A Survey and Comparative Study of Statistical Tests for Identifying Differential Expression from Microarray Data
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Anirban Mukhopadhyay | Sanghamitra Bandyopadhyay | Saurav Mallik | Saurav Mallik | S. Bandyopadhyay | A. Mukhopadhyay
[1] S. P. Fodor,et al. Determination of ancestral alleles for human single-nucleotide polymorphisms using high-density oligonucleotide arrays , 1999, Nature Genetics.
[2] S. Mohamed,et al. Statistical Normalization and Back Propagation for Classification , 2022 .
[3] David J. Spiegelhalter,et al. Microarrays, Empirical Bayes and the Two-Groups Model. Comment. , 2008 .
[4] W. Kruskal,et al. Use of Ranks in One-Criterion Variance Analysis , 1952 .
[5] E. Wit. Design and Analysis of DNA Microarray Investigations , 2004, Human Genomics.
[6] Ujjwal Maulik,et al. Integrated Statistical and Rule-Mining Techniques for Dna Methylation and Gene Expression Data Analysis , 2013, J. Artif. Intell. Soft Comput. Res..
[7] Ujjwal Maulik,et al. Integrated analysis of gene expression and genome-wide DNA methylation for tumor prediction: An association rule mining-based approach , 2013, 2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).
[8] H. Büning,et al. Jarque–Bera Test and its Competitors for Testing Normality – A Power Comparison , 2007 .
[9] John D. Storey,et al. Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.
[10] Martin T. Wells,et al. Laplace Approximated EM Microarray Analysis: An Empirical Bayes Approach for Comparative Microarray Experiments , 2010, 1101.0905.
[11] Erik Kristiansson,et al. BMC Bioinformatics BioMed Central Methodology article Weighted analysis of general microarray experiments , 2007 .
[12] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[13] Nancy R. Zhang,et al. Multiple hypothesis testing adjusted for latent variables, with an application to the AGEMAP gene expression data , 2013, 1301.2420.
[14] Johann A. Gagnon-Bartsch,et al. Using control genes to correct for unwanted variation in microarray data. , 2012, Biostatistics.
[15] Yen-Liang Chen,et al. An overlapping cluster algorithm to provide non-exhaustive clustering , 2006, Eur. J. Oper. Res..
[16] Jeffrey T Leek,et al. A general framework for multiple testing dependence , 2008, Proceedings of the National Academy of Sciences.
[17] S. Bandyopadhyay,et al. Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes , 2009, BMC Bioinformatics.
[18] Ujjwal Maulik,et al. Towards improving fuzzy clustering using support vector machine: Application to gene expression data , 2009, Pattern Recognit..
[19] J. Aldrich. Correlations Genuine and Spurious in Pearson and Yule , 1995 .
[20] Marti J. Anderson,et al. Permutation tests for univariate or multivariate analysis of variance and regression , 2001 .
[21] Mickael Guedj,et al. Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies , 2010, PloS one.
[22] AN Kolmogorov-Smirnov,et al. Sulla determinazione empírica di uma legge di distribuzione , 1933 .
[23] H. Horvitz,et al. MicroRNA expression profiles classify human cancers , 2005, Nature.
[24] Jean-Jacques Daudin,et al. VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data , 2005, Bioinform..
[25] I. Simon,et al. Studying and modelling dynamic biological processes using time-series gene expression data , 2012, Nature Reviews Genetics.
[26] Ingrid Lönnstedt. Replicated microarray data , 2001 .
[27] H. Büning,et al. Jarque-Bera test and its competitors for testing normality , 2004 .
[28] John D. Storey,et al. False Discovery Rate , 2020, International Encyclopedia of Statistical Science.
[29] Jennifer L. O'Day. Statistical Significance for Genome Wide Studies Under Unequal Variance , 2015 .
[30] Isaac Dialsingh,et al. Multiple Hypothesis Testing : A Review , 2014 .
[31] Y. Benjamini,et al. On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics , 2000 .
[32] John D. Storey,et al. Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[33] John D. Storey,et al. Empirical Bayes Analysis of a Microarray Experiment , 2001 .
[34] John D. Storey. A direct approach to false discovery rates , 2002 .
[35] Massimiliano Pontil,et al. Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods , 2009, BMC Bioinformatics.
[36] Wei Pan,et al. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments , 2002, Bioinform..
[37] J. Booth,et al. Resampling-Based Multiple Testing. , 1994 .
[38] Ujjwal Maulik,et al. Simulated annealing based automatic fuzzy clustering combined with ANN classification for analyzing microarray data , 2010, Comput. Oper. Res..
[39] Jae Won Lee,et al. Comparison of various statistical methods for identifying differential gene expression in replicated microarray data , 2006, Statistical methods in medical research.
[40] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[41] A. Scherer. Batch Effects and Noise in Microarray Experiments , 2009 .
[42] 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.
[43] V. Corces,et al. CTCF: Master Weaver of the Genome , 2009, Cell.
[44] Robert Nadon,et al. Comparison of small n statistical tests of differential expression applied to microarrays , 2009, BMC Bioinformatics.
[45] S. Dudoit,et al. STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .
[46] Andrew J Vickers,et al. Parametric versus non-parametric statistics in the analysis of randomized trials with non-normally distributed data , 2005, BMC medical research methodology.
[47] Mihai Aldea,et al. Expression profiling soybean response to Pseudomonas syringae reveals new defense-related genes and rapid HR-specific downregulation of photosynthesis. , 2005, Molecular plant-microbe interactions : MPMI.
[48] Gordon K Smyth,et al. Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .
[49] Baolin Wu,et al. Differential gene expression detection and sample classification using penalized linear regression models , 2006, Bioinform..
[50] Jean-Louis Foulley,et al. A structural mixed model for variances in differential gene expression studies. , 2007, Genetical research.
[51] X. Cui,et al. Improved statistical tests for differential gene expression by shrinking variance components estimates. , 2005, Biostatistics.
[52] Korbinian Strimmer,et al. Statistical Applications in Genetics and Molecular Biology , 2005 .
[53] Mahmoud Mounir,et al. On biclustering of gene expression data , 2015, 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS).
[54] M. Omair Ahmad,et al. Identification of Differentially Expressed Genes for Time-Course Microarray Data Based on Modified RM ANOVA , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[55] Sanghamitra Bandyopadhyay,et al. A Biologically Inspired Measure for Coexpression Analysis , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[56] David M. Simcha,et al. Tackling the widespread and critical impact of batch effects in high-throughput data , 2010, Nature Reviews Genetics.
[57] Wynne W. Chin,et al. Generating Non-normal Data for Simulation of Structural Equation Models Using Mattson's Method , 2002 .
[58] Ujjwal Maulik,et al. Multi-Class Clustering of Cancer Subtypes through SVM Based Ensemble of Pareto-Optimal Solutions for Gene Marker Identification , 2010, PloS one.
[59] Richard Simon,et al. A random variance model for detection of differential gene expression in small microarray experiments , 2003, Bioinform..
[60] Russ B. Altman,et al. Nonparametric methods for identifying differentially expressed genes in microarray data , 2002, Bioinform..
[61] R. Gentleman,et al. Independent filtering increases detection power for high-throughput experiments , 2010, Proceedings of the National Academy of Sciences.
[62] Yudi Pawitan,et al. False discovery rate, sensitivity and sample size for microarray studies , 2005, Bioinform..
[63] G. Church,et al. Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset , 2005, Genome Biology.
[64] Martin Vingron,et al. Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.
[65] B. L. Welch. THE SIGNIFICANCE OF THE DIFFERENCE BETWEEN TWO MEANS WHEN THE POPULATION VARIANCES ARE UNEQUAL , 1938 .
[66] B. Efron. Robbins, Empirical Bayes, And Microarrays , 2001 .
[67] K. Rothman. Curbing type I and type II errors , 2010, European Journal of Epidemiology.
[68] B. Efron. Size, power and false discovery rates , 2007, 0710.2245.
[69] S. Shapiro,et al. An Analysis of Variance Test for Normality (Complete Samples) , 1965 .
[70] Raymond H. Myers,et al. Probability and Statistics for Engineers and Scientists. , 1973 .
[71] K. K. Jose,et al. Statistical tests for identification of differentially expressed genes in cDNA microarray experiments , 2008 .
[72] Sudhir Varma,et al. Microarray-Based Analysis of Differential Gene Expression between Infective and Noninfective Larvae of Strongyloides stercoralis , 2011, PLoS Neglected Tropical Diseases.
[73] Y. Benjamini,et al. THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .
[74] J. Shaffer. Multiple Hypothesis Testing , 1995 .
[75] S. Holm. A Simple Sequentially Rejective Multiple Test Procedure , 1979 .
[76] S. Dudoit,et al. Multiple Hypothesis Testing in Microarray Experiments , 2003 .
[77] Y. B. Wah,et al. Power comparisons of Shapiro-Wilk , Kolmogorov-Smirnov , Lilliefors and Anderson-Darling tests , 2011 .
[78] Terence P. Speed,et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..
[79] Andreas Scherer,et al. Batch Effects and Noise in Microarray Experiments: Sources and Solutions , 2009 .
[80] Eric Depiereux,et al. A benchmark for statistical microarray data analysis that preserves actual biological and technical variance , 2010, BMC Bioinformatics.
[81] W. Gregory Alvord,et al. A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R , 2007, Briefings Bioinform..
[82] P. Müller,et al. Bayesian inference for gene expression and proteomics , 2006 .
[83] A. McCluskey,et al. Statistics IV: Interpreting the results of statistical tests , 2007 .