Gene Expression Data Analysis - M143004
Title: Analýza genové exprese
Guaranteed by: Department of Informatics and Chemistry (143)
Faculty: Faculty of Chemical Technology
Actual: from 2019 to 2025
Semester: summer
Points: summer s.:4
E-Credits: summer s.:4
Examination process: summer s.:
Hours per week, examination: summer s.:2/1, C+Ex [HT]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Level:  
Note: enabled for web enrollment
Guarantor: Kolář Michal Mgr. Ph.D.
Classification: Biology > Genetics
Interchangeability : N143049
Examination dates   
Annotation -
In this course, students will be introduced to the basic types of functional genomic data. These data primarily include data obtained by quantification of specific nucleic acids using polymerase chain reaction (RT-qPCR), data from DNA microarray profiling, and data obtained through high-throughput sequencing. Students will also be introduced to data preprocessing procedures, including the removal of technological artifacts and transformation into standardized form. They will become familiar with specific methods of exploratory data analysis and with statistical methods used in the analysis of multidimensional genomic data. Biological interpretation of the results will be demonstrated using gene ontologies. Students will also learn methods for data visualization and data archiving. Practical sessions will be conducted in the R scripting language, and acquired knowledge will be deepened using real datasets and commonly used analytical tools and database resources.
Last update: Kolář Michal (11.02.2026)
Course completion requirements -

During the semester, students complete and submit assigned tasks. At the end of the semester, students present a semester project and take a written examination, which may be followed by an oral examination if necessary.

Last update: Kolář Michal (11.02.2026)
Literature -

Recommended:

  • Z: Pfeiferová L, Kolář M, Svatoňová P, Novotný J, Dohnalová H, Pačes J (2022) Bioinformatika – Základy, University of Chemistry and Technology, Prague.

    D: Irizarry RA, Love MI (2021) Data Analysis for the Life Sciences, Leanpub. Online: http://leanpub.com/dataanalysisforthelifesciences

    D: Real-time PCR Handbook (2016) Thermo Fisher Scientific Inc.

    D: Wickham H, Grolemund G (2017) R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media. Online: https://r4ds.hadley.nz

    D: Love MI, Huber W, Anders S (2014) Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2. Genome Biology, 15, 550.

    D: Orchestrating Single-Cell Analysis with Bioconductor (OSCA) (2026) Bioconductor Online Book. Online: https://osca.bioconductor.org/

Last update: Kolář Michal (12.02.2026)
Syllabus -

1. Introduction: Types of functional genomic data. Objectives of analyses.

2. Preprocessing of RT-qPCR data: Standard curve. Amplification curve. Threshold cycle. Background correction. Data normalization.

3. Preprocessing of transcriptional microarray data: Background noise removal. Data normalization. Relative and absolute quantification. Variance stabilization. Data summarization.

4. Preprocessing of high-throughput sequencing data: Sequencing reads. Read mapping. Read counts. Data quality control.

5. Single-cell transcriptomics and spatially resolved analyses: Analysis of cell populations. Trajectories. Spatial heterogeneity.

6. Exploratory data analysis: Dimensionality reduction. Clustering. Quality control.

7. Statistical data analysis: Linear models. Statistical tests. Multiple testing problem.

8. Experimental design: Method selection. Controls. Randomization. Replication.

9. Annotation and archiving of results: Genome browsers. Functional genomics databases.

10. Biological interpretation of results: Gene set enrichment analysis (GSEA). Signaling pathway databases. Gene ontologies.

11. Additional applications of discussed methodologies: Analysis of single nucleotide polymorphisms and chromosomal aberrations (SNP, CNV, LOH). DNA methylation analysis. Identification of transcription factor binding sites.

Last update: Kolář Michal (12.02.2026)
Learning outcomes -

Students will be able to:

  • Process functional genomic data from RT-qPCR, DNA microarrays, and high-throughput sequencing.
  • Validate laboratory experiment results using exploratory data analysis.
  • Perform statistical analysis of functional genomic data and carry out their biological interpretation.
  • Design experiments with consideration of technological artifacts specific to individual methods.

Last update: Kolář Michal (12.02.2026)
Registration requirements -

Biochemistry, Molecular Genetics, Programming in R

Last update: Kolář Michal (12.02.2026)
Teaching methods
Activity Credits Hours
Obhajoba individuálního projektu 1 28
Účast na přednáškách 0.5 14
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi 0.5 14
Práce na individuálním projektu 0.5 14
Příprava na zkoušku a její absolvování 1.5 42
4 / 4 112 / 112