Omics data analysis: From quantitative data to biological information, 3 ETC
Application open: April 15
Application deadline: May 16
Responsible teachers: Erik Fredlund & Lukas Orre
Venue: KI Campus Solna and SciLifeLab
The course contains lectures and software demonstrations. The students will participate in a literature study with discussions in seminar groups as well a student exam project. The students will also be able to download and use some of the software in workshops during the course.
- The omics data analysis workflow: from quantitative data to biological information (emphasis on analysis of transcriptomics, proteomics, metabolomics expression data)
- Clinical experimental design and sample selection
- Introduction to data transformation and normalization.
- Introduction to basic statistics in omics data analysis: significance test/p-values/false discovery rate
- Introduction to multivariate statistical analysis (PCA and PLS): Outlier and pattern analysis by PCA, supervised analysis by PLS/O-PLS, finding significantly influential features, data model validity etc.
- Introduction to Gene Ontology and enrichment analysis
- Introduction to network and pathway analysis
- Case studies on clinical biomarker discovery
- 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.
After completed the course, the student will be able to:
- Understand and perform the basics of a data analysis workflow for omics expression data (transcriptomics, proteomics, metabolomics)
- Understand the aspects of study design, experimental planning and sample selection Know how to do basic quality control of data by use of boxplots, PCA etc
- Know what normalization, data transformation etc means and what it does to your data
- Know the principles of some basic statistics such as t-test and false discovery rate
- Know the principles of PCA and PLS, when to apply and how to validate those models
- Use tools for network and pathway analysis
- Use tools for GO annotation/enrichment
PhD students, postdocs. Selection will be based on 1) the relevance of the course syllabus for the applicant’s doctoral project (according to written motivation), 2) date for registration as a doctoral student (priority given to earlier registration date)).
More info: http://kiwas.ki.se/katalog/katalog/kurs/2383