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A mixture model approach for the analysis of microarray gene expression data

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  1. Makariou, Despoina & Barrieu, Pauline & Tzougas, George, 2021. "A finite mixture modelling perspective for combining experts’ opinions with an application to quantile-based risk measures," LSE Research Online Documents on Economics 110763, London School of Economics and Political Science, LSE Library.
  2. Lin Zhang & Inyoung Kim, 2021. "Finite mixtures of semiparametric Bayesian survival kernel machine regressions: Application to breast cancer gene pathway subgroup analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 251-269, March.
  3. Muir, W.M. & Rosa, G.J.M. & Pittendrigh, B.R. & Xu, Z. & Rider, S.D. & Fountain, M. & Ogas, J., 2009. "A mixture model approach for the analysis of small exploratory microarray experiments," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1566-1576, March.
  4. Andrew Y. Chen, 2022. "Do t-Statistic Hurdles Need to be Raised?," Papers 2204.10275, arXiv.org, revised Apr 2024.
  5. Cheng, Cheng, 2009. "Internal validation inferences of significant genomic features in genome-wide screening," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 788-800, January.
  6. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
  7. Hui-Min Wang & Ching-Lin Hsiao & Ai-Ru Hsieh & Ying-Chao Lin & Cathy S J Fann, 2012. "Constructing Endophenotypes of Complex Diseases Using Non-Negative Matrix Factorization and Adjusted Rand Index," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-12, July.
  8. Miecznikowski, Jeffrey C. & Gold, David & Shepherd, Lori & Liu, Song, 2011. "Deriving and comparing the distribution for the number of false positives in single step methods to control k-FWER," Statistics & Probability Letters, Elsevier, vol. 81(11), pages 1695-1705, November.
  9. Hunt, Daniel L. & Cheng, Cheng & Pounds, Stanley, 2009. "The beta-binomial distribution for estimating the number of false rejections in microarray gene expression studies," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1688-1700, March.
  10. Robert R. Delongchamp & John F. Bowyer & James J. Chen & Ralph L. Kodell, 2004. "Multiple-Testing Strategy for Analyzing cDNA Array Data on Gene Expression," Biometrics, The International Biometric Society, vol. 60(3), pages 774-782, September.
  11. Ghosh Debashis, 2012. "Incorporating the Empirical Null Hypothesis into the Benjamini-Hochberg Procedure," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-21, July.
  12. Mathias Drton & Martyn Plummer, 2017. "A Bayesian information criterion for singular models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 323-380, March.
  13. Marot Guillemette & Mayer Claus-Dieter, 2009. "Sequential Analysis for Microarray Data Based on Sensitivity and Meta-Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-35, January.
  14. Parrish, Rudolph S. & Spencer III, Horace J. & Xu, Ping, 2009. "Distribution modeling and simulation of gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1650-1660, March.
  15. Yongqiang Tang & Subhashis Ghosal & Anindya Roy, 2007. "Nonparametric Bayesian Estimation of Positive False Discovery Rates," Biometrics, The International Biometric Society, vol. 63(4), pages 1126-1134, December.
  16. Ferreira, J.A. & Nyangoma, S.O., 2008. "A multivariate version of the Benjamini-Hochberg method," Journal of Multivariate Analysis, Elsevier, vol. 99(9), pages 2108-2124, October.
  17. Nguyen, Danh V., 2004. "On estimating the proportion of true null hypotheses for false discovery rate controlling procedures in exploratory DNA microarray studies," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 611-637, October.
  18. Gary L Gadbury & David B Allison, 2012. "Inappropriate Fiddling with Statistical Analyses to Obtain a Desirable P-value: Tests to Detect its Presence in Published Literature," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-9, October.
  19. Xiang, Qinfang & Edwards, Jode & Gadbury, Gary L., 2006. "Interval estimation in a finite mixture model: Modeling P-values in multiple testing applications," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 570-586, November.
  20. Robin, Stephane & Bar-Hen, Avner & Daudin, Jean-Jacques & Pierre, Laurent, 2007. "A semi-parametric approach for mixture models: Application to local false discovery rate estimation," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5483-5493, August.
  21. Shigeyuki Matsui & Shu Zeng & Takeharu Yamanaka & John Shaughnessy, 2008. "Sample Size Calculations Based on Ranking and Selection in Microarray Experiments," Biometrics, The International Biometric Society, vol. 64(1), pages 217-226, March.
  22. Bickel David R., 2008. "Correcting the Estimated Level of Differential Expression for Gene Selection Bias: Application to a Microarray Study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-27, March.
  23. Pounds, Stan & Rai, Shesh N., 2009. "Assumption adequacy averaging as a concept for developing more robust methods for differential gene expression analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1604-1612, March.
  24. David R. Bickel, 2013. "Minimax-Optimal Strength of Statistical Evidence for a Composite Alternative Hypothesis," International Statistical Review, International Statistical Institute, vol. 81(2), pages 188-206, August.
  25. Long Qu & Tobias Guennel & Scott L. Marshall, 2013. "Linear Score Tests for Variance Components in Linear Mixed Models and Applications to Genetic Association Studies," Biometrics, The International Biometric Society, vol. 69(4), pages 883-892, December.
  26. McLachlan, G. J. & Khan, N., 2004. "On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 90-105, July.
  27. Yu, Chang & Zelterman, Daniel, 2017. "A parametric model to estimate the proportion from true null using a distribution for p-values," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 105-118.
  28. Bradley Efron, 2007. "Doing thousands of hypothesis tests at the same time," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 3-21.
  29. Park, DoHwan & Park, Junyong & Zhong, Xiaosong & Sadelain, Michel, 2011. "Estimation of empirical null using a mixture of normals and its use in local false discovery rate," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2421-2432, July.
  30. Charnigo, Richard & Fan, Qian & Bittel, Douglas & Dai, Hongying, 2013. "Testing unilateral versus bilateral normal contamination," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 163-167.
  31. Despoina Makariou & Pauline Barrieu & George Tzougas, 2021. "A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures," Risks, MDPI, vol. 9(6), pages 1-25, June.
  32. He, Yi & Pan, Wei & Lin, Jizhen, 2006. "Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 641-658, November.
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