NWI-MOL109
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;
Lecturer(s)
Coordinator
dr. J.J. Jansen
Other course modules lecturer
Examiner
dr. J.J. Jansen
Other course modules lecturer
Contactperson for the course
dr. J.J. Jansen
Other course modules lecturer
Lecturer
dr. J.J. Jansen
Other course modules lecturer
Lecturer
dr. G.J. Postma
Other course modules lecturer
Academic year2019
Period
KW3  (03/02/2020 to 12/04/2020)
Starting block
KW3
Course mode
full-time
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
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.
Content
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 and K-means Clustering 
  • Linear and Quadratic Discirminant Analysis for Classification
  • 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.
Level

Presumed foreknowledge
  • Statistics (NWI-MOL028)
  • Basic Linear Algebra is beneficial. This is a course in the theme 'Methods'.
  • Test information
    Four assignments: three in pairs of students, forth individual. All grades must be sufficient and the fourth assignment counts for 50% in the final grade.
    Specifics
  • 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.
  • Additional comments
    • 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.

    Topics
    • Multivariate analysis, Principal Component Analysis (PCA)
    • Clustering techniques (hierarchical, k-means)
    • Classification (discriminant analysis, nearest-neighbour methods)
    • Multivariate regression (PCR, PLS)
    • Validation strategies.

    Test information
    Four assignments: three in pairs of students, forth individual. All grades must be sufficient and the fourth assignment counts for 50% in the final grade.

    Prerequisites
    • Statistics (NWI-MOL028)
    • Basic Linear Algebra is beneficial.

    This is a course in the theme 'Methods'.

    Required materials
    Reader
    On BrightSpace we will provide a reader, a guide to the computer excercises, and several data sets.

    Recommended materials
    Book
    Esbensen, Kim H. & Swarbrick, Brad; Multivariate Data Analysis 6th edition; An introduction to Multivariate Analysis, Process Analytical Technology and Quality by Design.
    ISBN:978-82-691104-0-1
    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

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

    Lecture
    Attendance MandatoryYes

    Project
    Attendance MandatoryYes

    Tests
    Assignments
    Test weight1
    Test typeAssignment
    OpportunitiesBlock KW3, Block KW4

    Final assignment
    Test weight1
    Test typeAssignment
    OpportunitiesBlock KW3, Block KW4