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High-dimensional data analysis


Admission requirements


This course gives an overview of statistical methods that are used for analyzing high- dimensional data sets in which many variables (often thousands) have been measured for a limited number of subjects. This type of data arises in genomics, where genetic information is measured for many thousands of genes simultaneously, but also in functional MRI imaging of the brain.

The course covers the most important statistical issues in this field, which include:

  • initial processing of the data;

  • model- based differential expression analysis for Gaussian and count data (classical and Bayesian methods);

  • multiple testing (family-wise error rate and false discovery rate control);

  • penalized regression (lasso and ridge); and

  • shrinkage.

Several specific types of high-dimensional data will be discussed and used during the course. Philosophy: Teaching students the adjustments to classical statistical methodology, necessary to tackle high-dimensional data.

Course objectives

Students should be able to perform and understand the most common analysis types on several types of high-dimensional data, and be familiar with the specific issues in important types of high dimensional data sets.


See the Leiden University students' website for the Statistical Science programme -> Schedules

You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.

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For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.

Mode of Instruction

The course consists of a series of lectures and practicals (partly computer practicals, partly exercises).

Assessment method

Grading will be as follows:
80% exam grade
20% homework assignments
For both parts you will need to have at least a 5.5.

Reading list

Literature will be specified during course, no books are required.


From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.

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