Course: Statistical Methods for High-dimensional Data

Course type: elective
Lecturer: Lara Lusa, Ph.D., Associate Professor

Study programme and level Study field Academic year Semester
Applied statistics, second level All modules 1st or 2nd 1st or 2nd

For the timeline see Curriculum .


  • Regular inscription.

Content (Syllabus outline):

  • Characteristics of research in the field of bioinformatics. Statistical peculiarities of research using high-dimensional data. Design of experiments. Graphical representation of data. 
  • Identification of variables associated with an outcome (recurrence of disease, survival time, etc). Multiple testing: family-wise error rate, false discovery rate; parametric and non-parametric approaches. 
  • Evaluation of multivariable functions for outcome prediction.  Methods for variable selection, estimation of the classification function and of predictive accuracy.
  • Interpretation of results (differences between statistical and biological interpretations). 
  • Use of statistical program R and of Bioconductor.       

Objectives and competences:
The research using microarrays (for gene or microRNA expression, SNP analysis, copy number or metilation analysis, etc.) is widespread medicine and biology. The peculiarity of this type of experiments is that they measure thousands of variables while the number of samples included in the study rarely exceeds one hundred. For this reason it is important to use statistical methods that appropriately take into account the high-dimensionality of the data. The aim of this course is to allow the student to work independently with this type of data. The emphasis is on design and analysis of high-dimensional studies.

Intended learning outcomes:
The student knows how to design a study with high-dimensional data in the field of bioinformatics and can select the appropriate methods for the analysis of data. The student correctly interprets the results and can prepare a report that presents them.



Main contact:
e-mail: info.stat (at)

Contact for administrative questions (enrolment, technical questions):
Katarina Erjavec Drešar
University of Ljubljana, Faculty of electrical engineering, Tržaška cesta 25, 1000 Ljubljana.
room num.: AN012C-ŠTU
phone: 01 4768 209
e-mail: katarina.erjavec-dresar (at)