Introduction to R and Bioconductor BMI 731 Winter 2005

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Introduction to R and BioconductorBMI 731 Winter
2005

• Catalin Barbacioru
• Department of Biomedical Informatics
• Ohio State University

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References

• R Project (www.r-project.org)
• open-source language and environment for
  statistical computing and graphics.
• Comprehensive R Archive Network, CRAN
  (cran.r-project.org) source code and precompiled
  binary distributions for Linux, Windows, MacOS
  base and contributed packages.
• Bioconductor Project (www.bioconductor.org)
• open-source software for the analysis of
  biomedical and genomic data, mainly R packages.

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R Project

• R is a language and environment for statistical
  computing and graphics. It is a open source
  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.
• R provides a wide variety of statistical (linear
  and nonlinear modeling, classical statistical
  tests, time-series analysis, classification,
  clustering, ...) and graphical techniques, and is
  highly extensible. The S language is often the
  vehicle of choice for research in statistical
  methodology, and R provides an Open Source route
  to participation in that activity.

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R Project

• R can be extended (easily) via packages.
• An R package is a structured collection of code
  (R, C, or other), documentation, and/or data for
  performing specific types of analyses.
• Packages only need to be installed once, but ...
  they must be loaded with each new R session.
• Loading R function library, e.g.,
  library(Biobase)
• Various functions are available to obtain
  information on a package.
• For example, packageDescription returns the
  content of the DESCRIPTION file and .find.package
  returns the directory where the package was
  installed.
• gt packageDescription("hgu95av2")

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R Packages

• Analysis packages implementation of statistical
  and graphical methods. E.g. cluster , glm, graph,
  hexbin, lattice, rpart.
• Data packages Biological metadata packages
  consisting of environment objects for mappings
  between dierent gene identifiers (e.g., Aymetrix
  ID, GO ID, LocusLink ID, PubMed ID), CDF and
  probe sequence information for Aymetrix chips.
  E.g. GO, hgu95av2 , humanLLMappings, KEGG.
• Specialized/custom packages code, data,
  documentation, and exercises, for a particular
  project, article, or course. E.g. EMBO03
  Bioconductor course package golubEsets Golub
  et al. (2000) ALL/AML dataset yeastCC Spellman
  et al. (1998) yeast cell cycle dataset.

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R Packages

• Base packages (CRAN).
• E.g. base, graphics, RPackmethods, stats.
• Contributed packages (CRAN).
• E.g. ellipse, XML.
• Bioconductor packages.
• E.g. annotate, affy, marray, multtest, hgu95av2
  , ALL, EMBO03 .

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Bioconductor Project

• 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 25 core developers in the US, Europe,
  and Australia.
• Provide access to powerful statistical and
  graphical methods for the analysis of biomedical
  and genomic data.
• Facilitate the integration of biological metadata
  from WWW in the analysis of experimental data.
• E.g. GenBank, GO, LocusLink, PubMed.
• Provide training in computational and statistical
  methods.

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Bioconductor Packages

• Statistical methods cluster analysis, estimation
  and (multiple) testing for linear and non-linear
  models (with possibly censored continuous and
  polychotomous outcomes), resampling,
  visualization, etc.
• Biological assays cell-based assays, DNA
  microarrays (transcript levels, DNA copy number
  from CGH), proteomics, SAGE, SELDI-TOF, SNP, etc.
• Biological metadata from WWW GenBank, GO, KEGG,
  PubMed,etc
• Interfaces with other languages C, Java, Perl,
  Python, XML, etc. Omega Project
  (www.omegahat.org).
• Interactions with other projects BGL,
  GeneSpring, Graphviz, MAGE-ML, Resourcerer, etc.

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Bioconductor Packages

• Analysis packages e.g., annotate, affy, marray,
  multtest.
• Data packages
• Biological metadata mappings between dierent
  gene identifiers (e.g., AffyID, GO ID, LocusID,
  PMID), CDF and probe sequence information for
  Affymetrix chips.
• E.g. hgu95av2 , GO, KEGG.
• Experimental data code, data, and
  documentation for specific experiments or
  projects.
• ALL Chiaretti et al. (2004) ALL dataset.
• golubEsets Golub et al. (2000) ALL/AML dataset.
• yeastCC Spellman et al. (1998) yeast cell cycle
  dataset.

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Bioconductor Packages

• General infrastructure Biobase, Biostrings,
  DynDoc, reposTools, rhdf5 , ruuid, tkWidgets,
  widgetTools.
• Annotation annotate, AnnBuilder metadata
  packages.
• Graphics geneplotter, hexbin.
• Pre-processing Aymetrix oligonucleotide chip
  data affy, affycomp, affydata, affylmGUI ,
  affyPLM, annaffy, gcrma, makecdfenv, vsn.
• Pre-processing two-color spotted DNA microarray
  data arrayMagic, arrayQuality, limma, limmaGUI ,
  marray, vsn.
• Other assays aCGH, DNAcopy, prada, PROcess,
  RSNPer, SAGElyzer.
• Dierential gene expression EBarrays, edd,
  factDesign, genefilter, limma, limmaGUI ,
  multtest, ROC.
• Graphs and networks graph, RBGL, Rgraphviz .
• Gene Ontology GOstats, goTools.

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Microarray data analysis

• Pre-processing of
• spotted array data with marray packages
• Affymetrix chip data with affy packages.
• List of differentially expressed genes from
  genefilter, limma, or multtest packages.
• Prediction of tumor class using randomForest
  package.
• Clustering of genes using cluster or hopach
  packages.
• Use of annotate package
• to retrieve and search PubMed abstracts
• to generate an HTML report with links to
  LocusLink and PubMed for each gene.

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affy Package

• To load the necessary packages,
• gt library(affy)
• gt library(affydata)
• One of the main functions for reading in
  Affymetrix data is ReadAffy. It reads in data
  from CEL files and creates objects of class
  AffyBatch.
• In this lab we will work mainly with the Dilution
  dataset, which is included in the affydata
  package. To load the dataset, type
• gtdata(Dilution)
• For a description of Dilution, type
• gt? Dilution

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affy classes and methods

• One of the main classes in affy is the AffyBatch
  class.
• gtclass(Dilution)
• 1 AffyBatch
• gt slotNames(Dilution)
• 1 "cdfName "nrow "ncol" "exprs" "se.exprs
  "phenoData" 7"description" "annotation" "notes
• gtDilution
• AffyBatch object
• size of arrays640x640 features (12805 kb)
• cdfHG_U95Av2 (12625 affyids)
• number of samples4
• number of genes12625
• annotationhgu95av2

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affy classes and methods

• The exprs slot contains a matrix with columns
  corresponding to chips and rows to individual
  probes on the chip. To obtain the matrix of
  intensities for all four chips,
• gt e lt- exprs(Dilution)
• Probe-level PM and MM intensities can be accessed
  using the pm and mm methods.
• gt PM lt- pm(Dilution)

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affy classes and methods

• gt PM15,
• 20A 20B 10A 10B
• 1, 468.8 282.3 433.0 198.0
• 2, 430.0 265.0 308.5 192.8
• 3, 182.3 115.0 138.0 86.3
• 4, 930.0 588.0 752.8 392.5
• 5, 171.0 128.0 152.3 97.8

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affy classes and methods

• To get the probe-set names (Ay IDs),
• gt gnames lt- geneNames(Dilution)
• gt length(gnames)
• 1 12625
• gt gnames15
• 1 "1000_at" "1001_at" "1002_f_at" "1003_s_at"
  5"1004_at"

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affy classes and methods
To produce boxplots plots of log base 2 probe
intensities, gt boxplot(Dilution, col c(2, 2, 3,
3))
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affy classes and methods

• The boxplots show that the Dilution data needs
  normalization. As described in the dataset help
  file and in the phenoData slot (pData(Dilution)),
  two concentrations of mRNA were used and, for
  each concentration, two scanners were used. From
  the plots, we note that scanner effects seem
  stronger than concentration effects (different
  colors). In other words, chips that should be the
  same are different chips that should be
  different are similar.
• Because different mRNA concentrations were used,
  we perform normalization within concentration
  groups. The default procedure implemented in the
  normalize method is probe-level quantile
  normalization.

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affy classes and methods

• gt Dil20 lt- normalize(Dilution, 12)
• gt Dil10 lt- normalize(Dilution, 34)
• gt normDil lt- merge(Dil20, Dil10)
• gtboxplot(normDil, colc(2,2,3,3))

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affy classes and methods

• We view the process of going from probe-level
  intensities to gene-level expression measures as
  a three-step procedure consisting of (i)
  background adjustment (ii) normalization (iii)
  summarization. The affy package provides
  implementations for a number of methods for each
  of these steps (i) background correction e.g.,
  none, MAS 5.0, convolution (ii) normalization
  e.g., probe-level quantile, cyclic loess,
  contrast loess (iii) summarization e.g., MAS
  4.0, MAS 5.0, MBEI (Li Wong, 2001), median
  polish for additive linear model (Irizarry et
  al., 2003).
• The Robust Multichip Average (RMA) method refers
  to the sequence convolution background
  adjustment, probe-level quantile normalization,
  and median polish summarization for gene-specific
  additive models with probe and chip effects.
• gt rmaDil lt- rma(Dilution)

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affy classes and methods

• CDF data packages
• Data packages providing CDF information can be
  download from www.bioconductor.org. These
  packages contain environment objects which
  provide mappings between AffyIDs and matrices of
  probe locations, with rows corresponding to
  probe-pairs and columns to PM and MM cells. The
  CDF environment for the HGU95Av2 chip is already
  in the package. For information on the
  environment object type gt? hgu95av2cdf