Stat-XP Short course: Statistical methods to characterise the exposome from OMICs data

Stat-XP is an introductory short course to statistical models required to analyse high throughput data from well-established OMICs platforms. This includes genetic, transcriptomic, metabonomics and epigenetics data.

The course will provide an in-depth description of the OMICs data, their features, and the challenges their statistical analysis raises. Stat-XP will also propose a series of lectures describing the main statistical methods used in molecular epidemiology. These include:

  • univariate models and multiple testing correction strategies (FWER, FDR)
  • dimension reduction techniques, and 
  • variable selection approaches (penalised regression and Bayesian variable selection).

Corresponding seminars will show how these methods are used in practice, and computer-based practical sessions will give the opportunity to use and get familiar with the well-established software/packages enabling such analyses. Finally, we will describe methodological perspectives to improve the analysis of OMICs data in the context of the exposome.

More details (Brochure_2014 ) The full Brochure details the content of the course.

More information can be found here


Course Organisation 

Stat XP is a 5-days short course taking place in London 8th-12th December, 2014. The course comprises 4 days teaching that articulated as follows: the day starts with lectures introducing the theoretical concepts. These are subsequently illustrated on the same day by a seminar and practical. On Wednesday, 10th a full seminar day is planned to illustrate the practical use, validity, strength of the methods presented in the course.



Online registration is available until December 1st, 2014 from the following link


Teaching Team

Imperial College London

Prof Paul Elliott, Professor of Epidemiology and Public Health Medicine, Head of Dept of Epidemiology and Biostatistics.
 Director of MRC-PHE Centre for Environment and Health.

Prof Paolo Vineis,  Professor of Environmental Epidemiology. Dept of Epidemiology and Biostatistics. Adjunct Professor, Columbia University, NYC – USA.

Dr Marc Chadeau-Hyam,  Lecturer in Statistical Bioinformatics, Dept of Epidemiology and Biostatistics. Honorary Reader, Utrecht University

Dr Tim Ebbels,  Reader in Computational Bioinformatics, Dept of Surgery & Cancer

Dr Korbinian Strimmer, Reader in Biostatistics & Computational Biology, Dept of Epidemiology and Biostatistics.

Mr Gianluca Campanella, Doctoral Researcher, Dept of Epidemiology and Biostatistics.

Dr Benjamin Lehne, Research Associate, Dept of Epidemiology and Biostatistics.

Dr Raphaële Castagné,  Research Associate, Dept of Epidemiology and Biostatistics.

Dr Florence Guida,  Research Associate, Dept of Epidemiology and Biostatistics.

Utrecht University (NL)

Dr Roel Vermeulen,  Associate Professor, Institute for Risk Assessment Sciences. Visitng Professor, Imperial College London.

Dr Jelle Vlaanderen, Junior Assistant Professor, Institute for Risk Assessment Sciences.

University of Queensland (AUS)

Dr Benoît Liquet,  Senior Lecturer in Statistics, School of Mathematics and Physics

University College London (UK)

Dr Maria de Iorio,  Reader in Statistics in the Dept of Statistical Science

International Agency for Research on Cancer (FR)

Dr Augustin Scalbert,  Head of the Biomarkers Group

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