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Topic: 6th September 2019- ECN multivariate statistics webinar 2 (Read 24 times) previous topic - next topic

6th September 2019- ECN multivariate statistics webinar 2

The ECN committee are pleased to announce the second webinar in our series on multivariate statistics that will be delivered by Dr Claus Mayer:

Sparse multivariate methods and integration of omics data sets

Friday 6th September, 16:00-17:00 CET

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. Dr Mayer will introduce sparse versions of some commonly used multivariate methods and illustrate their use in data examples.
In a second part he 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.

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.

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