Skip to main content

Microarray Data Analysis

  • Protocol
  • First Online:
Plant Reverse Genetics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 678))

Abstract

Gene expression profiling has revolutionized functional genomics research by providing a quick handle on all the transcriptional changes that occur in the cell in response to internal or external perturbations or developmental programs. Microarrays have become the most popular technology for recording gene expression profiles. This chapter describes all the necessary steps for analyzing Affymetrix microarray data using the open-source statistical tools (R and bioconductor). The reader is walked through all the basic steps of data analysis: reading raw data, assessing quality, preprocessing/normalization, discovery of differentially expressed genes, comparison of gene lists, functional enrichment analysis, and saving results to files for future reference. Some familiarity with computer is assumed. This chapter is self-contained with installation instructions for R and bioconductor packages along with links to downloadable data and code for reproducing the examples.

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

References

  1. Lockhart, D., Dong, H., Byrne, M., Follettie, M., Gallo, M., Chee, M., et al. (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol. 14: 1675–1680.

    Article  PubMed  CAS  Google Scholar 

  2. Bolstad, B. M., Irizarry, R. A., Åstrand, M., and Speed, T. P. (2003) A comparison of normalization methods for high-density oligonucleotide array data based on variance and bias. Bioinformatics. 19: 185–193.

    Article  PubMed  CAS  Google Scholar 

  3. Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U., et al. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4: 249–264.

    Article  PubMed  Google Scholar 

  4. Smyth, G. (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 3: Article3.

    Google Scholar 

  5. Benjamini, Y., and Hochberg, Y. (1995) Controlling false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B. 57: 289–300.

    Google Scholar 

  6. Nettleton, D. (2006) A discussion of statistical methods for design and analysis of microarray experiments for plant scientists. Plant Cell. 18: 2112–2121.

    Article  PubMed  CAS  Google Scholar 

  7. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 25: 25–29.

    Article  PubMed  CAS  Google Scholar 

  8. Clarke, J. D., and Zhu, T. (2006) Microarray analysis of the transcriptome as a stepping stone towards understanding biological systems: practical considerations and perspectives. Plant J. 45: 630–650.

    Article  PubMed  CAS  Google Scholar 

  9. Allison, D. B., Cui, X., Page, G. P., and Sabripour, M. (2006) Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet. 7: 55–65.

    Article  PubMed  CAS  Google Scholar 

  10. Cordero, F., Botta, M., and Calogero, R. A. (2007) Microarray data analysis and mining approaches. Brief Funct Genomic Proteomic. 6: 265–281.

    Article  PubMed  CAS  Google Scholar 

  11. Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5: R80.

    Article  PubMed  Google Scholar 

  12. R Development Core Team. (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. URL http://www.R-project.org.

  13. Kilian, J., Whitehead, D., Horak, J., Wanke, D., Weinl, S., Batistic, O., et al. (2007) The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses. Plant J. 50: 347–363.

    Article  PubMed  CAS  Google Scholar 

  14. Falcon, S., and Gentleman, R. (2007) Using GOstats to test gene lists for GO term association. Bioinformatics. 23: 257–258.

    Article  PubMed  CAS  Google Scholar 

  15. Swarbreck, D., Wilks, C., Lamesch, P., Berardini, T. Z., Garcia-Hernandez, M., Foerster, H., et al. (2008) The Arabidopsis Information Resource (TAIR): gene structure and function annotation. Nucleic Acids Res. 36: D1009–D1014.

    Article  PubMed  CAS  Google Scholar 

  16. Wilson, C. L., and Miller, C. J. (2005) Simpleaffy: a BioConductor package for Affymetrix quality control and data analysis. Bioinformatics. 21: 3683–3685.

    Article  PubMed  CAS  Google Scholar 

  17. Gautier, L., Cope, L., Bolstad, B. M., and Irizarry, R. A. (2004) affy – analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20: 307–315.

    Article  PubMed  CAS  Google Scholar 

  18. Wu, Z., Irizarry, R. A., Gentleman, R., Murillo, F. M., and Spencer, F. (2004) A model based background adjustment for oligonucleotide expression arrays. J Am Stat Assoc. 99: 909–917.

    Article  Google Scholar 

  19. Iliev, E. A., Xu, W., Polisensky, D. H., Oh, M. H., Torisky, R. S., Clouse, S. D., et al. (2002) Transcriptional and posttranscriptional regulation of Arabidopsis TCH4 expression by diverse stimuli. Roles of cis regions and brassinosteroids. Plant Physiol. 130: 770–783.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Mohapatra, S.K., Krishnan, A. (2011). Microarray Data Analysis. In: Pereira, A. (eds) Plant Reverse Genetics. Methods in Molecular Biology, vol 678. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-682-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-60761-682-5_3

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60761-681-8

  • Online ISBN: 978-1-60761-682-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics