Skip to main content

Part of the book series: Statistics for Biology and Health ((SBH))

Abstract

DNA-Chip Analyzer (dChip) is a software package implementing model-based expression analysis of oligonucleotide arrays and several high-level analysis procedures. The model-based approach allows probe-level analysis on multiple arrays. By pooling information across multiple arrays, it is possible to assess standard errors for the expression indexes. This approach also allows automatic probe selection in the analysis stage to reduce errors due to cross-hybridizing probes and image contamination. High-level analysis in dChip includes comparative analysis and hierarchical clustering. The software is freely available to academic users at www.dchip.org.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Affymetrix, Inc. (2001). Microarray Suite 5.0, Affymetrix, Inc.: Santa Clara, CA.

    Google Scholar 

  • Cho RJ, Huang M, Campbell MJ, Dong H, Steinmetz L, Sapinoso L, Hampton G, Elledge SJ, Davis RW, Lockhart DJ (2001). Transcriptional regulation and function during the human cell cycle. Nature Genetics, 27:48–54.

    Google Scholar 

  • Cox, DR, Hinkley DV (1974). Theoretical Statistics. Chapman and Hall: London.

    Book  MATH  Google Scholar 

  • Dudoit S, Fridlyand J, Speed TP (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, 97(457):77–87.

    Article  MATH  MathSciNet  Google Scholar 

  • Eisen MB, Spellman PT, Brown PO, Botstein D (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences USA, 95:14863–14868.

    Article  Google Scholar 

  • Hakak Y, Walker JR, Li C, Wong WH, Davis KL, Buxbaum JD, Haroutunian V, Fienberg AA (2001). Genome-wide expression analysis reveals dysregulation of myelination-related genes in chronic schizophrenia. Proceedings of the National Academy of Sciences USA, 98:4746–4751.

    Article  Google Scholar 

  • Hill AA, Brown EL, Whitley MZ, Tucker-Kellogg G, Hunter GP, Slonim DK (2001). Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA controls. Genome Biology, 2(12):research0055.1–0055.13.

    Google Scholar 

  • Hoffmann R, Seidl T, Dugas M (2002). Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis. Genome Biology, 3(7):research0033.1–0033.11.

    Google Scholar 

  • Holder D, Raubertas RF, Pikounis VB, Svetnkik V, Soper K (2001). Statistical analysis of high density oligonucleotide arrays: A SAFER approach. Proceedings of the American Statistical Association.

    Google Scholar 

  • Ihaka R, Gentleman R (1996). R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3):299–314.

    Google Scholar 

  • Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed T (in press). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics.

    Google Scholar 

  • Lazaridis EN, Sinibaldi D, Bloom G, Mane S, Jove R (2002). A simple method to improve probe set estimates from oligonucleotide arrays. Mathematical Biosciences, 176(1):53–58.

    Article  MATH  MathSciNet  Google Scholar 

  • Li C, Wong WH (2001a). Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Proceedings of the National Academy of Sciences USA, 98:31–36.

    Article  MATH  Google Scholar 

  • Li C, Wong WH (2001b). Model-based analysis of oligonucleotide arrays: Model validation, design issues and standard error application. Genome Biology, 2(8): research0032.1–0032.11.

    Google Scholar 

  • Lipshutz RJ, Fodor S, Gingeras T, Lockhart D (1999). High density synthetic ologonucleotide arrays. Nature Genetics Supplement, 21:20–24.

    Article  Google Scholar 

  • Lockhart D, Dong H, Byrne M, Follettie M, Gallo M, Chee M, Mittmann M, Wang C, Kobayashi M, Horton H, Brown E (1996). Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnology, 14:1675–1680.

    Article  Google Scholar 

  • Schadt EE, Li C, Su C, Wong WH (2001). Analyzing high-density oligonucleotide gene expression array data. Journal Cellular Biochemistry, 80:192–202.

    Article  Google Scholar 

  • Schadt EE, Li C, Ellis B, Wong WH (2002). Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. Journal of Cellular Biochemistry, 84(S37):120–125.

    Article  Google Scholar 

  • Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM (1999). Systematic determination of genetic network architecture. Nature Genetics, 22:281–285.

    Article  Google Scholar 

  • Wallace D (1988). The Behrens-Fisher and Fieller-Creasy Problems. In: Fienberg SE, Hinkley DV (eds) R.A. Fisher: An Appreciation. pp.119–117. Lecture Notes in Statistics, Volume 1, Springer-Verlag: New York.

    Google Scholar 

  • Zhou Y, Abagyan R (2002). Match-only integral distribution (MOID) algorithm for high-density oligonucleotide array analysis. BMC Bioinformatics, 3:3.

    Article  Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag New York, Inc.

About this chapter

Cite this chapter

Li, C., Wong, W.H. (2003). DNA-Chip Analyzer (dChip). In: Parmigiani, G., Garrett, E.S., Irizarry, R.A., Zeger, S.L. (eds) The Analysis of Gene Expression Data. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-21679-0_5

Download citation

  • DOI: https://doi.org/10.1007/0-387-21679-0_5

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-95577-3

  • Online ISBN: 978-0-387-21679-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics