology and inflammation (Aii), Tumor Biology and Oncology (FoTO), Biology of Infections and Global Health (BIGH) and Development and Regeneration (DEVREG). See: . The course is full time (Mon-Fri, 9-16) during two weeks and will be given on Campus Solna, KI.
Additional course leader
Latest course evaluation Course evaluation report
Course responsible Haris Babacic
Department of Oncology-Pathology
Contact person Ioannis Siavelis
Institutionen för onkologi-patologi