Computational methods for the identification of differential and coordinated gene expression.
Claverie, J.
Human Molecular Genetics , Volume 8 (10), 1821-1832, 1999.
Abstract & PDF

Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments.
Dudoit, S., Y.H. Yang, M.J. Callow, and T.P. Speed.
Berkeley Tech Report #578 from Aug 2000.
Terry Speed's array Group and publications at UC Berkeley

Analysis of Variance for Gene Expression Microarray Data.
Kerr, M.K., Martin, M., and Churchill, G.A.
J of Comp Biology, 7 (6), 819-837, 2000.
Abstract & PDF

Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments.
Kerr MK, Churchill GA.
Proc Natl Acad Sci U S A 2001 Jul 31;98(16):8961-5
Abstract & PDF
Gary Churchill's Group and publications at Jackson Labs

Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data.
Ideker T, Thorsson V, Siegel AF, Hood LE.
J Comput Biol 2000;7(6):805-17
Abstract & PDF

On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data.
Newton, M.N., C.M. Kendziorski, C.S. Richmond, F.R. Blattner, and K.W. Tsui.
Journal of Computational Biology, 8, 37-52, 2001.
Abstract & PDF

Experimental design, analysis of variance and slide quality assessment in gene expression arrays
Sorin Draghici, Alexander Kuklin, Bruce Hoff & Soheil Shams
Current Opinion in Drug Discovery & Development 2001 4(3):332-337
Abstract & PDF

An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles.
Thomas JG, Olson JM, Tapscott SJ, Zhao LP.
Genome Res. vo. 11(7), 1227-36, 2001.
Abstract & PDF

General nonlinear framework for the analysis of gene interaction via multivariate expression arrays.
Kim S; Dougherty ER; Bittner ML; Chen Y; Sivakumar K; Meltzer P; Trent JM.
J Biomed Opt, 2000 Oct, 5(4):411-24.
Abstract & PDF

Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations.
Lee ML; Kuo FC; Whitmore GA; Sklar J.
PNAS, 2000 Aug 29, 97(18):9834-9.
Abstract & PDF

A statistical procedure for flagging weak spots greatly improves normalization and ratio estimates in microarray experiments.
Yang MC, Ruan QG, Yang JJ, Eckenrode S, Wu S, McIndoe RA, She JX.
Physiol Genomics 2001 Aug 8
Abstract & PDF

Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects.
Tseng GC, Oh MK, Rohlin L, Liao JC, Wong WH.
Nucleic Acids Res 2001 Jun 15;29(12):2549-57
Abstract & PDF

Singular value decomposition for genome-wide expression data processing and modeling.
Alter O, Brown PO, Botstein D.
Proc Natl Acad Sci U S A 2000 Aug 29;97(18):10101-6
Abstract & PDF

Cluster analysis and display of genome-wide expression patterns.
Eisen MB; Spellman PT; Brown PO; Botstein D.
PNAS, 1998 Dec 8, 95(25):14863-8.
Abstract & PDF

Ratio-based decisions and the quantitative analysis of cDNAmicroarray images
Chen Y, Dougherty, E, Bittner, M
Journal of Biomedical Optics, (1997) 2(4):364-374
Abstract & PDF

Visualizing associations between genome sequences and gene expression data using genome-mean expression profiles.
Chiang DY, Brown PO, Eisen MB.
Bioinformatics 2001 Jun;17:S49-S55
Abstract & PDF