Webinar 2019 4: September 2019
DATE: Friday, 6 September at 16:00 h (CET)
TITLE: Multivariate Statistics for Nutrional Sciences
HOST: Prof. Claus-Dieter Mayer (Biomathematics & Statistics Scotland, Rowett Institute of Nutrition and Health, University of Aberdeen, UK)
REGISTRATION: Attendance is FREE but registration is required.
Please register at: GoToWebinar
Space is limited and priority is given to ECN members on a first-come-first-served basis.
Multivariate methods like principle component analysis (PCA) or partial least squares (PLS) are essential in revealing structure in high-dimensional omics type data, where the number of variables is typically much larger than the number of samples (p>>n). As useful as these methods are to study the relationship between samples the high number of variables obscures the interpretation which genes, proteins or metabolites contribute to the patterns we see. Sparse methods enforce the loadings of most variables to be 0, while still explaining much of the variation in the data and thus enable an easier biological interpretation of the results. We will introduce sparse versions of some commonly used multivariate methods and illustrate their use in data examples.
In a second part we will present methods that simultaneously analyse two (or more) data sets like Canonical Correlation Analysis (CCA) or Co-Inertia Analysis (CIA). These tools allow to study the joint influence of two sets of variables (eg. a transcriptomic and a proteomic data set) on the variation within samples while showing the relationship between the data sets at the same time.
You can visit the webinar online on Vimeo here
Dr Claus Mayer is a senior statistician working for Biomathematics and Statistics Scotland. His main area of research in recent years has been the analysis of high-dimensional genomics data with a particular emphasis on gene expression studies (microarrays, RNAseq) and related areas (proteomics, methylation studies. Dr Mayer has worked on methods of integrating/combining such omics data sets from different sources like combining high-dimensional data from different stages of an experiment in a group-sequential setting, conducting meta-analysis of comparable gene expression studies or integrating different types of omics data collected from the same samples. Dr Mayer has also investigated ways of quickly calculating overall summary statistics of pairwise (cross-) correlations within one or more high-dimensional data sets and has studied ways of turning such (partial) correlation structures into sparse biologically interpretable networks