BioConductor/R framework

What is Bioconductor? (http://www.bioconductor.org)

• Bioconductor is an open source development software project
• Provides tools for analysis and comprehension of genomic data
• Extensively for Affymetrix and cDNA microarray technologies
• The project started in the autumn of 2001
• Includes 23 core collaborating developers
• Project Growth

v1.0: May 2nd, 2002, 15 packages

v1.1: November 18th, 2002, 20 packages.

v1.2: May 28th, 2003, 30 packages.

v1.3 Oct 29th 2003, 49 Packages

v1.4 May 18 2004, 82 Packages

v1.5 Nov 1st, 2004, 98 Packages

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Bioconductor Goals

The broad goals of the project are:

• To enable sound and powerful statistical analyses in genomics
• To provide a computing platform that allows the rapid design and deployment of high-quality software
• To develop a computing environment for both biologists and statisticians
• Promote high-quality dynamic documentation and reproducible research
• Using L^AT_EX, the Sweave system and tcl/tk to deliver interactive step by step pdf tutorials, e.g.

library(tkWidgets)
vExplorer()

File:Sweave diagram.tiff

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Object oriented class method design (OOP)

• Organized approach to handling large amounts of experimental data
• Class structure encapsulates the data required for microarray analysis → object
• Allows efficient representation and manipulation (including subsetting) of data from many microarray slides in an experiment
• A method is a function that performs an action on data (objects) throughout analysis

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Advantages

• Newest cutting edge statistical methods available
• Modern programming language
• Powerful graphical tools available
• Its freely available

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Disadvantages

• Steep learning curve
• Need to have experience programming in the R programming environment (http://www.r-project.org)
• Like all software there are bugs

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Accessing Bioconductor

• Bioconductor tools are accessed using the R programming language
• R is a programming environment for statistical computing and graphics
• Initially written by Robert Gentleman and Ross Ihaka (Auckland University)
• Download R from a Comprehensive R archive network (CRAN) mirror (http://cran.stat.auckland.ac.nz)
• Install R (available for Unix, Windows, and Mac OS X)
• R version 2.2.1 has been released on 2005-12-20
• R is the environment used to design and distribute software:
  • Locally downloaded files
  • Via the internet e.g. Commands in R

# Setting proxy variable # Downloading installation script

getBioC()                                                  # Running script

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Installing vs loading packages

• Packages only need to be installed once onto a computer
• Packages must be loaded with each new R session
• The R function library is used to load packages e.g.

library(limma)     #Installs the limma package 

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Documentation and Help

• R manuals and tutorials are available from the R website or on-line in an R session
• R on-line help system, detailed high quality on-line documentation, available in text, HTML, PDF, and L^AT_EX formats.

help.start()           # Browser based help documentation
help()                 # Help on a topic
? ls                   # alternative help method on ls function
apropos(mean)          # Find Objects by (Partial) Name
example(mean)          # Run an Examples Section from the Online Help
demo()                 # Demonstrations of R Functionality
demo(graphics)         # Demonstration or graphics Functionality
data()                 # Publically available datasets

• Frequently asked questions Bioconductor / R
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