Multivariate Projection-Based Methods for Data Exploration and Data Integration
Walter and Eliza Hall Institute
1g Royal Parade, Parkville
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Multivariate projection-based methods are useful statistical tools to obtain a first understanding of large and complex data sets. Principal Component Analysis (PCA) is the oldest and most popular multivariate method that reduce dimension of the data by summarising the most important sources of variation. Multivariate methods provide insightful visualisations and are highly flexible: unsupervised (exploratory) or supervised (classification) analyses can be performed, and they make little assumptions about the distribution of the data. The lecture will first introduce key concepts in multivariate dimension reduction with PCA, and introduce the latest developments in this exciting area of research to integrate different types of ‘omics data and to identify key biomarkers. The methods are available in the mixOmics R package (www.mixOmics.org).
No registration is required