CSAMA 2017: Statistical Data Analysis for Genome-Scale Biology

June 11-16, 2017
Bressanone-Brixen, Italy
URL: http://www.huber.embl.de/csama2017/

Lecturers: 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.

Resources

Source: Github

Monday, June 12

Lectures

• Introduction to R and Bioconductor (html).
• Computing with Sequences and Ranges (html).
• Tabular data management (pdf).
• Annotation resources (html); EnsemblDb (pdf).

Labs

• Introduction to Bioc. Introduction (html), R html), Bioconductor (html), Data representations (html), Annotation (html) (data: ALLphenoData.tsv, BRFSS-subset.csv, symgo.csv).
• Use of Git and GitHub with R, RStudio, and R Markdown (html)

Tuesday, June 13

Lectures

• Basics of sequence alignment and aligners (pdf).
• RNA-Seq data analysis and differential expression (pdf).
• New workflows for RNA-seq (pdf).
• Hypothesis testing (pdf).

Labs

• End-to-end RNA-Seq workflow (html)
• Independent hypothesis weighting (html)

Wednesday, June 14

Lectures

• Multiple testing (txt).
• Linear models (basic intro)) (html).
• Experimental design, batch effects and confounding (pdf).
• Robust statistics: median, MAD, rank test, Spearman, robust linear model (html).

Thursday, June 15

Lectures

• Visualization, the grammar of graphics and ggplot2 (pdf).
• Mass spec proteomics (html) and metabolomics (pdf).
• Clustering and classification (html).
• Resampling: cross-validation, bootstrap, and permutation tests (html).
• Analysis of microbiome marker gene data (pdf).

Labs

• Mass spec proteomics & metabolomics Proteomics (html), Metabolomics (html).
• MultiAssayExperiment (html), cheatsheet (pdf).

Friday, June 16

Lectures

• Gene set enrichment analysis (pdf).
• Working with large-scale (pdf) and remote (html) data.
• Developer Practices, Writing functions (html).
• Developer Practices, Writing packages (html).

Labs

• Graphics (pdf) (data: diabetes.csv).
• Machine learning (supervised) (pdf).
• Large data, performance, and parallelization; large-scale efficient computation with genomic intervals (html).