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. Proceedings of the National Academy of Sciences USA 95:14863-14868.

Bar-Joseph, Z., D.K. Gifford, T.S. Jaakkola (2001)
Fast optimal leaf ordering for hierarchical clustering. Bioinformatics Vol. 17 no. 90001 2001:S22-S29

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 hierarchical unsupervised growing neural network for clustering gene expression 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
Soukas, A., P. Cohen, N.D. Socci, and J.M. Friedman (2000)
Leptin-specific patterns of gene expression in white adipose tissue. Genes and Development 14:963-980.

SOM - Self-Organizing Maps
Kohonen, T. (1982) Self-organized formation of topologically correct feature maps. Biological 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 - Jackknife Clustering
Heyer, L.J., S. Kruglyak, and S. Yooseph (1999)
Exploring expression data: identification and analysis of coexpressed genes. Genome Research 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.

PCA - Principal Components Analysis
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://smi-web.stanford.edu/pubs/SMI_Abstracts/SMI-1999-0804.html

COA - Correspondence Analysis
Fellenberg K. et al. (2001)
Correspondence analysis applied to microarray data. Proceedings of the National Academy of Sciences USA 98: 10781-10786

Culhane A.C. et al. (2002) Between-group analysis of microarray data. Bioinformatics 18:1600-1608.

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 chemotherapautic susceptibility using relevance networks. Proceedings of the National Academy of Sciences 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.

Korn, E.L., J.F. Troendle, L.M. McShane, R. Simon (2001). Controlling the number of false discoveries: application to high-dimensional genomic data Technical report 003, Biometric Research Branch, National Cancer Institute. http://linus.nci.nih.gov/~brb/TechReport.htm

Korn, E.L., J.F. Troendle, L.M. McShane, R. Simon (2004). Controlling the number of false discoveries: application to high-dimensional genomic data Journal of Statistical Planning and Inference 124: 379-398.

TFA - Two Factor ANOVA
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.

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. Proceedings of the National Academy of Sciences USA 97: 262-267.

KNNC - K-Nearest Neighbor Classification
Theilhaber, J., Connolly, S. Roman-Roman, S. Bushnell, A. Jackson, K. Call, T. Garcia, R. Baron (2002)
Finding Genes in the C2Cl2 Osteogenic Pathway by K-Nearest-Neighbor Classification of Expression Data. Genome Research 12:165-176

DAM - Discriminant Analysis Module
Nguyen, D.V., D.M. Rocke.
Multi-class Cancer Classification via Partial Least Squares with Gene Expression Profiles. Bioinformatics 18(9):1216-1226, 2002

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.

EASE - Expression Analysis Systematic Explorer
Hosack, D.A., G. Dennis Jr., B.T. Sherman, H.C. Lane, R.A. Lempicki. (2001)
Identifying biological themes within lists of genes with EASE. Genome Biol. 4:R70-R70.8, 2003.

nEASE - An addition to the EASE module
Chittenden, T.W., E.A. Howe, J. Taylor, J. Mar, M. Aryee, J. Braisted, S. Nair, J. Quackenbush, C. Holmes. (2009)
Molecular Subtype-specific Gene Ontology Classification in Human Breast Cancer Submitted.

TEASE - Tree EASE

Gottardo R, Raftery A.E, Yeung K.Y, and R.E. Bumgarner, Quality Control and Robust Estimation for cDNA Microarrays with Replicates, accepted for publication in The Journal of the American Statistical Association

Gottardo R, Raftery AE, Yee Yeung K, Bumgarner RE. 2006. Bayesian robust inference for differential gene expression in microarrays with multiple samples. Biometrics. 62(1):10-8.

USC - Uncorrelated Shrunken Centroid

Yeung KY, Bumgarner RE. Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biol. 2003;4(12):R83. Epub 2003 Nov 24.

Yeung KY, Bumgarner RE. Correction: Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biol. 2005;6(13):405. Epub 2006 Jan 3.

CGH - Comparitive Genomic Hybridization

Margolin AA, Greshock J, Naylor TL, Mosse Y, Maris JM, Bignell G, Saeed AI, Quackenbush J, Weber BL. CGHAnalyzer: a stand-alone software package for cancer genome analysis using array-based DNA copy number data. Bioinformatics. 2005 Aug 1;21(15):3308-11.

NonpaR

Hollander M, Wolfe D. 1999 Nonparametric Statistical Methods. 1999.

BN/LM - Bayes Networks / Literature Mining

Djebbari, A., Quackenbush, J. In Review. Seeded Bayesian Networks: constructing genetic networks from microarray data.

BETR - Bayesian Estimation of Temporal Regulation

Aryee, M., J. Gutierrez-Pabello, I. Kramnik, T., Maiti, J. Quackenbush 2008. An Improved Empirical Bayes Approach to Estimating Differential Expression in Microarray Time-Course Data: BETR (Bayesian Estimation of Temporal Regulation). BMC Bioinformatics, accepted Oct. 2009.

RP - Rank Products

Breitling, R., P. Armengaud, A. Amtmann, P. Herzyk 2004. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. Federation of European Biochemical Societies. 83-92.

GSEA - Gene Set Enrichment Analysis

Jiang, Z. and Gentleman, R. (2007). Extensions to gene set enrichment analysis. Bioinformatics, 23, 306–313.

R-CRAN - R and BioConductor

R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

LIMMA - LIMMA algorithm

Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, No. 1, Article 3.

LIMMA - LIMMA R software

Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397-420.

NMF - Non-negative Matrix Factorization

Brunet, J-P., Tamayo, P., Golub, T.R., and Mesirov, J.P. 2004. Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. USA 101(12):4164–4169.

Devarajan K, 2008 Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology. PLoS Comput Biol 4(7): e1000029. doi:10.1371/journal.pcbi.1000029

Lee DD, Seung SH (2001) Algorithms for nonnegative matrix factorization. Adv Neural Inform Process Syst 13: 556–562.