270214 PR Data science in metabolomics and proteomics in R (2024W)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von So 01.09.2024 08:00 bis Mo 23.09.2024 23:59
- Abmeldung bis Mo 23.09.2024 23:59
Details
max. 12 Teilnehmer*innen
Sprache: Deutsch
Lehrende
Termine
Preliminary meeting on Monday, 14th October at 1:00 PM, in the meeting room, Top 16, in Sensengasse 8, third floor
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Methods used to evaluate the students’ performances:
• 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)
• 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)
Mindestanforderungen und Beurteilungsmaßstab
Students must be present during the first lecture of the course. Moreover, they have to participate in at least 75% of the course.In order to pass the course, students must reach at least 50% of the maximum possible points. Additionally, each evaluation criteria must be evaluated positively.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.)
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.)
Prüfungsstoff
The contents of the lectures and practical work.
Literatur
The programming language R is open source and thus a large number of teaching materials are available free of charge for the students, which should be read and studied prior to the course. Some of these are:
• 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
• 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
Zuordnung im Vorlesungsverzeichnis
CH-CBS-05, BC-CHE II-8
Letzte Änderung: Mo 14.10.2024 14:06
• 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