Multivariate Statistics 2

Requirements:

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:

Participants will learn when and how to apply unsupervised and supervised dimension reduction techniques, including MDS, MFA, t-SNE, UMAP, PCR, and PLSR. The lecture will begin with a brief introduction to PCA, while more detailed coverage of PCA is offered in the Multivariate Statistics 1 course. Additionally, the lecture includes a short overview of multi-omics factor analysis (MOFA). The course content is designed to provide a foundational understanding of the theory behind multivariate analysis. Each topic is accompanied by hands-on exercises using the statistical software R. Participants are encouraged to ask questions and seek advice on analyzing their own datasets.

Topics:

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).

Methods:

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.

Format:

• 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.