C1170 Applied Statistics for Bioinformatics I

Faculty of Science
Spring 2026
Extent and Intensity
2/2. 5 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
Mgr. Veronika Horská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Michaela Wimmerová, Ph.D.
National Centre for Biomolecular Research – Faculty of Science
Contact Person: RNDr. Tomáš Raček, Ph.D.
Supplier department: National Centre for Biomolecular Research – Faculty of Science
Prerequisites
Basic knowledge of mathematics at a level of secondary education.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course aims to familiarize students with basic statistical methods and principles, and to provide sufficient practice in applying these methods to real bioinformatics data using the statistical software R.
Learning outcomes
At the end of this course, the student will be able to:
- design own experiment, including setting the aims of the experiment;
- define statistical hypotheses, which testing will fulfill the experiment's aims;
- collect data and clearly process all the necessary materials for performing data analysis;
- use appropriate statistical methods to obtain an initial overview of the data set;
- perform correct data analysis to verify the statistical hypotheses;
- infer valid statistical conclusions from the results of data analysis;
- correctly interpret statistical conclusions in the field of bioinformatics;
- show results of a data analysis clearly in a presentation.
Syllabus
  • Introduction to applied statistics, motivation and aims of the course, examples of real bioinformatics data and their data analysis performed in R software, and basics of working with R software.
  • Data types; exploratory data analysis: data visualization, numerical characteristics.
  • Selected probability models for discrete random variables.
  • Selected probability models for continuous random variables.
  • Introduction to hypothesis testing, one-dimensional and two-dimensional tests of normality.
  • One-sample parametric tests: of mean, variance, correlation coefficient, and probability.
  • Two-sample parametric tests: of two means, two variances, two correlation coefficients, two probabilities, and the odds ratio.
  • One-sample and two-sample non-parametric tests of medians.
  • Design of experiment, data collection, and processing of data for data analysis.
  • Presentation of data analysis results using LaTeX software.
Literature
    recommended literature
  • SHAHBABA, Babak. Biostatistics with R : an introduction to statistics through biological data. Dordrecht: Springer, 2012, xvi, 352. ISBN 9781461413011. info
  • CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 8131503941. info
  • BUDÍKOVÁ, Marie; Tomáš LERCH and Štěpán MIKOLÁŠ. Základní statistické metody. 1. vyd. Brno: Masarykova univerzita, 2005, 170 pp. ISBN 978-80-210-3886-8. info
Teaching methods
Lectures: 2 hours per week; theoretical lectures focused on familiarization with statistical terms and methods.
Exercises: 2 hours per week; practical exercises on computers focused on the application of statistical methods in the analysis of bioinformatics data using statistical software R.
Assessment methods
Requirements for registration for the exam: continuous semester work consisting of active attendance at seminars (2 unexcused absences are permitted); completion and submission of three homework assignments reflecting the material covered in the seminars.
Exam: final semester written assignment (minimum 50% required); presentation of statistical analysis results (approx. 10 min), which the student prepares either using their own data or provided data.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught every week.
  • Enrolment Statistics (recent)
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