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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 tudents?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.
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 tudents?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.
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