Omics data analysis: From quantitative data to biological information, 3 ETC

Important dates

Application open: April 15

Application deadline: May 16


Responsible teachers: Erik Fredlund & Lukas Orre

If you have questions about the course please contact or

Venue: KI Campus Solna and SciLifeLab

Course content

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.

Topics covered:

  • 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.

Learning outcomes:

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

Entry requirements

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)).

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