Berlin, 19-23 March 2018
Dr. Sebastien Le (Agrocampus Ouest, FR)
Dr. Aubry Marc (University of Rennes1, FR)
This course is divided into two parts.
First, a detailed overview of the classical exploratory methods conceived for multivariate data: Principal Components Analysis, Correspondence Analysis, and Multiple Correspondence Analysis. From a unified theoretical framework, we will see how these methods are linked, as well as their specificities in terms of interpretation, due to the nature of the data they are dealing with. From a practical point of view, we will see how they can be applied to genomic data, and how they can be used to obtain meaningful information. We will see notably, how we can add supplementary information to get a better understanding of the data.
Second, an overview of methods that handle multivariate data, when variables are structured according to groups: generalised canonical analysis, and Multiple Factor Analysis. These methods are really useful when different points of view on the same set of individuals have to be compared. It is the case for instance, when one has at his disposal gene expressions on the one hand, and chemical measures on the other hand.
The methods will be presented from a geometrical point of view. The concepts of quality of representation, active versus illustrative variables, automatic description of the dimensions provided by the analyses will be discussed. Format
Each day will include an introductory lecture with class discussion of key concepts. The remainder of each day will consist of practical hands-on sessions. These sessions will involve a combination of both mirroring exercises with the instructor to demonstrate a skill as well as applying these skills on your own to complete individual exercises. After and during each exercise, interpretation of results will be discussed as a group. Computing will be done using a combination of tools installed on the attendees laptop computer and web resources accessed via web browser. Who should attend
Researchers who would like to investigate multivariate and heterogenous data from an exploratory point of view. Researchers who would like to invest in methods capable of handling multi-block data, in the sense that data are structured into groups of variables.
Botanisches Museum, Königin-Luise-Straße 6-8, Berlin
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