CSAMA 2017 – Statistical Data Analysis for Genome Scale Biology

CSAMA 2017 (15th edition)
Statistical Data Analysis for Genome Scale Biology
Bressanone-Brixen, Italy (South Tyrol Alps)
June 11-16, 2017

Lecturers:

  • Simon Anders, University of Heidelberg
  • Jennifer Bryan, RStudio and UBC
  • Vincent J. Carey, Harvard Medical School
  • Laurent Gatto, University of Cambridge
  • Wolfgang Huber, European Molecular Biology Laboratory (EMBL), Heidelberg
  • Martin Morgan, Roswell Park Cancer Institute, Buffalo
  • Johannes Rainer, European Academy of Bozen (EURAC)
  • Charlotte Soneson, University of Zurich
  • Levi Waldron, CUNY School of Public Health at Hunter College, New York

Teaching Assistants:

  • Simone Bell, EMBL, Heidelberg
  • Vladislav Kim, EMBL, Heidelberg
  • Lori Shepherd, RPCI, Buffalo
  • Mike L. Smith, EMBL, Heidelberg

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.

Topics include:

  • 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

The course consists of

  • morning lectures: 20 x 45 minutes: Monday to Friday 8:30h – 12:00h
  • 4 practical computer tutorials in the afternoons (13:30h – 16:30h) on Monday, Tuesday, Thursday and Friday

Visit the course’s website at: http://www.huber.embl.de/csama

Welcome Frederik Ziebell

Frederik has a PhD in Applied Mathematics from Heidelberg University and the German Cancer Research Center (DKFZ). He works on developing statistical methods for high-dimensional heterogeneous data and the analysis of multi-omic level drug treatment effects in collaboration with Cellzome.

Welcome Almut Lütge

Almut is a master student in molecular biotechnology at the University of Heidelberg. She joined the Huber group in 2017 and works on the analysis of RNA-seq data from Chronic lymphatic leukemia (CLL) samples.