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27411 Biological data analysis and chemometrics

Danish title:

Biologisk dataanalyse og kemometri


Point( ECTS )


Course type:

Taught under single-course student
Technological specialization course, MSc. Eng., Mathematical Modelling and Computing


F2A (Mon 13-17)


Campus Lyngby

Scope and form:

Lectures, PC work (multivariate statistical programmes)

Duration of Course:

13 weeks

Date of examination:


Type of assessment:



Previous Course:


Not applicable together with:

Recommended prerequisites:


Participants restrictions:

Minimum 15 Maximum: 40

General course objectives:

After the course you will have experience in the common chemometric methods that are used in all biological and chemical disciplines, in which advanced spectrometric and analytical separation techniques are used. Chemometrics is used more and more in basic science and in the industries.

Learning objectives:

A student who has met the objectives of the course will be able to:
  • Give an overview of important chemometric methods.
  • Identify situations where exploratory data-analysis is required.
  • Describe and use the different forms of scaling, transformation and normalization.
  • Understand and describe the difference between classification and regression.
  • Understand and describe the difference between clustering and ordination.
  • Apply and interpret principal component analysis (PCA) on multivariate data.
  • Apply and interpret the principles of validation and outlier detection.
  • Use and interpret cluster analysis on multivariate data.
  • Describe, apply and interpret multiple linear regression (MLR) and ridge regression (RR) and where to apply them in two data matrix problems.
  • Describe, apply and interpret principal component regression (PCR) and partial least squares regression (PLSR) and where to apply them in two data matrix problems.
  • Apply and interpret correspondence analysis.
  • Describe the method metric multidimensional scaling.


Multivariate statistical treatment of quantitative and qualitative datamatrices. Exploratory dataanalysis, regression and classification methods. Scaling and standardization of raw data, cluster analysis and ordination (eigen value analysis). Principal component analysis, principal component regression, partial least square and ridge regression, principal coordinate analysis and correspondence analysis. Validation of data, especially cross-validation and jack-knifing. Use of plots in the evaluation of data. Special problems concerning chemical and biotechnogical data.

Green challenge participation:

Please contact the teacher for information on whether this course gives the student the opportunity to prepare a project that may participate in DTU´s Study Conference on sustainability, climate technology, and the environment (GRØN DYST). More infor http://www.groendyst.dtu.dk/english


Jens Christian Frisvad , Lyngby Campus, Building 221, Ph. (+45) 4525 2626 , jcf@bio.dtu.dk
Per B. Brockhoff , Lyngby Campus, Building 324, Ph. (+45) 4525 3365 , perbb@dtu.dk


27 Department of Systems Biology

Department involved:

01 Department of Applied Mathematics and Computer Science

Home page:


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Last updated: 04. maj, 2015