CSAMA 2016: Statistical Data Analysis for Genome-Scale Biology
July 10-15, 2016
Bressanone-Brixen, Italy
URL: http://www.huber.embl.de/csama2016/
Lecturers: Simon Anders, Institute for Molecular Medicine, Helsinki;
Jennifer Bryan, University of British Columbia, Vancouver; Vincent
J. Carey, Channing Laboratory, Harvard Medical School; Wolfgang Huber,
European Molecular Biology Laboratory (EMBL), Heidelberg; Michael
Love, Dana Farber Cancer Institute and the Harvard School of Public
Health; Martin Morgan, Roswell Park Cancer Institute, Buffalo, New
York; Charlotte Soneson, University of Zurich; Levi Waldron, CUNY
School of Public Health at Hunter College, New York.
Teaching Assistants: Simone Bell, EMBL, Heidelberg; Alejandro Reyes,
EMBL, Heidelberg; Mike L. Smith, EMBL, Heidelberg.
Resources
Monday, July 11
Lectures
- 01 Introduction to R and Bioconductor (MM)
pdf
- 02 Hypothesis testing (WH)
pdf
- 03 Learning to love the data frame (JB)
pdf
- 04 Linear models (basic intro) (LW)
pdf
Labs
- 1 Introduction to R and Bioconductor (MM)
html
- 2 Use of Git and GitHub with R, RStudio, and R Markdown (JB)
pdf
Tuesday, July 12
Lectures
- 05 Basics of sequence alignment and aligners (SAn)
pdf
- 06 RNA-Seq data analysis and differential expression (ML)
pdf
- 07 New workflows for RNA-seq (CS)
pdf
- 08 Computing with sequences and genomic intervals (MM)
pdf
Labs
- 3 End-to-end RNA-Seq workflow (SA, ML and CS)
Rmd
Wednesday, July 13
Lectures
- 09 Experimental design, batch effects and confounding (CS)
pdf
- 10 Clustering and classification (VJC)
pdf
- 11 Robust statistics (ML)
pdf
- 12 Resampling: cross-validation, bootstrap and permutations
(LW)
pdf
Thursday, July 14
Lectures
- 13 Multiple testing (WH)
pdf
- 14 Working with annotation – genes, genomic features and
variants (MM and VC)
pdf
- 15 Analysis of microbiome data (marker gene based) (CS)
pdf
- 16 Visualization, the grammar of graphics and ggplot2 (WH)
pdf
Labs
- 4 Reproducible research and R authoring with markdown and knitr
(JB) github
- 5 ChIP-Seq analysis basics (AR and MS)
Rmd
html
Rpackage
Friday, July 15
Lectures
- 17 Gene set enrichment analysis (MM, ML)
pdf;
pdf
- 18 Meta-analysis (LW)
pdf
- 19 Large data, performance, and parallelization; large-scale
efficient computation with genomic intervals (MM, VJC)
pdf;
pdf
- 20 What should you do next? (JB)
pdf
Labs
- 6 Machine Learning, Parallelization and performance (WH, MM,
VJC) Independent Hypthesis weighting
Rmd
html;
Efficient and Parallel
html;
Machine Learning
html
Rmd
pdf
Code
- 7 Graphics (WH)
Rmd