Biosystems Data Analysis
6 EC
Semester 1, period 3
5304BIDA6Y
| Owner | Master Life Sciences |
| Coordinator | dr. J.A. Westerhuis |
| Part of | Master Computational Science (Joint Degree), year 1Master Life Sciences, year 1 |
In the analysis of biochemical systems, many measurements are performed, leading to complex multivariate data sets. The tendency is to measure more and more of just a few samples. Multivariate data analysis methods are often used to explore such sets.
This course covers a broad range of multivariate data analysis methods, for e.g. exploration, clustering, classification. The latter is especially important in biomarker discovery. Design of experiments and ANOVA for multivariate data is also discussed. Furthermore, the interpretation of selected features in terms of function and networks is discussed.The course starts with an introduction on the properties of the different types of functional genomics data.
The main goal of this course is to teach students how to interpret the results of the multivariate methods and how this relates to the biological problem that is studied.
Material is available at the Blackboard site.
Material is available on the Blackboard site.
Training material is available on the Blackboard site.
Matlab
Various data analysis methods are discussed in this course. For each of these methods the students will be provided knowledge on how and when to use the multivariate method for analyzing complex sets of multivariate data in biological systems, and especially how to interpret the data analysis results. After the course:
At the start of the course students have sufficient knowledge of Matlab, linear algebra and basic statistics. Students are expeccted to work through the provided Matlab tutorial (which also contains basic linear algebra) when this is not the case.
Each topic of 8 topics starts with self-study of provided material (4h). A two hour 2 hour lecture introduces the specific topic which is followed by 4 hours of computer exercises.
Worked out exercises are provided after the computer labs are finished. Students can ask for feedback at the start of the next computer lab.
Activity | Number of hours |
Digitale Toets | 3 |
Zelfstudie | 165 |
The programme does not have requirements concerning attendance (OER-B).
| Item and weight | Details |
|
Final grade | |
|
2 (67%) Tentamen | Must be ≥ 5 |
|
1 (33%) Digitale Toets | Must be ≥ 5 |
Both exams are "closed book". It is not allowed to take any material to the exam.
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.uva.nl/plagiarism
| Weeknummer | Onderwerpen | Studiestof |
| 1 | ||
| 2 | ||
| 3 | ||
| 4 |
The schedule for this course is published on DataNose.
Matlab, Linear Algebra, introduction level Statistics.
The maximum number of participants is 60. Admission to the course will depend on capacity, total number of applications, date of registration and background of the individual student. If the number of applications exceeds the capacity of the course, students may have to be selected and priority will be given in the following order: