General Information
| Lecturer | Prof. Dr. Mathias Wilhelm, Prof. Dr. Melanie Schirmer, Dr. Sapna Sharma, Prof. Dr. Markus List |
| ECTS | 5 ECTS |
| Language | English |
| Turnus | Sommersemester |
| Registration | TUMonline & Moodle |
| Exam | Project work (final presentation and written evaluation) |
Introduction
Data Science is a crucial skill for biologists, allowing them to analyze and interrogate data sets and to answer important research questions. In this course, we introduce you to important concepts of data science and working with available resources and databases. You will learn to perform analysis and data visualization in R leveraging its rich package ecosystem. You will acquire basic analysis skills across different omics types and become more independent in data analysis.
Content
In this module, the fundamentals of Data Science in the field of Molecular Biology are covered. The following contents will be addressed:
- Fundamentals of R, Bioconductor
- Scientific principles from Open Science to FAIR data
- Visualization of OMICS data
- Fundamentals of machine learning
- Data Science fundamentals of metagenomics and statistical analyses
- Data Science fundamentals of transcriptomics, differential gene expression analysis
- Data Science fundamentals of functional enrichment analysis
- Data Science fundamentals of proteomics
- Data Science fundamentals of metabolomics
Exercise and Exam
The module examination is conducted as a project work at the end of the semester in groups of 3-4 participants. This includes, among other things, problem definition, role distribution, ideation, criteria development, as well as decision making, project planning, and execution. The project assignment involves the development of an analysis pipeline, through which students demonstrate that they have understood the concepts of the course. The final report proves that participants can present the subject matter of their work clearly and understandably within a given scope. After submitting the report, participants will conduct a peer review evaluation. This shows that they can critically question and evaluate the pipelines of other groups. The grade is equally based on the peer review and the final written evaluation. For the latter, a documentation of the software in the form of a report must be created. Special attention is paid to the choice of methodology, correct handling, reproducibility, and correct interpretation of the pipeline results. For grading (individual assessment), the contributions of team members must be evident, e.g., through the division of the report and the peer review.
The correct application of the methods and the expectations for the module examination are practically learned by the participants through the semester-long processing of assignment sheets in groups of 3-4 participants. The tasks of the students include, among other things, loading, processing, and analyzing data from various OMICS areas, as well as interpreting and discussing the results with the inclusion of literature research. The students document their analyses through scripts (e.g., Rmarkdown), thereby demonstrating that they have successfully mastered the required tools. Participants receive a grade bonus of 0.3 on the module examination if they achieve 75% of the total points from the semester-long assignment sheets.