Microarrays: how many do you need?
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Juliane Fluck | Thomas Lengauer | Alexander Zien | Ralf Zimmer | Thomas Lengauer | A. Zien | R. Zimmer | J. Fluck
[1] Pierre R. Bushel,et al. Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models , 2001, J. Comput. Biol..
[2] 日野 寛三,et al. 対数正規分布(Lognormal Distribution)のあてはめについて , 1994 .
[3] David M. Rocke,et al. A Model for Measurement Error for Gene Expression Arrays , 2001, J. Comput. Biol..
[4] W. Pan,et al. How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach , 2002, Genome Biology.
[5] E. Winzeler,et al. Genomics, gene expression and DNA arrays , 2000, Nature.
[6] Thomas Lengauer,et al. Centralization: a new method for the normalization of gene expression data , 2001, ISMB.
[7] J. Mesirov,et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[8] Nir Friedman,et al. Tissue classification with gene expression profiles. , 2000 .
[9] Trey Ideker,et al. Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data , 2000, J. Comput. Biol..
[10] Russell D. Wolfinger,et al. The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster , 2001, Nature Genetics.
[11] Wei Pan,et al. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments , 2002, Bioinform..
[12] Bradley Efron,et al. Microarrays empirical Bayes methods, and false discovery rates , 2001 .
[13] G. Churchill,et al. Experimental design for gene expression microarrays. , 2001, Biostatistics.
[14] R Herwig,et al. Statistical evaluation of differential expression on cDNA nylon arrays with replicated experiments. , 2001, Nucleic acids research.
[15] D. Lockhart,et al. Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[16] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[17] L. Bruhn,et al. Tissue Classiication with Gene Expression Prooles , 2000 .
[18] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[19] P. Brown,et al. Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.
[20] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[21] Fred A. Wright,et al. Theoretical and experimental comparisons of gene expression indexes for oligonucleotide arrays , 2002, Bioinform..
[22] Gregory R. Grant,et al. Generation of patterns from gene expression data by assigning confidence to differentially expressed genes , 2000, Bioinform..
[23] S. Dudoit,et al. STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .
[24] Pierre Baldi,et al. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes , 2001, Bioinform..
[25] R. O. Stuart,et al. Changes in global gene expression patterns during development and maturation of the rat kidney , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[26] Eric P. Hoffman,et al. Sources of variability and effect of experimental approach on expression profiling data interpretation , 2002, BMC Bioinformatics.
[27] Gary A. Churchill,et al. Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..
[28] G. A. Whitmore,et al. Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[29] R. Tibshirani,et al. Empirical bayes methods and false discovery rates for microarrays , 2002, Genetic epidemiology.
[30] Michael L. Bittner,et al. Assessing the significance of consistently mis-regulated genes in cancer associated gene expression matrices , 2002, Bioinform..
[31] Douglas M. Hawkins,et al. A variance-stabilizing transformation for gene-expression microarray data , 2002, ISMB.
[32] William Stafford Noble,et al. Analysis of strain and regional variation in gene expression in mouse brain , 2001, Genome Biology.
[33] A. Zien,et al. Correlated stage‐ and subfield‐associated hippocampal gene expression patterns in experimental and human temporal lobe epilepsy , 2003, The European journal of neuroscience.
[34] W. Stahel,et al. Log-normal Distributions across the Sciences: Keys and Clues , 2001 .
[35] H. B. Mann,et al. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .
[36] Martin Vingron,et al. Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.
[37] J. Aitchison,et al. The Lognormal Distribution. , 1958 .
[38] N. Friedman,et al. Tissue Classi cation with Gene Expression Pro les , 2004 .
[39] R. Tibshirani,et al. Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[40] Student,et al. THE PROBABLE ERROR OF A MEAN , 1908 .