Rebecka Jörnsten             

Professor of Biostatistics and Applied Statistics

Division of Applied Mathematics and Statistics

Mathematical Sciences
University of Gothenburg/Chalmers

Main menu: Research | | Publications & Presentations | | Teaching | | R codes and packages | | People

What's new?

PhD position. I am looking for a new PhD student to work on estimation and validation of high-dimensional models in cancer genomic. We are particularly interested in (i) the integration of data sources (gene expression, methylation, copy number variation, mutations), (ii) the development of disease-comparative models; and (iii) the derivation of formal validation tools for comparative models at different levels of detail.

I am looking for someone with strong mathematical, statistical AND computational skills. Contact me via email for further details - please include a CV and a personal reference (advisor or employer) with your email.

Research Projects

My research centers on the development of new statistical methodology for network modeling, clustering and model selection, with applications to high-dimensional biological data. I am particularly interested in integrating techniques from information theory into new tools for statistical model selection and high-dimensional data exploration. Efforts in this area include Simultaneous model selection via rate-distortion which allows for the identification of genes and gene clusters associated with interpretable models derived from the experimental design. This work has been extended to data integration, involving mRNA expression, protein metabolic data and pathway information (work with former student Alexandra Jauhiainen ).

Data integration and joint modeling is a rich source for research problems. These types of problems are central components in several joint projects now underway in collaboration with Sven Nelander's group. Our group aims to formulate integrated models for mRNA, microRNA, DNA copy number, methylation and mutation in human cancer.

In addition to projects stemming from systems biology problem, my students and I are also investigating statistical clustering methodology, particularly how subsets of features play in role in the formation of clusters of observations. These projects constitute continuations of my research into mixture modeling, data depth and missing value imputation (see publication list).

Collaborative projects

My research is often motivated by my collaborative projects. The best thing about being a statistician is that you get the opportunity to work with people from other disciplines.

I work closely with the Nelander lab on problems pertaining to network modeling of cancer, data integration in cancer genomics, and the identification of therapeutic targets.
I work with Gunnar Steineck's group, investigating long-term effects of chemotherapy. I derive computational phenotypes from factor analysis of patient self-assessment data.

With Mikael Benson and Mika Gustavsson at Linköping university, I work on data integration problems, looking for disease causing (biomarker) genes using both mRNA expression, SNP, and PPI data.

Together with scientists at the Center for Brain Repair and Rehabilitation (CBR), I explore the therapeutic impact of music.

R, Sweave and Reproducible research

I am an avid fan of the R project and Bioconductor.
The impact of statistical research is greatly increased if easy-to-use implementations are available. I am an editor for Journal of Statistical Software where R and Matlab packages and other computational solutions are published.

The power of R is that current research is often available as packages almost immediately upon the publication of the methodology in a journal. This leads to a fluidity of ideas and implementations and, more importantly, is a key component of reproducible research.

Here is a short lecture about the integration of R and LaTeX (R-Sweave) for dynamic report writing. I am so sold on this idea that I am implementing most of my lecture notes in R-sweave. My students can thus reproduce lecture notes and coding demos at home.


Teaching Philosophy

My classes is usually made up of a mix of students; undergraduates, master students and PhD students and all from different fields of study. I enjoy this kind of dynamic classroom.
I tend to mix black-board lectures with computer demonstrations for all my classes. My goal in teaching is that the students leave my class recognizing that statistical modeling is not a "push-the-button" type exercise, and every data set requires unique consideration.

Courses & Workshops

Current courses:

I cycle teaching applied statistics couses (Linear models, Applied multivariate analysis) and method courses (Statistical inference principles and Survival analysis).
I also teach PhD courses, sometimes jointly with upper division masters programs (Sparse modeling, Empirical Bayes, Bootstrap methods). Here are some links to recent courses:


    PhD students
  • Jonatan Kallus 2010-present Integrative modeling of cancer.
  • Jose Sanchez, 2009-2014 ​Network models with applications to genomic data: generalization, validation and uncertainty assessment Currently working as an analyst at Astra Zeneca
  • Alexandra Jauhiainen 2009-2010 Statistics in Gene Expression, Metabolomics, and Comparative Genomics in Evolution (co-supervisor). Currently working as principal statistician at Astra Zeneca
    Current Master students
  • As supervisor: Oskar Lilja, Maja Fahlen, Sebastian Franzen, Filip Birve, Linus Lundin
  • As examiner: Ludvig Wikström, Emilio Jorge, Pasha Hashemi, Björn Herder, Nils Wireklint