MeV: MultiExperiment Viewer
Normalized and filtered expression files can be analyzed using TIGR Multiexperiment Viewer (MeV). MeV is a versatile microarray data analysis tool, incorporating sophisticated algorithms for clustering, visualization, classification, statistical analysis and biological theme discovery. MeV can handle several input file formats. These include the “.mev” and “.tav” files generated by TIGR Spotfinder and TIGR MIDAS, and also Affymetrix® (“.txt”) and Genepix® (“.gpr”) files. MeV generates informative and interrelated displays of expression and annotation data from single or multiple experiments. At this final stage of the TM4 pipeline, flexibility and the variety of analysis techniques are critical, as every algorithm has strengths that can be exploited when used on certain datasets and experimental designs. The concept of modularization lends itself particularly well to this system, as novel algorithms and existing codebases from the microarray community can be integrated with the Java based MeV using a well-defined module API.
Many of the algorithms currently implemented are listed below. Bootstrapping, jackknifing and k-means support resample the dataset to generate consensus clusters. Figure of merit graphs (FOM; (Yeung et al., 2001) suggest appropriate input parameters for algorithms such as k-means. Modules to allow metabolic pathways and genomic/chromosomal maps to be viewed with expression data overlaid are in development and testing. A wizard to handle links to public database websites is also being developed. Clusters identified through any analysis method can be labeled and tracked through other analyses, providing the user with the ability to compare the results of several clustering algorithms to determine consensus and focus on genes with specified expression patterns and biological profiles.
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HCL - Hierarchical clustering
Eisen, M.B., P.T. Spellman, P.O. Brown, and D. Botstein. 1998. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95:14863-14868.
ST - Support trees (Bootstrapping)
Graur, D., and W.-H. Li. 2000. Fundamentals of Molecular Evolution. Second Edition.Sinauer Associates, Sunderland, MA. pp 209-210.
SOTA - Self-organizing trees
Herrero J., A. Valencia, J. Dopazo (2001). A hierarchicalunsupervised growing neural network for clustering geneexpression patterns. Bioinformatics 17(2):126-136.
Dopazo, J., and J. M. Carazo (1997). Phylogenetic reconstruction using an unsupervised growing neural network that adopts the topology of a phylogenetic tree. Journal of Molecular Evolution 44:226-233.
KMC - K-Means Clustering
Soukas, A., P. Cohen, N.D. Socci, and J.M. Friedman. 2000. Leptin-specific patterns of gene expression in white adipose tissue. Genes Dev. 14:963-980.
SOM - Self-organizing maps
Kohonen, T. 1992. Self-organized formation of topologically correct feature maps. Biol. Cybernetics 43:59-69.
Tamayo, P., D. Slonim, J. Masirov, Q. Zhu, S. Kitareewan, E. Dmitrovsky, E.S. Lander, and T.R. Golub 1999. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences USA 96:2907-2912.
CAST - Clustering Affinity Search Technique
Ben-Dor, A., R. Shamir, and Z. Yakhini (1999) Clustering gene expression patterns. Journal of Computational Biology 6:281-297.
QTC - QT_Clust
Heyer, L.J., S. Kruglyak, and S. Yooseph. 1999. Exploring expression data: identification and analysis of coexpressed genes. Genome Res. 9:1106-1115.
GSH - Gene shaving
Hastie, T. et al. (2000). 'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biology 1(2):research0003.1-0003.21.
FOM - Figures of Merit
Yeung, K.Y., D.R. Haynor, and W.L. Ruzzo (2001) Validating clustering for gene expression data. Bioinformatics 17:309-318.
PCAE and PCAG - Principal components analysis
Raychaudhuri, S., J.M. Stuart, and R.B. Altman. 2000. Principal components analysis to summarize microarray experiments: application to sporulation time series. Pacific Symposium on Biocomputing 2000, Honolulu, Hawaii, 452-463. Available here
RN - Relevance networks
Butte, A.J., P. Tamayo, D. Slonim, T.R. Golub, and I.S. Kohane. 2000. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc. Natl. Acad. Sci. USA 97:12182-12186.
PTM - Template matching
Pavlidis, P., and W.S. Noble 2001. Analysis of strain and regional variation in gene expression in mouse brain. Genome Biology 2:research0042.1-0042.15.
SAM - Significance Analysis of Microarrays
Tusher, V.G., R. Tibshirani and G. Chu. 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences USA 98: 5116-5121.
Chu, G., B. Narasimhan, R. Tibshirani and V. Tusher (2002). SAM "Significance Analysis of Microarrays" Users Guide and Technical Document. http://www-stat.stanford.edu/~tibs/SAM/
ANOVA - One-way Analysis of Variance
Zar, J.H. 1999. Biostatistical Analysis. 4th ed. Prentice Hall, NJ.
TTEST - T-Tests
Pan, W. (2002). A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18: 546-554.
Dudoit, S., Y.H. Yang, M.J. Callow, and T. Speed (2000).Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Technical report 2000 Statistics Department, University of California, Berkeley.
Welch B.L. (1947).The generalization of ‘students’ problem when several different population variances are involved. Biometrika 34: 28-35.
SVM - Support Vector machines
Brown, M.P., W.N. Grundy, D. Lin, N. Cristianini, C.W. Sugnet, T.S. Furey, M. Ares, Jr., and D. Haussler. 2000. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. USA 97:262-267.
TRN - Expression Terrain Maps
Kim, S.K., J. Lund, M. Kiraly, K. Duke, M. Jiang, J.M. Stuart, A. Eizinger,B.N. Wylie, and G.S. Davidson (2001) A Gene Expression Map for Caenorhabditis elegans. Science 293: 2087-2092.
Copyright © 2005, Dana-Farber Cancer Institute, 44 Binney St, Boston, MA, USA.
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Last modified: 12/06/2005
Latest Version
MeV v4.1.01 requires Java v1.5
Windows
Linux
Mac OSX
v4.1.01 source
The v4.1 release notes describe changes made since version 4.0.
MeV Manual
The Manual is a detailed description of the workings of MeV.
System Requirements
MeV v4.1 runs on the Windows, Mac OSX or Linux operating systems and requires Java v1.6.0 or higher.
Java 3D is required for some MeV modules, like PCA.
Download Java v1.6
Download Java 3D v1.3.1
Training Documents
The powerpoint slides here contain the slides our group uses to teach courses on the program.
FAQ
The frequently asked questions page is very helpful in troubleshooting problems in installation and usage of MeV.
Support
If the above resources don't answer your questions, please contact mev@tigr.org for more help.