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  • Search: subject:"Microarray analysis"
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Year of publication
Subject
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gene expression 4 microarray analysis 4 Microarray analysis 3 Clustering 2 MCMC computation 2 Mixture distributions 2 Multiple hypothesis testing 2 Non-central t-distribution 2 stochastic dependence 2 Empirical Bayes 1 False discovery rate 1 Multiple decision function 1 Multiple decision process 1 Multiple testing 1 Sample splitting 1 Test data 1 Training data 1 correlated data 1 ecological genomics 1 empirical Bayes method 1 experimental design 1 pooled data 1 resampling techniques 1 split-plot design 1 two-sample tests 1
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Online availability
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Undetermined 6 Free 1
Type of publication
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Article 6 Book / Working Paper 1
Language
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Undetermined 6 English 1
Author
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Klebanov, Lev 3 Yakovlev, Andrei 3 Garrett, K. 1 Habiger, Joshua D. 1 Jordan, Craig 1 Marín, J.M. 1 Marín, Miguel J. 1 Milliken, G. 1 Peña, Edsel A. 1 Qiu, Xing 1 Rodríguez-Bernal, M. Teresa 1 Rodríguez-Bernal, M.T. 1 Travers, S. 1
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Institution
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Departamento de Estadistica, Universidad Carlos III de Madrid 1
Published in...
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Statistical Applications in Genetics and Molecular Biology 4 Computational Statistics & Data Analysis 1 Journal of Multivariate Analysis 1 Statistics and Econometrics Working Papers 1
Source
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RePEc 7
Showing 1 - 7 of 7
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Multiple hypothesis testing and clustering with mixtures of non-central t-distributions applied in microarray data analysis
Marín, Miguel J.; Rodríguez-Bernal, M. Teresa - Departamento de Estadistica, Universidad Carlos III de … - 2010
Multiple testing analysis, based on clustering methodologies, is usually applied in Microarray Data Analysis for comparisons between pair of groups. In this paper, we generalize this methodology to deal with multiple comparisons among more than two groups obtained from microarray expressions of...
Persistent link: https://www.econbiz.de/10008692050
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Compound p-value statistics for multiple testing procedures
Habiger, Joshua D.; Peña, Edsel A. - In: Journal of Multivariate Analysis 126 (2014) C, pp. 153-166
Many multiple testing procedures make use of the p-values from the individual pairs of hypothesis tests, and are valid if the p-value statistics are independent and uniformly distributed under the null hypotheses. However, it has recently been shown that these types of multiple testing...
Persistent link: https://www.econbiz.de/10011041965
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Multiple hypothesis testing and clustering with mixtures of non-central t-distributions applied in microarray data analysis
Marín, J.M.; Rodríguez-Bernal, M.T. - In: Computational Statistics & Data Analysis 56 (2012) 6, pp. 1898-1907
Multiple testing analysis and clustering methodologies are usually applied in microarray data analysis. A combination of both methods to deal with multiple comparisons among groups obtained from microarray expressions of genes is proposed. Assuming normal data, a statistic which depends on...
Persistent link: https://www.econbiz.de/10011056509
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Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?
Klebanov, Lev; Yakovlev, Andrei - In: Statistical Applications in Genetics and Molecular Biology 5 (2007) 1, pp. 9-9
One of the prevailing ideas in the literature on microarray data analysis is to pool the expression measures across genes and treat them as a sample drawn from some distribution. Several universal laws were proposed to analytically describe this distribution. This idea raises a number of...
Persistent link: https://www.econbiz.de/10005585102
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Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes
Qiu, Xing; Klebanov, Lev; Yakovlev, Andrei - In: Statistical Applications in Genetics and Molecular Biology 4 (2007) 1, pp. 34-34
Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test statistics across genes. The empirical Bayes methodology in the nonparametric and parametric formulations, as well as closely...
Persistent link: https://www.econbiz.de/10005752557
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A New Type of Stochastic Dependence Revealed in Gene Expression Data
Klebanov, Lev; Jordan, Craig; Yakovlev, Andrei - In: Statistical Applications in Genetics and Molecular Biology 5 (2007) 1, pp. 7-7
Modern methods of microarray data analysis are biased towards selecting those genes that display the most pronounced differential expression. The magnitude of differential expression does not necessarily indicate biological significance and other criteria are needed to supplement the information...
Persistent link: https://www.econbiz.de/10005752564
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Experimental Design for Two-Color Microarrays Applied in a Pre-Existing Split-Plot Experiment
Milliken, G.; Garrett, K.; Travers, S. - In: Statistical Applications in Genetics and Molecular Biology 6 (2007) 1, pp. 20-20
Microarray applications for the study of gene expression are becoming accessible for researchers in more and more systems. Applications from field or laboratory experiments are often complicated by the need to superimpose sample pairing for two-color arrays on experimental designs that may...
Persistent link: https://www.econbiz.de/10005246492
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