Bi1121c Data analysis in R for experimental and molecular biologists - practical course

Faculty of Science
Spring 2025
Extent and Intensity
0/2/0. 2 credit(s). Type of Completion: z (credit).
Teacher(s)
Mgr. Petra Ovesná, Ph.D. (seminar tutor)
Mgr. Kristína Gömöryová, Ph.D. (seminar tutor)
Mgr. Radek Fedr (seminar tutor)
Mgr. Antónia Mikulová (seminar tutor)
Guaranteed by
prof. Mgr. Vítězslav Bryja, Ph.D.
Department of Experimental Biology – Biology Section – Faculty of Science
Contact Person: prof. Mgr. Vítězslav Bryja, Ph.D.
Supplier department: Department of Experimental Biology – Biology Section – Faculty of Science
Prerequisites
NOW( Bi1121 Data analysis in R for EMB )
No prerequisities, however students are required to enroll at the same time to Bi1121 (lecture).
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 24 student(s).
Current registration and enrolment status: enrolled: 0/24, only registered: 17/24, only registered with preference (fields directly associated with the programme): 0/24
fields of study / plans the course is directly associated with
Course objectives
The main aims of this course are to teach the students from biological backgrounds (i) fundamentals of programming in R, (ii) how to perform basic data operations, visualizations, statistical analysis and reporting, (iii) fundamentals of proper experimental design and how to conduct research reproducibly, (iv) introduction to omics data evaluation (including mass spectrometry, scRNA-seq), flow cytometry and microscopy data analysis.
Learning outcomes
At the end of the course, students will be able to independently design biological experiments using high-throughput technologies, as well as evaluate and report the results of such analysis using the R language.
Syllabus
  • 1. Introduction – installation of R, RStudio, introduction to base R 2. Data manipulation – data input, data types, transformation, summarization, dplyr package 3. Graphics in R – plotting graphs in base R, ggplot2, interactive visualizations; types of graphs in molecular biology 4. Introduction to statistical methods – principles of statistical thinking, confidence intervals, common statistical tests and models 5. Experimental design, power analysis 6. Reproducibility – git, GitHub, R Markdown, workflow 7. Basics of image data processing – point transformation, animations 8. Artificial intelligence and machine learning – dimensions reduction, clustering, neural networks 9. Flow cytometry – FCS, data structure, automatic gating, graphical outputs 10. Mass spectrometry data analysis – introduction to MS, types of data and their analysis, biological follow-up (gene ontologies, interacting partners, …) 11. Analysis of scRNA-seq data – introduction to scRNA-seq, quality control, clustering and visualization 12. Analysis of scRNA-seq data (II) – automatic cluster annotation using machine learning, developmental trajectories modeling, data integration
Literature
    recommended literature
  • Modern Statistics for Modern Biology - https://www.huber.embl.de/msmb/
  • WICKHAM, Hadley and Garrett GROLEMUND. R for data science : import, tidy, transform, visualize, and model data. First edition. Sebastopol, CA: O'Reilly, 2016, xxv, 492. ISBN 9781491910399. info
  • WILKE, C. Fundamentals of data visualization : a primer on making informative and compelling figures. Beijing: O'Reilly, 2019, xvi, 370. ISBN 9781492031086. info
  • Orchestrating Single-Cell Analysis with Bioconductor - http://bioconductor.org/books/release/OSCA/
Teaching methods
The course is taught in Czech, every week practicals after the lecture. There are no prerequisities, however, it is required to be enrolled at the same time in Bi1121 (the lecture). At least a basic knowledge of programming is an advantage, otherwise the course requires individual work at home. Homeworks are optional, but strongly recommended.
Assessment methods
Credit will be given for at least 80% attendance at the practicals. Homeworks can be taken into account upon agreement. In case of failure to meet attendance, a test is given at the end of the semester to verify basic knowledge of programming in R.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
Information on course enrolment limitations: Na předmět se vztahuje povinnost registrace; bez registrace může být znemožněn zápis předmětu!
The course is also listed under the following terms Spring 2022, Spring 2023, Spring 2024.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/spring2025/Bi1121c