Advanced [R] for molecular biology

Due to technological advances in molecular biology (genomics, large-scale systems biology) research in the life sciences is becoming increasingly data rich. Currently, appropriate analyses of large-scale datasets (proteomics, genomes, Htseq, RNAseq, ChIPseq, etc) are a limiting factor. The aim of this course is to provide students with a good understanding of how to use R for advanced data analyses. Students will learn how to use several commonly used statistical/mathematical approaches to extract meaningful information from data.

Date: Feb 2-13, 2015

Target audience: This course is open for Master and PhD students.

Program: Topics include: dimension reduction techniques (clustering, principal component analysis, singular value decomposition, etc), working with genomic (location) data as obtained from next-generation sequencing technology, regression models and classification and prediction algorithms. In addition, more technical programming skills will also be taught, so that at the end of the course, students are able to structure their R code, modify or create functions and write their own R programs. At the beginning of the course you will be given a programming assignment that has to be handed in at the end of the course, based upon which a grade will be given.

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