Overview of Methods

The following is a list of methods, for in-depth information please refer to the MeV manual and the original papers cited below.

Clustering

• Clustering can be applied to samples and / or expression profiles
• Hierarchical clustering and k-Means / k-Medians clustering
• Supports average linkage, complete linkage, and single linkage for determining cluster-to-cluster distances
• Supported distance metrics: Euclidean distance, Manhattan distance, Pearson correlation, Pearson uncentered correlation, Pearson squared correlation, Average dot product, Cosine correlation, Covariance, Spearman’s rank correlation, Kendall's tau.
• Construction of support trees by resampling methods: bootstrapping (resampling with replacement), and jackknifing (resampling by leaving out one observation).

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.

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.

Template Matching

• Templates can be defined for expression profiles and samples.
• Templates can be defined interactively, from a given expression profile, or from a cluster.

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.

Principal Component Analysis

• Principal component analysis is available for both samples and expression profiles.
• Three-dimensional and two-dimensional displays are available
• New clusters can be defined by dragging in a two-dimensional display.

Raychaudhuri, S., J. M. Stuart, & 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 at http://psb.stanford.edu/psb-online/proceedings/psb00/raychaudhuri.pdf

Statistical Hypothesis Testing

TTEST - T-Tests

• T-tests: one-sample, between samples, paired t-test
• Assuming equal or different group variances
• P-values can be computed based on normal distribution or using randomization.
• Corrections for multiple testing: Bonferroni, adjusted Bonferroni, Westfall-Young
• Control of false discovery rate
• Volcano Plot

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.

ANOVA - One-way Analysis of Variance

• P-values can be computed based on F-distribution or using randomization.
• Corrections for multiple testing: Bonferroni, adjusted Bonferroni, Westfall-Young
• Control of false discovery rate

Zar, J.H. 1999. Biostatistical Analysis. 4th ed. Prentice Hall, NJ.

TFA - Two-factor Analysis of Variance

Keppel, G., and S. Zedeck.1989. Data Analysis for Research Designs. W. H. Freeman and Co., NY.

Manly, B.F.J. 1997. Randomization, Bootstrap and Monte Carlo Methods in Biology. 2nd ed. Chapman and Hall / CRC , FL.

Zar, J.H. 1999. Biostatistical Analysis. 4th ed. Prentice Hall, NJ.

References

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. TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003 Feb;34(2):374-8.

Alter O, Brown PO, Botstein D (2000) Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A 97:10101–10106

Holter NS, Mitra M, Maritan A, Cieplak M, Banavar JR, Fedoroff NV (2000) Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc Natl Acad Sci U S A 97:8409-8414

TIGR Multiple Experiment Viewer (MeV):
http://www.tm4.org/mev.html

TIGR MeV manual:
www.decodon.com/Support/Documentation/MeV/MeV_Manual_4_0.pdf