Educators:
Simon Anders, Simone Bell, Jennifer Bryan, Vincent J. Carey, Laurent Gatto, Wolfgang Huber, Martin Morgan, Mike Smith, Johannes Rainer, Charlotte Soneson, Levi Waldron (HD-HuB)
Date:
11-06-2017 – 16-06-2017
Location:
University of Padova, Bressanone-Brixen
Contents:
The one-week intensive course teaches statistical and computational analysis of multi-omics studies in biology and biomedicine. It covers the underlying theory and state of the art (the morning lectures), and practical hands-on exercises based on the R / Bioconductor environment (the afternoon labs). The course covers the primary analysis of high-throughput sequencing based assays in functional genomics and integrative methods including efficiently operating with genomic intervals, statistical testing, linear models, machine learning, bioinformatic annotation and visualization. The planned topics include: Introduction to Bioconductor Elements of statistics: hypothesis testing, multiple testing, regression, linear models, regularization, clustering and classification, visualization, and experimental design Reproducible research with Rmarkdown and knitr Computing with sequences and genomic intervals Working with annotation – genes, genomic features and variants RNA-Seq data analysis and differential expression Single-cell RNA-Seq Working with ChIP-seq data Version control with Git Improving performance with code parallelization Learning goals: At the end of the course, participants should be able to run analysis workflows on their own (multi-)omic data, adapt and combine different tools, and make informed and scientifically sound choices about analysis strategies.
Prerequisites:
The course is intended for researchers who have basic familiarity with the experimental technologies and their applications in biology, and who are interested in making the step from a user of bioinformatics software towards adapting or developing their own analysis workflows. The four practical sessions of the course will require you to follow and modify scripts in the computer language R.
Keywords:
R, Bioconductor, Genomics, Statistics, Multi-omics
Tools:
R & Bioconductor
Contact:
Mike Smith
http://www.huber.embl.de/csama2017