PřF:Bi1121 Data analysis in R for EMB - Course Information
Bi1121 Data analysis in R for experimental and molecular biologists
Faculty of ScienceSpring 2023
- Extent and Intensity
- 2/0/0. 2 credit(s) (plus 1 for the colloquium). Type of Completion: k (colloquium).
- Teacher(s)
- Mgr. Petra Ovesná, Ph.D. (lecturer)
Mgr. Kristína Gömöryová, Ph.D. (lecturer)
Mgr. Petr Tauš, Ph.D. (lecturer)
Mgr. Radek Fedr (lecturer) - 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 - Timetable
- Fri 8:00–9:50 B09/316
- Prerequisites
- No prerequisities, however students are required to enroll at the same time to Bi1121c (practicals).
- 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: 8/24, only registered: 0/24, only registered with preference (fields directly associated with the programme): 0/24 - fields of study / plans the course is directly associated with
- Physiology (programme PřF, N-EBZ)
- Immunology (programme PřF, N-EBZ)
- Developmental Biology (programme PřF, N-EBZ)
- 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 and reporting, (iii) fundamentals of proper experimental design, (iv) introduction to omics data evaluation (including mass spectrometry, scRNA-seq), flow cytometry and microscopy data analysis, (v) how to conduct research reproducibly.
- 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 analysis – data input, data types, transformation, summarization 3. Introduction to statistical methods – principles of statistical thinking, confidence intervals, common statistical tests, introduction to survival analysis 4. Experimental design, power analysis 5. Graphics in R – plotting graphs in base R, ggplot2, interactive visualizations; types of graphs in molecular biology 6. Artificial intelligence and machine learning – dimensions reduction, clustering, neural networks 7. Flow cytometry – FCS, data structure, automatic gating, graphical outputs 8. Mass spectrometry data analysis – introduction to MS, types of data and their analysis, biological follow-up (gene ontologies, interacting partners, …) 9. Analysis of scRNA-seq data – introduction to scRNA-seq, quality control, clustering and visualization 10. Analysis of scRNA-seq data (II) – automatic cluster annotation using machine learning, developmental trajectories modeling, data integration 11. Basics of image data processing – point transformation, animations 12. Reproducibility – git, GitHub, R Markdown, workflow
- 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. There are no prerequisities, however, it is required to be enrolled at the same time in Bi1121c (practicals to Bi1121).
- Assessment methods
- At the end of the semester, students will choose one dataset from the topics covered during the semester (mass spectrometry, scRNA-seq and flow cytometry), independently analyze the data and prepare a reproducible RMarkdown document describing all the steps of analysis, along with the biological evaluation of the results.
- Language of instruction
- Czech
- Follow-Up Courses
- Further comments (probably available only in Czech)
- The course is taught annually.
Information on course enrolment limitations: Na předmět se vztahuje povinnost registrace; bez registrace může být znemožněn zápis předmětu! - Listed among pre-requisites of other courses
- Enrolment Statistics (Spring 2023, recent)
- Permalink: https://is.muni.cz/course/sci/spring2023/Bi1121