Skip to main content

Pathway analysis: methods and perspectives

Time: Wed 2022-09-28 14.00

Location: Air & Fire, Tomtebodavägen 23A, Solna

Video link:

Language: English

Subject area: Biotechnology

Doctoral student: Gustavo Stolf Jeuken , Genteknologi, Science for Life Laboratory, SciLifeLab

Opponent: Biträdande professor Mika Gustafsson, Linköping University, Faculty of Science & Engineering, Department of Physics, Chemistry and Biology.

Supervisor: Professor Lukas Käll, Science for Life Laboratory, SciLifeLab, Genteknologi

QC 2022-08-25


The amount of data being generated by high throughput molecular biology experiments grows every day, both in quantity and quality. With this comes the desire to have more powerful and comprehensive methods for statistical analysis that have been developed with the nature of this data in mind.

One of the lines of research that has been developed with this specific goal in mind is pathway analysis. Here, pathways are units of information that have been curated in a way that makes biological knowledge of cellular processes available in a programmatic way, and pathway analysis methods make use of this information to help understand the results of high throughput experiments.

This is an exploratory thesis on the field of pathway analysis. I give a brief introduction to the field, what motivated its development, the problems it tries to solve, and some of the proposed statistical methods, together with some discussion on the implications of this type of analysis.

I then present three original works on pathway analysis, each with a different perspective on the task. First, we present a more reliable null model for pathway analysis methods that use functional association networks, which results in better-calibrated statistics. Second, we show how we can combine pathway analysis methods with other statistical methods, such as survival analysis. We applied this method to a large breast cancer cohort and show that in this case pathways provide better prognostic power than individual genes. Third, we leverage concepts from information theory to design an original pathway analysis method that is very sensitive and flexible, while being practically without parameters. Together, all three papers contribute to furthering the field's usefulness and to the understanding of this type of analysis.