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Lecturer(s)
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Friedecký David, prof. RNDr. Ph.D.
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Kvasnička Aleš, Mgr. Ph.D.
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Course content
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The course requires an understanding of the basic statistical methods presented in the course "Basic Statistical Data Processing". We will move from univariate to multivariate statistical methods and focus on how to visualize them. Typical problems of multivariate statistics (missing values, zeros; data transformations; relationship between variables - correlation) will be discussed. Principal component methods (PCA) and its interpretation (construction, interpretation, graphical outputs - biplot), cluster analysis (dendrogram, self-organizing maps), classification approaches (PLS-DA, OPLS-DA) and their use in diagnosis/prediction will be studied diseases. For these purposes we will work with Statistica and SIMCA software. The course will also focus on statistical methods and procedures used in laboratory diagnostics such as survival analysis, Bland-Altman for comparing two methods, correlation matrices, quality control tools, etc.
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Learning activities and teaching methods
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Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration, Group work
- Attendace
- 26 hours per semester
- Preparation for the Course Credit
- 10 hours per semester
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Learning outcomes
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The aim is to broaden students' knowledge in statistics with a focus on multivariate statistical methods and their visualization
The student will acquire knowledge concerning statistical methods and procedures used in laboratory diagnostics
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Prerequisites
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Knowledge of basic statistics acquired through the course KBC/ZSZD (Basic Statistical Data Processing)
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Assessment methods and criteria
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Student performance, Systematic Observation of Student
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Recommended literature
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Dharmaraja Selvamuthu, Dipayan Das. (2018). Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control.
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Editor: Bernd Mayer. (2011). Bioinformatics for Omics Data: Methods and Protocols.
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Hans-Michael Kaltenbach. (2021). Statistical Design and Analysis of Biological Experiments.
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Shuzhao Li. (2020). Computational Methods and Data Analysis for Metabolomics.
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