270148 PR Data science in metabolomics and proteomics (2022S)
Labels
Registration/Deregistration
- Registration is open from Tu 01.02.2022 08:00 to Th 24.02.2022 23:59
- Deregistration possible until Th 24.02.2022 23:59
Details
Lecturers
Classes
23.-27.05.2022 9:00 - 16:00
Planned: physically (PC-Raum des Praktikums Physikalische Chemie", Währinger Straße 42, EG Raum 2E16),
if necessary, the lecture will be shifted to an online-form
Note: The lecture will be held in a PC seminarroom at the Faculty of Chemistry. If necessary (due to another course), the lecture will shift to an online-form for individual days. This will be communicated daily before via the moodle system to the students.
While there will be some PCs available, it is not possible to book the seminarroom exclusively for the entire period and we might need to shift into a different lecture hall or to the online format. Thus, please bring your own device (preferably a laptop) that can connect to the university's network via VPN.
Information
Aims, contents and method of the course
Assessment and permitted materials
• Active participation during the course
• Written final exam on the (presented) contents
• Presentation of the carried-out dataset evaluationShould it be necessary, students may be invited to an interview with the course instructor. This interview will then also count for the final mark.Permitted aids: R-Documentation (offline)
Minimum requirements and assessment criteria
Students can earn a maximum of 100 points during the lecture. These are divided into:
• Active participation: 30 points
• Final exam: 40 points
• Presentation of the dataset evaluation: 30 pointsThe final marks are:
• 1 (A): 100 - 89 points
• 2 (B): 88 - 76 points
• 3 (C): 75 - 63 points
• 4 (D): 62 - 50 points
• 5 (F): 49 - 0 points
(Points will be rounded in favour of the student.)
Examination topics
Reading list
• https://www.statmethods.net/r-tutorial/index.html
• https://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdfXCMS and CAMERA are two commonly used software packages. They are also freely available and more information about them can be found at:
• https://dx.doi.org/10.1186/1471-2105-9-504
• https://dx.doi.org/10.1021/ac202450g
• https://www.bioconductor.org/packages/release/bioc/vignettes/CAMERA/inst/doc/CAMERA.pdf
• Introduction to the programming language R
• Import of LC-HRMS datasets into R
• Algorithms and functions of XCMS and CAMERA
• Explanation of parameters of XCMS
• Semi-automated optimization of the used data processing parameters to improve the analysis
• Export of the detected compounds into a data matrix
• Brief overview of basic and advanced statistical methods using the evaluated dataset
The course is organized into a lecture part as well as practical work in R, where the students will evaluate the dataset themselves.Methods: This course will be conducted via presentations, practical work either alone or in form of small groups, discussions between the students and student presentations, among others.Note: The students will use a central R-Installation. Each student needs to bring a laptop, which can access the university’s network.Prerequisites:
• Confident handling of PCs
• Confidence with MS Office Excel with a special focus on formulas
• Knowledge about LC-HRMS datasets