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BioConductor. Steffen Durinck Robert Gentleman Sandrine Dudoit November 28, 2003 NETTAB Bologna. Outline. what is R what is Bioconductor packages getting and using Bioconductor. R.

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Bioconductor l.jpg

BioConductor

Steffen Durinck

Robert Gentleman

Sandrine Dudoit

November 28, 2003

NETTAB Bologna


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Outline

  • what is R

  • what is Bioconductor

  • packages

  • getting and using Bioconductor


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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 AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S.


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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


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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


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Overview of the Bioconductor Project


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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.


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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.


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Bioconductor Packages


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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.


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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


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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


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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


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Microarray data analysis

.gpr, .Spot, MAGEML

CEL, CDF

marray

limma

vsn

affy

vsn

Pre-processing

exprSet

Annotation

Differential

expression

Graphs &

networks

annotate

annaffy

+ metadata

packages

Cluster

analysis

Prediction

CRAN

class

e1071

ipred

LogitBoost

MASS

nnet

randomForest

rpart

edd

genefilter

limma

multtest

ROC

+ CRAN

graph

RBGL

Rgraphviz

CRAN

class

cluster

MASS

mva

Graphics

geneplotter

hexbin

+ CRAN


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marray packages

  • Pre-processing two-color spotted array data:

  • diagnostic plots,

  • robust adaptive normalization (lowess, loess).

maImage

maBoxplot

maPlot + hexbin


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affy package

  • Pre-processing oligonucleotide chip data:

  • diagnostic plots,

  • background correction,

  • probe-level normalization,

  • computation of expression measures.

plotAffyRNADeg

barplot.ProbeSet

plotDensity

image


Annotate annafy and annbuilder l.jpg

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.

annotate, annafy, and AnnBuilder

Metadata package hgu95av2

mappings between different gene

identifiers for hgu95av2 chip.

GENENAME

zinc finger protein 261

LOCUSID

9203

ACCNUM

X95808

MAP

Xq13.1

AffyID

41046_s_at

SYMBOL

ZNF261

PMID

10486218

9205841

8817323

GO

GO:0003677

GO:0007275

GO:0016021

+ many other mappings


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MAGEML package

<!DOCTYPE MAGE-ML SYSTEM "D:/DATA/MAGE-ML/MAGE-ML.dtd">

<MAGE-ML identifier="MAGE-ML:E-SNGR-4">

<QuantitationTypeDimension_assnlist>

<QuantitationTypeDimension identifier="QTD:1">

<QuantitationTypes_assnreflist>

<MeasuredSignal_ref identifier="QT:F635 Median"/>

<MeasuredSignal_ref identifier="QT:F635 Mean"/>

….

marray packages

(cDNA arrays)


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SIGGENES PACKAGE - SAM


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multtest package

  • Multiple hypothesis testing

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


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mva package -clustering

heatmap


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mva package – principal component analysis


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Getting started


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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.


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Installation

  • After installing R, install Bioconductor packages using getBioC install script.

  • From R

    > source("http://www.bioconductor.org/getBioC.R")

    > getBioC()

  • In general, R packages can be installed using the function install.packages.

  • In Windows, can also use “Packages” pull-down menus.


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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.


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Widgets

Reading in phenoData

tkSampleNames

tkphenoData

tkMIAME


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Documentation and help

  • R manuals and tutorials:available 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.

    > help.start()

    > help(lm)

    > ?hclust

    > apropos(mean)

    > example(hclust)

    > demo()

    > demo(image)


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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.


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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.


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Vignettes

  • HowTo’s: Task-oriented descriptions of package functionality.

  • Executable documents consisting of documentation text and code chunks.

  • Dynamic, integrated, and reproduciblestatistical documents.

  • Can be used interactively – vExplorer.

  • Generated using Sweave (toolspackage).

vExplorer


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References

  • Rwww.r-project.org, cran.r-project.org

    • software (CRAN);

    • documentation;

    • newsletter: R News;

    • mailing list.

  • Bioconductorwww.bioconductor.org

    • software, data, and documentation (vignettes);

    • training materials from short courses;

    • mailing list.

  • Personal

    • [email protected]


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acknowledgements

  • Robert Gentleman

    Department of Biostatistical Science,Dana Faber Cancer Institute, Boston

  • Sandrine Dudoit

    Division Biostatistics, University of California, Berkeley


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