Microarray Expression References

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• Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling . Nature 403: 503-511. [doi:10.1038/35000501]

• Baldi P (2001) A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinformatics 17: 509-519. [doi:10.1093/bioinformatics/17.6.509]
• Bassett DE, Eisen MB, Boguski MS (1999) Gene expression informatics--it's all in your mine. Nature Genetics 21: 51-55. [doi:10.1038/4478]
• Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nature Genetics 21: 33-37. [doi:10.1038/4462]

• Camp RL, Chung GG, Rimm DL (2002) Automated subcellular localization and quantification of protein expression in tissue microarrays. Nature Medicine 8: 1323-1327. [doi:10.1038/nm791]
• Chen Y (1997) Ratio-based decisions and the quantitative analysis of cDNA microarray images. Journal of Biomedical Optics 2: 364-NA. [doi:10.1117/1.429838]
• Coombes KR, Wang J, Baggerly KA (2007) Microarrays: retracing steps. Nature Medicine 13: 1276-1277. [doi:10.1038/nm1107-1276b]
• Cui X, Churchill GA (2003) . Genome biology 4: 210-NA. [doi:10.1186/gb-2003-4-4-210]

• Daily JP, Scanfeld D, Pochet N, Le Roch K, Plouffe D, Kamal M, Sarr O, Mboup S, Ndir O, Wypij D, Levasseur K, Thomas E, Tamayo P, Dong C, Zhou Y, Lander ES, Ndiaye D, Wirth D, Winzeler EA, Mesirov JP, Regev A (2007) Distinct physiological states of Plasmodium falciparum in malaria-infected patients. Nature 450: 1091-1095. [doi:10.1038/nature06311]
• Das D, Pellegrini M, Gray JW (2009) A primer on regression methods for decoding cis-regulatory logic. PLoS Computational Biology 5: e1000269-NA. [doi:10.1371/journal.pcbi.1000269]
• DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, Chen Y, Su YA, Trent JM (1996) Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nature Genetics 14: 457-460. [doi:10.1038/ng1296-457]
• Dressman HK, Muramoto GG, Chao NJ, Meadows S, Marshall D, Ginsburg GS, Nevins JR, Chute JP (2007) Gene expression signatures that predict radiation exposure in mice and humans. PLoS medicine 4: e106-NA. [doi:10.1371/journal.pmed.0040106]
• Dudoit S, Fridlyand J, Speed TP (2002) Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Journal of the American Statistical Association 97: 77-87. [doi:10.1198/016214502753479248]
• Duggan DJ, Bittner M, Chen Y, Meltzer P, Trent JM (1999) Expression profiling using cDNA microarrays. Nature Genetics 21: 10-14. [doi:10.1038/4434]
• Dunning MJ, Barbosa-Morais NL, Lynch AG, Tavare S, Ritchie ME (2008) Statistical issues in the analysis of Illumina data. BMC bioinformatics 9: 85-NA. [doi:10.1186/1471-2105-9-85]

• Efron B (2007) Size, power and false discovery rates. The Annals of Statistics 35: 1351-1377. [doi:10.1214/009053606000001460]
• Efron B, Tibshirani R (2007) On testing the significance of sets of genes. Annals of Applied Statistics 1: 107-129. [doi:10.1214/07-AOAS101]
• Efron B, Tibshirani R, Storey JD, Tusher V (2001) Empirical Bayes Analysis of a Microarray Experiment. Journal of the American Statistical Association 96: 1151-1160. [doi:10.1198/016214501753382129]
• Ermolaeva O, Rastogi M, Pruitt KD, Schuler GD, Bittner ML, Chen Y, Simon R, Meltzer P, Trent JM, Boguski MS (1998) Data management and analysis for gene expression arrays. Nature Genetics 20: 19-23. [doi:10.1038/1670]

• Fang G, Kuang R, Pandey G, Steinbach M, Myers CL, Kumar V (2010) Subspace differential coexpression analysis: problem definition and a general approach. [http://psb.stanford.edu/psb-online/proceedings/psb10/fang.pdf | http://vk.cs.umn.edu/SDC]

• Golub TR (1999) Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286: 531-537. [doi:10.1126/science.286.5439.531]
• Guo L, Lobenhofer EK, Wang C, Shippy R, Harris SC, Zhang L, Mei N, Chen T, Herman D, Goodsaid FM, Hurban P, Phillips KL, Xu J, Deng X, Sun YA, Tong W, Dragan YP, Shi L (2006) Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nature Biotechnology 24: 1162-1169. [doi:10.1038/nbt1238]

• Hastie T, Tibshirani R, Botstein D, Brown P (2001) . Genome biology 2: research0003.1-NA. [doi:10.1186/gb-2001-2-1-research0003]
• Hastie T, Tibshirani R, Eisen MB, Alizadeh A, Levy R, Staudt L, Chan WC, Botstein D, Brown P (2000) . Genome biology 1: research0003.1-NA. [doi:10.1186/gb-2000-1-2-research0003]
• Hilsenbeck SG (1999) Statistical Analysis of Array Expression Data as Applied to the Problem of Tamoxifen Resistance. Journal of the National Cancer Institute 91: 453-459. [doi:10.1093/jnci/91.5.453]
• Hu J, Zou F, Wright FA (2005) Practical FDR-based sample size calculations in microarray experiments. Bioinformatics (Oxford, England) 21: 3264-3272. [doi:10.1093/bioinformatics/bti519]
• Huber W, Toedling J, Steinmetz LM (2006) Transcript mapping with high-density oligonucleotide tiling arrays. Bioinformatics (Oxford, England) 22: 1963-1970. [doi:10.1093/bioinformatics/btl289]

• Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics (Oxford, England) 4: 249-264. [doi:10.1093/biostatistics/4.2.249]

• Jansen RC (2003) Studying complex biological systems using multifactorial perturbation. Nature Reviews Genetics 4: 145-151. [doi:10.1038/nrg996]

• Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME, Oler AT, Stapleton DS, Argmann C, Schueler KL, Edwards S, Steinberg HA, Chaibub Neto E, Kleinhanz R, Turner S, Hellerstein MK, Schadt EE, Yandell BS, Kendziorski C, Attie AD (2008) A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome research 18: 706-716. [doi:10.1101/gr.074914.107 | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10785]
• Kendziorski C, Irizarry RA, Chen KS, Haag JD, Gould MN (2005) On the utility of pooling biological samples in microarray experiments. Proceedings of the National Academy of Sciences of the United States of America 102: 4252-4257. [doi:10.1073/pnas.0500607102]
• Kendziorski C, Wang P (2006) A review of statistical methods for expression quantitative trait loci mapping. Mammalian genome : official journal of the International Mammalian Genome Society 17: 509-517. [doi:10.1007/s00335-005-0189-6]
• Kendziorski CM, Newton MA, Lan H, Gould MN (2003) On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in medicine 22: 3899-3914. [doi:10.1002/sim.1548]
• Kendziorski CM, Zhang Y, Lan H, Attie AD (2003) The efficiency of pooling mRNA in microarray experiments. Biostatistics (Oxford, England) 4: 465-477. [doi:10.1093/biostatistics/4.3.465]
• Kerr K, Churchill GA (2001) Statistical design and the analysis of gene expression microarray data. Genetics Research 77: 123-128. [doi:10.1017/S0016672301005055]
• Kerr MK, Churchill GA (2001) Experimental design for gene expression microarrays. Biostatistics (Oxford, England) 2: 183-201. [doi:10.1093/biostatistics/2.2.183]
• Kerr MK, Martin M, Churchill GA (2000) Analysis of variance for gene expression microarray data. Journal of computational biology : a journal of computational molecular cell biology 7: 819-837. [doi:10.1089/10665270050514954]
• Kim PM, Tidor B (2003) Subsystem identification through dimensionality reduction of large-scale gene expression data. Genome research 13: 1706-1718. [doi:10.1101/gr.903503]
• Kim S, Dougherty ER, Bittner ML, Chen Y, Sivakumar K, Meltzer P, Trent JM (2000) General nonlinear framework for the analysis of gene interaction via multivariate expression arrays. Journal of Biomedical Optics 5: 411-NA. [doi:10.1117/1.1289142]
• Kluger Y, Basri R, Chang JT, Gerstein M (2003) Spectral biclustering of microarray data: coclustering genes and conditions. Genome research 13: 703-716. [doi:10.1101/gr.648603]

• Lander ES (1999) Array of hope. Nature Genetics 21: 3-4. [doi:10.1038/4427]
• Larkin JE, Frank BC, Gavras H, Sultana R, Quackenbush J (2005) Independence and reproducibility across microarray platforms. Nature Methods 2: 337-344. [doi:10.1038/nmeth757]
• Le CT, Pan W, Lin J (2003) A mixture model approach to detecting differentially expressed genes with microarray data. Functional & Integrative Genomics 3: 117-124. [doi:10.1007/s10142-003-0085-7]
• Lee C (1999) Gene Expression Profile of Aging and Its Retardation by Caloric Restriction. Science 285: 1390-1393. [doi:10.1126/science.285.5432.1390]
• Leek JT, Storey JD (2008) A general framework for multiple testing dependence. Proceedings of the National Academy of Sciences 105: 18718-18723. [doi:10.1073/pnas.0808709105 | http://www.pnas.org]
• Lipshutz RJ, Fodor SP, Gingeras TR, Lockhart DJ (1999) High density synthetic oligonucleotide arrays. Nature Genetics 21: 20-24. [doi:10.1038/4447]
• Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnology 14: 1675-1680. [doi:10.1038/nbt1296-1675]
• Lockhart DJ, Winzeler EA (2000) . Nature 405: 827-836. [doi:10.1038/35015701]
• Long AD (2001) Improved Statistical Inference from DNA Microarray Data Using Analysis of Variance and A Bayesian Statistical Framework. Analysis of global gene expression in Escherichia coli K12. Journal of Biological Chemistry 276: 19937-19944. [doi:10.1074/jbc.M010192200]

• Newton MA (2004) Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5: 155-176. [doi:10.1093/biostatistics/5.2.155]
• Newton MA, Kendziorski CM, Richmond CS, Blattner FR, Tsui KW (2001) On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data. Journal of Computational Biology 8: 37-52. [doi:10.1089/106652701300099074]
• Newton MA, Quintana FA, den Boon JA, Sengupta S, Ahlquist P (2007) Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis. Annals of Applied Statistics 1: 85-106. [doi:10.1214/07-AOAS104]

• Page GP, Edwards JW, Gadbury GL, Yelisetti P, Wang J, Trivedi P, Allison DB (2006) The PowerAtlas: a power and sample size atlas for microarray experimental design and research. BMC Bioinformatics 7: 84-NA. [doi:10.1186/1471-2105-7-84]
• Pan W, Lin J, Le CT (2002) How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome biology 3: research0022.1-NA. [doi:10.1186/gb-2002-3-5-research0022]
• Pollard K (2002) Statistical inference for simultaneous clustering of gene expression data. Mathematical biosciences 176: 99-121. [doi:10.1016/S0025-5564(01)00116-X]

• Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge CL, Haase J, Janes J, Huss JW, Su AI (2009) BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome biology 10: R130-NA. [doi:10.1186/gb-2009-10-11-r130 | http://biogps.gnf.org]

Last modified: Tue Mar 30 15:40:50 2010 by Brian S. Yandell