BioConductor

1
BioConductor

• Steffen Durinck
• Robert Gentleman
• Sandrine Dudoit
• November 28, 2003
• NETTAB Bologna

2
Outline

• what is R
• what is Bioconductor
• packages
• getting and using Bioconductor

3
R

• R is a language and environment for statistical
  computing and graphics. It is a GNU project which
  is similar to the S language and environment
  which was developed at Bell Laboratories
  (formerly ATT, now Lucent Technologies) by John
  Chambers and colleagues. R can be considered as a
  different implementation of S.

4
R

• what sorts of things is R good at?
• there are very many statistical algorithms
• there are very many machine learning algorithms
• visualization
• it is possible to write scripts that can be
  reused
• R is a real computer language

5
R

• R supports many data technologies
• XML,database integration,SOAP
• R interacts with other languages
• C FORTRAN Perl Python Java
• R has good visualization capabilities
• R has a very active development environment
• R is largely platform independent
• Unix Windows OSX

6
Overview of the Bioconductor Project
7
Bioconductor

• Bioconductor is an open source and open
  development software project for the analysis of
  biomedical and genomic data.
• The project was started in the Fall of 2001 and
  includes 23 core developers in the US, Europe,
  and Australia.
• R and the R package system are used to design and
  distribute software.
• Releases
• v 1.0 May 2nd, 2002, 15 packages.
• v 1.1 November 18th, 2002, 20 packages.
• v 1.2 May 28th, 2003, 30 packages.
• v 1.3 October 28th, 2003, 54
  packages.
• ArrayAnalyzer Commercial port of Bioconductor
  packages in S-Plus.

8
Goals

• Provide access to powerful statistical and
  graphical methods for the analysis of genomic
  data.
• Facilitate the integration of biological metadata
  (GenBank, GO, LocusLink, PubMed) in the analysis
  of experimental data.
• Allow the rapid development of extensible,
  interoperable, and scalable software.
• Promote high-quality documentation and
  reproducible research.
• Provide training in computational and statistical
  methods.

9
Bioconductor Packages
10
Bioconductor packages

• Bioconductor software consists of R add-on
  packages.
• An R package is a structured collection of code
  (R, C, or other), documentation, and/or data for
  performing specific types of analyses.
• E.g. affy, cluster, graph, hexbin packages
  provide implementations of specialized
  statistical and graphical methods.

11
Bioconductor packagesRelease 1.3, October 28th,
2003

• AnnBuilder Bioconductor annotation data package
  builder
• Biobase Biobase Base functions for Bioconductor
• DynDoc Dynamic document tools
• MAGEML handling MAGEML documents
• MeasurementError.cor Measurement Error model
  estimate for correlation coefficient
• RBGL Test interface to boost C graph lib
• ROC utilities for ROC, with uarray focus
• RdbiPgSQL PostgreSQL access
• Rdbi Generic database methods
• Rgraphviz Provides plotting capabilities for R
  graph objects
• Ruuid Ruuid Provides Universally Unique ID
  values
• SAGElyzer A package that deals with SAGE
  libraries
• SNPtools Rudimentary structures for SNP data
• affyPLM affyPLM - Probe Level Models
• Affy Methods for Affymetrix Oligonucleotide
  Arrays
• Affycomp Graphics Toolbox for Assessment of
  Affymetrix Expression Measures
• Affydata Affymetrix Data for Demonstration
  Purpose
• Annaffy Annotation tools for Affymetrix
  biological metadata
• Annotate Annotation for microarrays

12

Bioconductor packagesRelease 1.3, October 28th,
2003

• Ctc Cluster and Tree Conversion.
• daMA Efficient design and analysis of factorial
  two-colour microarray data
• Edd expression density diagnostics
• externalVector Vector objects for R with external
  storage
• factDesign Factorial designed microarray
  experiment analysis
• Gcrma Background Adjustment Using Sequence
  Information
• Genefilter Genefilter filter genes
• Geneplotter Geneplotter plot microarray data
• Globaltest Global Test
• Gpls Classification using generalized partial
  least squares
• Graph graph A package to handle graph data
  structures
• Hexbin Hexagonal Binning Routines
• Limma Linear Models for Microarray Data
• Makecdfenv CDF Environment Maker
• marrayClasses Classes and methods for cDNA
  microarray data
• marrayInput Data input for cDNA microarrays
• marrayNorm Location and scale normalization for
  cDNA microarray data
• marrayPlots Diagnostic plots for cDNA microarray
  data
• marrayTools Miscellaneous functions for cDNA
  microarrays

13
Bioconductor packagesRelease 1.3, October 28th,
2003

• Matchprobes Tools for sequence matching of probes
  on arrays
• Multtest Multiple Testing Procedures
• ontoTools graphs and sparse matrices for working
  with ontologies
• Pamr Pam prediction analysis for microarrays
• reposTools Repository tools for R
• Rhdf5 An HDF5 interface for R
• Siggenes Significance and Empirical Bayes
  Analyses of Microarrays
• Splicegear splicegear
• tkWidgets R based tk widgets
• Vsn Variance stabilization and calibration for
  microarray data
• widgetTools Creates an interactive tcltk widgets

14
Microarray data analysis
.gpr, .Spot, MAGEML
CEL, CDF
marray limma vsn
affy vsn
Pre-processing
exprSet
Annotation
annotate annaffy metadata packages
Differential expression
Graphs networks
Cluster analysis
Prediction
CRAN class e1071 ipred LogitBoost MASS nnet random
Forest rpart
graph RBGL Rgraphviz
edd genefilter limma multtest ROC CRAN
CRAN class cluster MASS mva
Graphics
geneplotter hexbin CRAN
15
marray packages

• Pre-processing two-color spotted array data
• diagnostic plots,
• robust adaptive normalization (lowess, loess).

maImage
maBoxplot
maPlot hexbin
16
affy package

• Pre-processing oligonucleotide chip data
• diagnostic plots,
• background correction,
• probe-level normalization,
• computation of expression measures.

plotAffyRNADeg
barplot.ProbeSet
image
plotDensity
17
annotate, annafy, and AnnBuilder
Metadata package hgu95av2 mappings between
different gene identifiers for hgu95av2 chip.

• Assemble and process genomic annotation data from
  public repositories.
• Build annotation data packages or XML data
  documents.
• Associate experimental data in real time to
  biological metadata from web databases such as
  GenBank, GO, KEGG, LocusLink, and PubMed.
• Process and store query results e.g., search
  PubMed abstracts.
• Generate HTML reports of analyses.

GENENAME zinc finger protein 261
LOCUSID 9203
ACCNUM X95808
MAP Xq13.1
AffyID 41046_s_at
SYMBOL ZNF261
PMID 10486218 9205841 8817323
GO GO0003677 GO0007275 GO0016021
many other mappings
18
MAGEML package
lt!DOCTYPE MAGE-ML SYSTEM "D/DATA/MAGE-ML/MAGE-ML.
dtd"gt ltMAGE-ML identifier"MAGE-MLE-SNGR-4"gt ltQua
ntitationTypeDimension_assnlistgt ltQuantitationType
Dimension identifier"QTD1"gt ltQuantitationTypes_a
ssnreflistgt ltMeasuredSignal_ref
identifier"QTF635 Median"/gt ltMeasuredSignal_ref
identifier"QTF635 Mean"/gt
.
marray packages (cDNA arrays)
19
SIGGENES PACKAGE - SAM
20
multtest package

• Multiple hypothesis testing
• Control type I error rate by using e.g.
  Bonferroni method

21
mva package -clustering

heatmap
22
mva package principal component analysis
23
Getting started
24
Installation

• Main R software download from CRAN
  (cran.r-project.org), use latest release, now
  1.8.0.
• Bioconductor packages download from Bioconductor
  (www.bioconductor.org), use latest release, now
  1.3.
• Available for Linux/Unix, Windows, and Mac OS.

25
Installation

• After installing R, install Bioconductor packages
  using getBioC install script.
• From R
• gt source("http//www.bioconductor.org/getBioC.R")
  
• gt getBioC()
• In general, R packages can be installed using the
  function install.packages.
• In Windows, can also use Packages pull-down
  menus.

26
User interaction

• R Command-line
• Widgets. Small-scale graphical user interfaces
  (GUI), providing point click access for
  specific tasks.
• E.g. File browsing and selection for data input,
  basic analyses.

27
Widgets
Reading in phenoData
tkSampleNames
tkphenoData
tkMIAME
28
Documentation and help

• R manuals and tutorialsavailable from the R
  website or on-line in an R session.
• R on-line help system detailed on-line
  documentation, available in text, HTML, PDF, and
  LaTeX formats.
• gt help.start()
• gt help(lm)
• gt ?hclust
• gt apropos(mean)
• gt example(hclust)
• gt demo()
• gt demo(image)

29
Short courses

• Bioconductor short courses
• modular training segments on software and
  statistical methodology
• lectures notes, computer labs, and course
  packages available on WWW for self-instruction.

30
Vignettes

• Bioconductor has adopted a new documentation
  paradigm, the vignette.
• A vignette is an executable document consisting
  of a collection of code chunks and documentation
  text chunks.
• Vignettes provide dynamic, integrated, and
  reproducible statistical documents that can be
  automatically updated if either data or analyses
  are changed.
• Each Bioconductor package contains at least one
  vignette, providing task-oriented descriptions of
  the package's functionality.

31
Vignettes

• HowTos Task-oriented descriptions of package
  functionality.
• Executable documents consisting of documentation
  text and code chunks.
• Dynamic, integrated, and reproducible
  statistical documents.
• Can be used interactively vExplorer.
• Generated using Sweave (tools package).

vExplorer
32
References

• R www.r-project.org, cran.r-project.org
• software (CRAN)
• documentation
• newsletter R News
• mailing list.
• Bioconductor www.bioconductor.org
• software, data, and documentation (vignettes)
• training materials from short courses
• mailing list.
• Personal
• sdurinck_at_esat.kuleuven.ac.be

33
acknowledgements

• Robert Gentleman
• Department of Biostatistical Science, Dana Faber
  Cancer Institute, Boston
• Sandrine Dudoit
• Division Biostatistics, University of California,
  Berkeley