Nowadays high-throughput methods are often used to study gene expression (transcriptomics), proteins (proteomics) and metabolites (metabolomics). Because high-throughput methods generate large datasets, special statistical analysis and visualization techniques are required to extract relevant information to help elucidating the functions of genes, proteins and metabolites, the interactions among these molecules and their relationship with observed phenotypic traits. Statistical analysis of these data is non-trivial since in many cases the number of genes/metabolites outweighs the number of samples by hundreds or thousands.
Date: Dec 15-19, 2014
Target audience: This is a course aimed at applied biologists, molecular and cell biologists, geneticists and plant/animal breeding PhDs, and statistics and bioinformatics PhD students who deal with large ~omics (transcriptomics, proteomics, metabolomics) data sets.
Program: Course design: In order to successfully interpret experimental results generated by high-throughput ~omics methods we will teach the principles underlying preprocessing, statistical analysis and visualization of large datasets derived from transcriptomics and metabolomics experiments. The emphasis will be on statistical aspects and analysis. Relevant software will be mentioned and some will be used during hands-on exercises. During the course students are provided with a syllabus, handouts, exercises and an overview of relevant literature and Internet links.
The course is about statistical analyses, and some preprocessing steps, but not about bioinformatics in the wider sense. Genomics in the sense of sequence bioinformatics, alignment of DNA sequences etc., is not part of this course. Proteomics as such is also not part of this course, but the methods used in this course can be relevant for multivariate datasets in proteomics as well.
Course level: This is an advanced level statistical course on statistical analyses, where transcriptomics and metabolomics data sets will be used to demonstrate how these methods can be applied. The methodology taught is also useful for other large scale analysis data sets but the emphasis in the examples is on transcriptomics and metabolomics data.
