Skip to main content
SpringerLink
Log in

R Classes and Methods for SNP Array Data

  • Protocol
  • First Online:
Bioinformatics Methods in Clinical Research

Part of the book series: Methods in Molecular Biology ((MIMB,volume 593))

Abstract

The Bioconductor project is an “open source and open development software project for the analysis and comprehension of genomic data” (1), primarily based on the R programming language. Infrastructure packages, such as Biobase, are maintained by Bioconductor core developers and serve several key roles to the broader community of Bioconductor software developers and users. In particular, Biobase introduces an S4 class, the eSet, for high-dimensional assay data. Encapsulating the assay data as well as meta-data on the samples, features, and experiment in the eSet class definition ensures propagation of the relevant sample and feature meta-data throughout an analysis. Extending the eSet class promotes code reuse through inheritance as well as interoperability with other R packages and is less error-prone. Recently proposed class definitions for high-throughput SNP arrays extend the eSet class. This chapter highlights the advantages of adopting and extending Biobase class definitions through a working example of one implementation of classes for the analysis of high-throughput SNP arrays.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol5(10):R80.

    Article  PubMed  Google Scholar 

  2. Di X, Matsuzaki H, Webster TA, Hubbell E, Liu G, Dong S, Bartell D, Huang J, Chiles R, Yang G, Mei Shen M, Kulp D, Kennedy GC, Mei R, Jones KW, Cawley S. (2005) Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays. Bioinformatics 21(9):1958–1963.

    Article  CAS  PubMed  Google Scholar 

  3. Rabbee N, Speed TP. (2006) A genotype calling algorithm for Affymetrix SNP arrays. Bioinformatics 22(1):7–12.

    Article  CAS  PubMed  Google Scholar 

  4. Affymetrix. (2006) BRLMM: an improved genotype calling method for the genechip human mapping 500 k array set. Tech. rep., Affymetrix, Inc. White paper, Santa Clara, CA.

    Google Scholar 

  5. Carvalho B, Bengtsson H, Speed TP, Irizarry RA. (2007) Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data. Biostatistics 8(2):485–499.

    Article  PubMed  Google Scholar 

  6. Nannya Y, Sanada M, Nakazaki K, Hosoya N, Wang L, Hangaishi A, Kurokawa M, Chiba S, Bailey DK, Kennedy GC, Ogawa S. (2005) A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism genotyping arrays. Cancer Res 65(14):6071–6079.

    Article  CAS  PubMed  Google Scholar 

  7. Huang J, Wei W, Chen J, Zhang J, Liu G, Di X, Mei R, Ishikawa S, Aburatani H, Jones KW, Shapero MH. (2006) CARAT: a novel method for allelic detection of DNA copy number changes using high density oligonucleotide arrays. BMC Bioinformatics 7:83.

    Article  PubMed  Google Scholar 

  8. Laframboise T, Harrington D, Weir BA. (2006) PLASQ: a generalized linear model-based procedure to determine allelic dosage in cancer cells from SNP array data. Biostatistics 8(2):323–336.

    Article  PubMed  Google Scholar 

  9. Carter NP. (2007) Methods and strategies for analyzing copy number variation using DNA microarrays. Nat Genet 39(7 Suppl):S16–S21.

    Article  CAS  PubMed  Google Scholar 

  10. Chambers JM. (1998) Programming with Data: A Guide to the S Language, Springer-Verlag, New York.

    Google Scholar 

  11. Scharpf RB, Ting JC, Pevsner J, Ruczinski I. (2007) SNPchip: R classes and methods for SNP array data. Bioinformatics 23(5): 627–628.

    Article  CAS  PubMed  Google Scholar 

  12. Scharpf RB, Parmigiani G, Pevsner J, Ruczinski I. (2008) Hidden Markov models for the assessment of chromosomal alterations using high-throughput SNP arrays. Ann Appl Stat 2(2):687–713.

    Article  PubMed  Google Scholar 

  13. Leisch F. (2003) Sweave and beyond: Computations on text documents. In Kurt Hornik, Friedrich Leisch, and Achim Zeileis (eds). Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria, 2003.Sarkar D. (2008) Lattice: Multivariate Data Visualization with R. Springer, New York.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Robert B. Scharpf & Ingo Ruczinski

Authors
  1. Robert B. Scharpf
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ingo Ruczinski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Inst. Patologia e Imunologia Molecular, Universidade do Porto, Rua Dr. Roberto Frias s/n, Porto, 4200-465, Portugal

    Rune Matthiesen

Appendix

Appendix

This document was created using Sweave (13).

  • R version 2.8.0 Under development (unstable) (2008-06-18 r45949), powerpc-apple-darwin8.11.0

  • Locale: C

  • Base packages: base, datasets, grDevices, graphics, methods, stats, tools, utils

  • Other packages: Biobase 2.1.0, DBI 0.2-4, RSQLite 0.6-4, SNPchip 1.5.2, VanillaICE 1.3.7, oligoClasses 1.1.22, pd.mapping50k.hind240 0.4.1, pd.mapping50k.xba240 0.4.1

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Humana Press, a part of Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Scharpf, R.B., Ruczinski, I. (2010). R Classes and Methods for SNP Array Data. In: Matthiesen, R. (eds) Bioinformatics Methods in Clinical Research. Methods in Molecular Biology, vol 593. Humana Press. https://doi.org/10.1007/978-1-60327-194-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-60327-194-3_4

  • Published:

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-60327-193-6

  • Online ISBN: 978-1-60327-194-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation