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Multivariate Statistics 2

Advanced dimensionality reduction techniques

Course description


Programming skills with R, e.g., course Introduction to R, basic knowledge of statistics, e.g., course Introduction to Statistics and knowledge on basic dimension reduction techniques (PCA), e.g., course Multivariate Statistics 1.  Some practice in ggplot2 is also welcome, which can be achieved in the course Graphics with R (not mandatory).

Course overview:

The participants will learn when and how to apply unsupervised and supervised dimension reduction techniques such as MDS, MFA, t-SNE, UMAP, PCR or PLSR. A brief introduction to PCA will be given at the beginning of the lecture (more details on PCA can be learned in the course Multivariate Statistics 1). The content of the course will help understand the basis of the theory when doing a multivariate analysis. All topics are accompanied by hands-on exercises using the statistical software R. The participants are invited to ask as many questions as they want about the analyses on their own data set.


This course on multivariate statistics covers two different topics:

  • Unsupervised dimension reduction methods. This first chapter starts with a short repetition on the basic principles of principal component analysis (PCA). After this introduction, more advanced dimension reduction techniques are explained, namely multidimensional scaling (MDS) and multiple factor analysis (MFA) for data structured into groups. A brief overview on multi-omics factor analysis (MOFA) is also part of the lecture. This chapter focuses as well on techniques developed for high-dimensional data set (e.g., omics data), namely t-SNE and UMAP.
  • Supervised dimension reduction methods. This second chapter covers two supervised learning methods: principal component regression (PCR) and partial least squares regression (PLSR).


Each day consists of blocks covering first the theory behind the methods and their applications in R. Theoretical lessons will be followed by hands-on examples with best-practice solutions.


  • Duration: 2 Days
  • Language: English
  • This course will be offered either on campus (in person), or online
  • For online courses we use the software Zoom.


Dates and Application:

  • Courses provided for Helmholtz Munich:
    • You can check the current dates and whether the courses are already fully booked here*.
    • Please read the corresponding FAQ* before applying via the forms of the HR Development department*.
  • Courses provided for HIDA:
    • You can check the current dates and whether the courses are already fully booked here.
    • Registrations for these courses are exclusively possible via the provided homepage.

 * Links marked with * are only available for Helmholtz Munich staff.