High Dimensional Data Analysis
Modern high throughput technologies easily generate data on thousands of variables; e.g. health care data, genomics, chemometrics, environmental monitoring, web logs, movie ratings, … Conventional statistical methods are no longer suited for effectively analysing such high-dimensional data. Multivariate statistical methods may be used, but for often the dimensionality of the data set is much larger than the number of (biological) samples. Modern advances in statistical data analyses allow for the appropriate analysis of such data.
Praktische info:
Leertraject
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- Voorwaarden: ready at hand knowledge of basic statistics: data exploration and descriptive statistics, statistical modeling, and inference: linear models, confidence intervals, t-tests, F-tests, anova, chi-squared test
- Prijs: €595-€1320
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Methods for the analysis of high dimensional data rely heavily on multivariate statistical methods. Therefore a large part of the course content is devoted to multivariate methods, but with a focus on high dimensional settings and issues.
Multivariate statistical analysis covers many methods. In this course a selection of techniques is covered based on our experience that they are frequently used in industry and research institutes.
The course is taught using case studies with applications from different fields (analytical chemistry, ecology, biotechnology, genomics, …).
Lesgever/spreker
Lieven Clement is an Associate Professor of Statistical Genomics at Ghent University. He is an expert in developing statistical methods and open source tools for differential omics data analysis. His lab is built around two strategic research pillars each connected to an omics domain: (single cell) transcriptomics and proteomics. He is a member of the core team that established a new Master of Science in Bioinformatics at Ghent University and has a track record in teaching statistics, statistical genomics and high dimensional data analysis to students in the life sciences and statistical data-analysis. He also gives short courses in statistics and proteomics data analysis in prominent bioinformatics programmes in Europe (Wellcome Trust Advanced Courses and Gulbenkian Institute Training Programme, amongst others). He is a strong advocate of open and reproducible science and teaching to empower researchers and students with freely available, user-friendly, operating system independent, state-of-the-art bioinformatics tools, and by making all research code, data and teaching materials well documented, open and accessible.
