[Apologizes for cross-posting]

CSAMA 2017: Statistical Data Analysis for Genome Scale Biology

Bressanone-Brixen (IT)

June 11-16, 2017

About the Course

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The one-week intensive course Statistical Data Analysis for Genome-Scale Biology 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). At the end of the course, you should be able to run analysis workflows on your own (multi-)omic data, adapt and combine different tools, and make informed and scientifically sound choices about analysis strategies.

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.

Topics

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* Introduction to R and Bioconductor

* The elements of statistics: hypothesis testing, multiple testing, regression, regularization, clustering and classification, parallelization and performance (machine learning), visualisation

* RNA-Seq data analysis

* Computing with sequences and genomic intervals

* Working with annotation – genes, genomic features, variants, transcripts and proteins

* Gene set enrichment analysis

* Mass spec proteomics and metabolomics

* Basis of microbiome analysis

* Experimental design, batch effects and confounding

* Reproducible research and workflow authoring with R markdown

* Package development, version control and developer tools (incl. git, github, RStudio)

* Working with large data: performance parallelisation and cloud computing

Course Structure

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