BioConductor

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BioConductor
Bioinformatics and Computational Molecular
Biology

• R93922048 Chia-Chi Yeh
• R93922002 Tzu-Kuo Huang

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Outline

• Introduction to BioConductor
• Introduction to R
• Installation
• Packages
• Affy
• Demonstration
• Questions and Answers

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Introduction to BioConductor (1/2)

• Bioconductor is an open source and open
  development software project based on R, to
  provide tools for the analysis and comprehension
  of genomic data (bioinformatics).

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Introduction to BioConductor (2/2)

• The broad goals of the projects are to
• provide access to a wide range of powerful
  statistical and graphical methods for the
  analysis of genomic data
• facilitate the integration of biological metadata
  in the analysis of experimental data
• allow the rapid development of extensible,
  scalable, and interoperable software
• provide training in computational and statistical
  methods for the analysis of genomic data.

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Introduction to R (1/2)

• R is a language and environment for statistical
  computing and graphics.

• R is an integrated suite of software facilities
  for data manipulation, calculation and graphical
  display.

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Introduction to R (2/2)

• R provides a wide variety of statistical, such as
• linear and nonlinear modeling,
• classical statistical tests,
• time-series analysis,
• classification,
• and clustering.

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Installation

• Since BioConductor is based on R, we need to
  install R first. To get more information, just
  google for CRAN and see the first record.
• The installation script getBioC.R is also written
  in R, so we start a R session.
• To get the script, in the R session, type
• source("http//www.bioconductor.org/getBioC.R")
• Then we call getBioC() in the R session to
  install Bioconductor packages
• getBioC()

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Packages (1/3)

• There are two main types of packages.
• One set provide basic infrastructure support that
  will help other developers produce high quality
  software for the analysis of genomic data.
• The other variety provide innovative methodology
  for analyzing genomic data.

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Packages (2/3)

• Some packages are related to this course
• Affy provides methods for Affymetrix
  Oligonucleotide Arrays.
• Gostats includes tools for manipulating GO and
  microarrays.
• Sagenhaft is a collection of functions for
  reading and comparing SAGE libraries.

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Packages (3/3)

• The full package index is listed at
• http//www.bioconductor.org/repository/release1.5/
  package/html/index.html
• Vignettes can be download from
• http//www.bioconductor.org/viglistingindex.html
• Package descriptions, function descriptions and
  tutorials can be found from the links above.

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Affy (1/3)

• The package contains functions for exploratory
  affymetrix oligonucleotide arrays analysis.
• There are two main classes.
• AffyBatch is a class representation of
  affymetrix genechip probe level data, loaded from
  CEL files.
• ProbeSet contains PM and MM intensities for
  individual probe sets.

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Affy (2/3)

• ReadAffy() read CEL files into an AffyBatch.
• pData() and phenoData() get descriptions from
  AffyBatch.
• sampleNames() and geneNames() get sample names
  or gene names from AffyBatch.

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Affy (3/3)

• pm() and mm() get ProbeSets of perfect matches
  and mismatches from AffyBatch.
• hist() draw the histogram of PM intensities.
• image() produce spatial images of probe log
  intensities and probe raw intensities.

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Demonstration

• Here is a demonstration of the following steps
• Create a R session.
• Load the affy library of BioConductor.
• Load a AffyBatch, Dilution.
• Get information from Dilution.
• Get PM and MM intensities of some genes.
• draw the histogram of PM intensities.
• produce images of probe intensities.

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Questions and Answers

• Any questions?