MeV: MultiExperiment Viewer

  • Eleanor Howe
  • Kristina Holton
  • Sarita Nair
  • Daniel Schlauch
  • Raktim Sinha
  • John Quackenbush
Chapter

Abstract

MultiExperiment Viewer (MeV) is a freely available software application that puts modern bioinformatics tools for integrative data analysis in the hands of bench biologists. MeV is a versatile microarray data analysis tool, incorporating sophisticated algorithms for clustering, visualization, classification, statistical analysis, and biological theme discovery from single or multiple experiments. This chapter gives an overview of MeV technical details and its use in a real setting.

References

  1. Aryee MJ, Gutiérrez-Pabello JA, Kramnik I, Maiti T, Quackenbush J (2009) An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation). BMC Bioinformatics 10:409PubMedCrossRefGoogle Scholar
  2. Breitling R, Armengaud P, Amtmann A, Herzyk P (2004) Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett 573(1–3):83–92PubMedCrossRefGoogle Scholar
  3. Chiaretti S, Li X, Gentleman R, Vitale A, Vignetti M, Mandelli F, Ritz J, Foa R (2004) Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. Blood 103(7):2771–2778PubMedCrossRefGoogle Scholar
  4. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol 4(5):P3PubMedCrossRefGoogle Scholar
  5. Djebbari A, Quackenbush J (2008) Seeded Bayesian networks: constructing genetic networks from microarray data. BMC Syst Biol 2:57PubMedCrossRefGoogle Scholar
  6. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95(25):14863–14868PubMedCrossRefGoogle Scholar
  7. Hosack DA, Dennis G Jr, Sherman BT, Lane HC, Lempicki RA (2003) Identifying biological themes within lists of genes with EASE. Genome Biol 4(10):R70PubMedCrossRefGoogle Scholar
  8. Jiang Z, Gentleman R (2007) Extensions to gene set enrichment. Bioinformatics 23(3):306–313PubMedCrossRefGoogle Scholar
  9. Killcoyne S, Carter GW, Smith J, Boyle J (2009) Cytoscape: a community-based framework for network modeling. Methods Mol Biol 563:219–239PubMedCrossRefGoogle Scholar
  10. Kim SY, Volsky DJ (2005) PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics 6:144PubMedCrossRefGoogle Scholar
  11. R_Development_Core_Team (2005) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, AustriaGoogle Scholar
  12. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, Sturn A, Snuffin M, Rezantsev A, Popov D, Ryltsov A, Kostukovich E, Borisovsky I, Liu Z, Vinsavich A, Trush V, Quackenbush J (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34(2):374–8PubMedGoogle Scholar
  13. Shannon PT, Reiss DJ, Bonneau R, Baliga NS (2006) The Gaggle: an open-source software system for integrating bioinformatics software and data sources. BMC Bioinformatics 7:176PubMedCrossRefGoogle Scholar
  14. Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article3Google Scholar
  15. Smyth GK (2005) Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W (eds) Bioinformatics and computational biology solutions using R and bioconductor. Springer, New York, pp 397–420CrossRefGoogle Scholar
  16. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550PubMedCrossRefGoogle Scholar
  17. Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A 99(10):6567–6572PubMedCrossRefGoogle Scholar
  18. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98(9):5116–5121PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Eleanor Howe
  • Kristina Holton
  • Sarita Nair
  • Daniel Schlauch
  • Raktim Sinha
  • John Quackenbush
    • 1
  1. 1.Dana-Farber Cancer InstituteBostonUSA

Personalised recommendations