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Learning R / Bioconductor for Sequence Analysis

Seattle, USA

2014-10-27 ~ 2014-10-29

Instructors

  • Sonali Arora
  • Nathaniel Hayden
  • Martin Morgan
  • Hervé Pagès
  • Marc Carlson
  • Valerie Obenchain
  • Dan Tenenbaum
  • Paul Shannon

Description

This course is directed at beginning and intermediate users who would like an introduction to the analysis and comprehension of high-throughput sequence data using R and Bioconductor. Day 1 focuses on learning essential background: an introduction to the R programming language; central concepts for effective use of Bioconductor software; and an overview of high-throughput sequence analysis work flows. Day 2 emphasizes use of Bioconductor for specific tasks: an RNA-seq differential expression work flow; exploratory, machine learning, and other statistical tasks; gene set enrichment; and annotation. Day 3 transitions to understanding effective approaches for managing larger challenges: strategies for working with large data, writing re-usable functions, developing reproducible reports and work flows, and visualizing results. The course combines lectures with extensive hands-on practicals; students are required to bring a laptop with wireless internet access and a modern version of the Chrome or Safari web browser.

Materials

Download the package (containing all material) for use with R-3.1.1 / Bioconductor 3.0.

Install the course package with

install.packages("LearnBioconductor_0.1.6.tar.gz", repos=NULL)

Optionally install suggested packages (used in exercises, etc.)

source("http://bioconductor.org/biocLite.R")
biocLite(c("knitr", "BiocStyle", "BiocInstaller", "ALL",
    "BSgenome.Hsapiens.UCSC.hg19", "BiocParallel", "Biostrings",
    "GenomicAlignments", "GenomicFeatures", "Gviz", "MLSeq",
    "PoiClaClu", "RColorBrewer", "RNAseqData.HNRNPC.bam.chr14",
    "Rsamtools", "ShortRead", "TxDb.Hsapiens.UCSC.hg19.knownGene",
    "VariantAnnotation", "airway", "class", "cn.mops",
    "dendextend", "fission", "genefilter", "ggplot2", "gplots",
    "org.Hs.eg.db", "sva", "xtable", "PoiClaClu", "sva",
    "fission", "kernlab", "e1071"))

Explore the material through the following documents: