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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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 10:409
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):83–92
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):2771–2778
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):P3
Djebbari A, Quackenbush J (2008) Seeded Bayesian networks: constructing genetic networks from microarray data. BMC Syst Biol 2:57
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):14863–14868
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):R70
Jiang Z, Gentleman R (2007) Extensions to gene set enrichment. Bioinformatics 23(3):306–313
Killcoyne S, Carter GW, Smith J, Boyle J (2009) Cytoscape: a community-based framework for network modeling. Methods Mol Biol 563:219–239
Kim SY, Volsky DJ (2005) PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics 6:144
R_Development_Core_Team (2005) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria
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):374–8
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 7:176
Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article3
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 397–420
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):15545–15550
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):6567–6572
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):5116–5121
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Howe, E., Holton, K., Nair, S., Schlauch, D., Sinha, R., Quackenbush, J. (2010). MeV: MultiExperiment Viewer. In: Ochs, M., Casagrande, J., Davuluri, R. (eds) Biomedical Informatics for Cancer Research. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5714-6_15
Download citation
DOI: https://doi.org/10.1007/978-1-4419-5714-6_15
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-5712-2
Online ISBN: 978-1-4419-5714-6
eBook Packages: MedicineMedicine (R0)