Biosystems Data Analysis

2019-2020

Course Objective

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:
o Students know the origin of the data and the specific properties of
the data.
o Students understand how the data analysis methods work theoretically.
o Students are able to apply the methods, and they are able to interpret
the results.
o Students comprehend the pitfalls of multivariate data and validation
strategies to prevent overfit.
o Students are able to critically review data analysis applications in
which the above mentioned methods have been used.
o Students are able to select the most appropriate method for a given
biological question.

Course Content

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.

Teaching Methods

o Lecture
o Laptop seminar
o Self-study

Method of Assessment

There will be two tests. The first test is a practical one. Here
students are given a specific data set which they have to analyse using
Matlab and interpret given the instructions provided. This test will
last 3 hours, at the end of which students hand in their data analysis
report.
The second (final) test is a theoretical one in which questions are
asked on the data analysis methods, especially on how to interpret their
results.
For the final grade, the weights are 1/3 and 2/3 for the practical and
the theoretical test respectively.

Literature

Literature:
o Will be provided using Canvas.
Software:
o Matlab

Target Audience

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:
o First-year students of the master Bioinformatics and Systems Biology
(JD UvA and VU)
o Second-year students of the master Bioinformatics and Systems Biology
(JD UvA and VU)
o Students of the master Computational Sciences
o Students of the master Chemistry
o Students of the master Forensic Sciences
o Students of other master programmes

Recommended background knowledge

Matlab, Linear Algebra, introduction level Statistics.

General Information

Course Code XM_0078
Credits 6 EC
Period P3
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. J.A. Westerhuis
Examiner dr. J.A. Westerhuis
Teaching Staff dr. D. Molenaar
dr. ir. H.C.J. Hoefsloot
dr. J.A. Westerhuis

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Computer lab