Chemometrics for Molecular Life Sciences
Course infoSchedule
Course moduleNWI-MOL109
Credits (ECTS)6
CategoryBA (Bachelor)
Language of instructionEnglish
Offered byRadboud University; Faculty of Science; Moleculaire Wetenschappen;
dr. J.J. Jansen
Other course modules lecturer
dr. J.J. Jansen
Other course modules lecturer
Contactperson for the course
dr. J.J. Jansen
Other course modules lecturer
dr. J.J. Jansen
Other course modules lecturer
dr. G.J. Postma
Other course modules lecturer
Academic year2021
KW3  (31/01/2022 to 10/04/2022)
Starting block
Course mode
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-
The course teaches the student to apply and familiarize him/herself with the techniques that constitute the cornerstones of modern Chemical data analysis. These data visualization and analysis techniques are crucial in contemporary biomedicine.
At the end of the course the student:
  • ...knows the principles of the most important chemometrical methods;
  • ...can select the proper method, based on the biological/biochemical question and the principles of each method 
  • ... can apply each technique in the correct context on several real life data sets of different size and complexity with interactive data analysis software;
  • ...can correctly interpret and validate the results and can translate them to answers to the biological/biochemical question.
No programming skills are required to successfully follow this course.
During the statistics course, the main focus was on univariate chemical and biological data analysis. However, modern life sciences experiments yield multivariate measurements on chemical and biological systems. Examples are NMR spectra, NIR spectra or mass spectral data. Analysis of such data requires techniques that take the multivariate character of the data into account, providing improved results as compared to simple univariate analysis. This in turn leads to better diagnostic predictions. and better insight into the disease mechanism.
This course provides you with the main tools for multivariate data visualization and analysis.
The course gives an overview of the most widely used chemometric methods using examples from molecular life science. The methods that will be covered are :
  • Principal Component Analysis for exploratory analysis of multivariate data
  • Hierarchical Clustering 
  • Linear Discirminant Analysis for Classification
  • Statistical model validation
  • Partial Least Squares regression and Discriminant Analysis for multivariate regression and prediction
The course will focus on methods to handle experimental measurements as data and the interpretation of biomedical information (i.e. biomarkers). The course will treat applications in metabolomics, proteomics and genomics. During the course, the various chemometric methods are studied and exercised thoroughly, with a focus on interpretation of the resulting models in a validated and robust way. 
The course is obligatory for students doing a master internship at the Department of Analytical Chemistry.

Instructional Modes
Subjects start with an introductory lecture, which is followed by a computer course in which the relevant methods are programmed in MATLAB. The student will use the programmed method to do a data analysis of a chemical experiment, which may be discussed and completed in a second computer course.

Presumed foreknowledge
  • Statistics (NWI-MOL028) or DATA: Statistics and Programming (NWI-MOL105)
  • Basic Linear Algebra is beneficial. This is a course in the theme 'Methods'.
Test information
Three assignments: All grades must be sufficient and the third assignment counts for 50% in the final grade.
  • This course is a follow up of the data analysis topics in the 'RNA' course NWI-MOL107.
  • A follow-up course is Pattern Recognition in the Natural Sciences (SM299).
  • The course will be given in parallel with the course Chemometrics (MOL065), which focuses on a deeper understanding of the chemometric techniques. Switching with MOL065 is possible until after the first computer course.
Required materials
On BrightSpace we will provide a reader, a guide to the computer excercises, and several data sets.

Recommended materials
Esbensen, Kim H. & Swarbrick, Brad; Multivariate Data Analysis 6th edition; An introduction to Multivariate Analysis, Process Analytical Technology and Quality by Design.
Title:Multivariate Data Analysis 6th edition; An introduction to Multivariate Analysis, Process Analytical Technology and Quality by Design.
Author:Esbensen, Kim H. & Swarbrick, Brad
Publisher:Camo Software AS

Instructional modes
Computer training / indiv. project work
Attendance MandatoryYes

16 h computer training
12 h individual project work
4 h response lecture

Attendance MandatoryYes

Attendance MandatoryYes

Test weight1
Test typeAssignment
OpportunitiesBlock KW3, Block KW4

Final assignment
Test weight1
Test typeAssignment
OpportunitiesBlock KW3, Block KW4