Kerr, M. K. Design considerations for efficient and effective microarray studies. Biometrics 59, 822–828 (2003).
Page, G. P., Edwards, J. W., Barnes, S., Weindruch, R. & Allison, D. B. A design and statistical perspective on microarray gene expression studies in nutrition: the need for playful creativity and scientific hard-mindedness. Nutrition 19, 997–1000 (2003).
Yang, M. C., Yang, J. J., McIndoe, R. A. & She, J. X. Microarray experimental design: power and sample size considerations. Physiol. Genomics 16, 24–28 (2003).
Kerr, M. K. & Churchill, G. A. Experimental design for gene expression microarrays. Biostatistics 2, 183–201 (2001).
Dobbin, K., Shih, J. H. & Simon, R. Statistical design of reverse dye microarrays. Bioinformatics 19, 803–810 (2003).
Churchill, G. A. Fundamentals of experimental design for cDNA microarrays. Nature Genet. 32, S490–S495 (2002).
Yang, Y. H. & Speed, T. Design issues for cDNA microarray experiments. Nature Rev. Genet. 3, 579–588 (2002).
Allison, D. B., Allison, R. L., Faith, M. S., Paultre, F. & Pi-Sunyer, F. X. Power and money: designing statistically powerful studies while minimizing financial costs. Psychol. Methods 2, 20–33 (1997).
Allison, D. B. et al. A mixture model approach for the analysis of microarray gene expression data. Comput. Stat. Data Analysis 39, 1–20 (2002). This was the first paper in the field of microarray research to introduce mixture modelling.
Pavlidis, P., Li, Q. & Noble, W. S. The effect of replication on gene expression microarray experiments. Bioinformatics 19, 1620–1627 (2003).
Tsai, C. A., Hsueh, H. M. & Chen, J. J. Estimation of false discovery rates in multiple testing: application to gene microarray data. Biometrics 59, 1071–1081 (2003).
Pan, W., Lin, J. & Le, C. T. How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biol. 3, research0022 (2002).
Zien, A., Fluck, J., Zimmer, R. & Lengauer, T. Microarrays: how many do you need? J. Comput. Biol. 10, 653–667 (2003).
Gadbury, G. L. et al. Power analysis and sample size estimation in the age of high dimensional biology: a parametric bootstrap approach and examples from microarray research. Stat. Methods Med. Res. 13, 325–338 (2004). This paper offers convenient FDR-based methods for power analysis and sample-size estimation in microarray and other high-dimensional testing situations.
Pawitan, Y., Michiels, S., Koscielny, S., Gusnanto, A. & Ploner, A. False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics 21, 3017–3024 (2005).
Muller, P., Parmigiani, G., Robert, C. & Rousseau, J. Optimal sample size for multiple testing: The case of gene expression microarrays. J. Am. Stat. Assoc. 99, 990–1001 (2004).
Dobbin, K. & Simon, R. Sample size determination in microarray experiments for class comparison and prognostic classification. Biostatistics. 6, 27–38 (2005).
Garge, N., Page, G. P., Spargue, A. P., Gorman, B. S. & Allison, D. B. Reproducible clusters from microarray research: whither? BMC Bioinformatics 6 (Suppl. 2), S10 (2005). The authors evaluate clustering techniques using real data, and find that with sample sizes of less than 50, the reproducibility of results is poor.
Kendziorski, C. M., Zhang, Y., Lan, H. & Attie, A. D. The efficiency of pooling mRNA in microarray experiments. Biostatistics 4, 465–477 (2003). This paper clarifies concepts and statistical design issues that are involved with mRNA pooling in microarray experiments.
Kendziorski, C., Irizarry, R. A., Chen, K. S., Haag, J. D. & Gould, M. N. On the utility of pooling biological samples in microarray experiments. Proc. Natl Acad. Sci. USA 102, 4252–4257 (2005).
Chen, Y., Dougherty, E. R. & Bittner, M. L. Ratio-based decisions and the quantitative analysis of cDNA microarray images. J. Biomed. Opt. 2, 364–374 (1997).
Schadt, E. E., Li, C., Ellis, B. & Wong, W. H. Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. J. Cell Biochem. Suppl. 37, 120–125 (2001).
Ekstrom, C. T., Bak, S., Kristensen, C. & Rudemo, M. Spot shape modelling and data transformations for microarrays. Bioinformatics 20, 2270–2278 (2004).
Steinfath, M. et al. Automated image analysis for array hybridization experiments. Bioinformatics 17, 634–641 (2001).
Yang, Y. H., Buckley, M. J. & Speed, T. P. Analysis of cDNA microarray images. Brief Bioinform. 2, 341–349 (2001).
Quackenbush, J. Microarray data normalization and transformation. Nature Genet. 32, 496–501 (2002).
Yang, Y. H. et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 30, e15 (2002).
Smyth, G. K. & Speed, T. Normalization of cDNA microarray data. Methods 31, 265–273 (2003).
Qin, L. X. & Kerr, K. F. Empirical evaluation of data transformations and ranking statistics for microarray analysis. Nucleic Acids Res. 32, 5471–5479 (2004). This article presents the effect of different image-processing and normalization techniques on microarray analysis conclusions.
Affymetrix. Affymetrix Expression Analysis Technical Manual (Affymetrix, Santa Clara, California, 2004).
Nielsen, H. B., Gautier, L. & Knudsen, S. Implementation of a gene expression index calculation method based on the PDNN model. Bioinformatics 21, 687–688 (2005).
Irizarry, R. A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).
Mehta, T., Tanik, M. & Allison, D. B. Towards sound epistemological foundations of statistical methods for high-dimensional biology. Nature Genet. 36, 943–947 (2004). This paper clarifies the importance of methods for evaluating the validity of proposed statistical methodologies in high-dimensional biology, with an emphasis on microarray research.
Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).
Choe, S. E., Boutros, M., Michelson, A. M., Church, G. M. & Halfon, M. S. Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset. Genome Biol. 6, R16 (2005).
Cope, L. M., Irizarry, R. A., Jaffee, H. A., Wu, Z. & Speed, T. P. A benchmark for Affymetrix GeneChip expression measures. Bioinformatics 20, 323–331 (2004).
Chen, D. T. A graphical approach for quality control of oligonucleotide array data. J. Biopharm. Stat. 14, 591–606 (2004).
Hsiao, A., Worrall, D. S., Olefsky, J. M. & Subramaniam, S. Variance-modeled posterior inference of microarray data: detecting gene-expression changes in 3T3-L1 adipocytes. Bioinformatics 20, 3108–3127 (2004).
Miller, R. A., Galecki, A. & Shmookler-Reis, R. J. Interpretation, design, and analysis of gene array expression experiments. J. Gerontol. A 56, B52–B57 (2001).
Budhraja, V., Spitznagel, E., Schaiff, W. T. & Sadovsky, Y. Incorporation of gene-specific variability improves expression analysis using high-density DNA microarrays. BMC Biol. 1, 1 (2003).
Cui, X., Hwang, J. T., Qiu, J., Blades, N. J. & Churchill, G. A. Improved statistical tests for differential gene expression by shrinking variance components estimates. Biostatistics 6, 59–75 (2005). This article provides one method of shrinkage and compares its performance with other variance shrinkage methods.
Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci USA 98, 5116–5121 (2001).
Baldi, P. & Long, A. D. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17, 509–519 (2001).
Edwards, J. W. et al. Empirical Bayes estimation of gene-specific effects in micro-array research. Funct. Integr. Genomics 5, 32–39 (2005).
Ge, Y. C., Dudoit, S. & Speed, T. P. Resampling-based multiple testing for microarray data analysis. Test 12, 1–77 (2003).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate — a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
Hsueh, H. M., Chen, J. J. & Kodell, R. L. Comparison of methods for estimating the number of true null hypotheses in multiplicity testing. J. Biopharm. Stat. 13, 675–689 (2003).
van der Lann, M. J., Dudoit, S. & Pollard, K. S. Augmentation procedures for control of the generalized family-wise error rate and tail probabilities for the proportion of false positives. Stat. Appl. Genet. Mol. Biol. 3, A15 (2004).
Storey, J. D. The positive false discovery rate: A Bayesian interpretation and the q-value. Ann. Stat. 31, 2013–2035 (2003). This paper clarifies the key terminology and concepts used in FDR-related methods.
Do, K. A., Mueller, P. & Tang, F. A nonparametric Bayesian mixture model for gene expression. J. R. Stat. Soc. Ser. C 54, 1–18 (2005).
Pounds, S. & Morris, S. W. Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics 19, 1236–1242 (2003).
Datta, S. & Datta, S. Empirical Bayes screening of many p-values with applications to microarray studies. Bioinformatics 21, 1987–1994 (2005).
Efron, B., Tibshirani, R., Storey, J. D. & Tusher, V. G. Empirical Bayes analysis of a microarray experiment. J. Am. Stat. Assoc. 96, 1151–1160 (2001).
Newton, M. A., Noueiry, A., Sarkar, D. & Ahlquist, P. Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5, 155–176 (2004).
Newton, M. A., Kendziorski, C. M., Richmond, C. S., Blattner, F. R. & Tsui, K. W. On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J. Comput. Biol. 8, 37–52 (2001).
Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genet. 34, 267–273 (2003).
Osier, M. V. in DNA Microarrays and Statistical Genomic Techniques: Design, Analysis, and Interpretation of Experiments (Marcel Dekker, New York, 2005).
Osier, M. V., Zhao, H. & Cheung, K. H. Handling multiple testing while interpreting microarrays with the Gene Ontology Database. BMC Bioinformatics 5, 124 (2004).
Khatri, P., Draghici, S., Ostermeier, G. C. & Krawetz, S. A. Profiling gene expression using onto-express. Genomics 79, 266–270 (2002).
Pavlidis, P., Weston, J., Cai, J. & Noble, W. S. Learning gene functional classifications from multiple data types. J. Comput. Biol. 9, 401–411 (2002).
Pavlidis, P., Qin, J., Arango, V., Mann, J. J. & Sibille, E. Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex. Neurochem. Res. 29, 1213–1222 (2004). This study introduces a gene-class testing method that uses the full continuous evidence that is available within p -values.
Ben Shaul, Y., Bergman, H. & Soreq, H. Identifying subtle interrelated changes in functional gene categories using continuous measures of gene expression. Bioinformatics 21, 1129–1137 (2005).
Zeeberg, B. R. et al. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol. 4, R28 (2003).
Damian, D. & Gorfine, M. Statistical concerns about the GSEA procedure. Nature Genet. 36, 663 (2004).
Persson, S., Wei, H., Milne, J., Page, G. P. & Somerville, C. R. Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets. Proc. Natl Acad. Sci. USA 102, 8633–8638 (2005).
Kyng, K. J., May, A., Kolvraa, S. & Bohr, V. A. Gene expression profiling in Werner syndrome closely resembles that of normal aging. Proc. Natl Acad. Sci. USA 100, 12259–12264 (2003).
Schmid, C. H., Lau, J., McIntosh, M. W. & Cappelleri, J. C. An empirical study of the effect of the control rate as a predictor of treatment efficacy in meta-analysis of clinical trials. Stat. Med. 17, 1923–1942 (1998).
Berger, R. L. Multiparameter hypothesis testing and acceptance sampling. Technometrics 24, 295–300 (1982).
Neuhauser, M., Boes, T. & Jockel, K. H. Two-part permutation tests for DNA methylation and microarray data. BMC Bioinformatics 6, 35 (2005).
Barry, W. T., Nobel, A. B. & Wright, F. A. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 21, 1943–1949 (2005).
Pan, W. On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression. Bioinformatics 19, 1333–1340 (2003).
Xu, R. H. & Li, X. C. A comparison of parametric versus permutation methods with applications to general and temporal microarray gene expression data. Bioinformatics 19, 1284–1289 (2003).
Landgrebe, J., Wurst, W. & Welzl, G. Permutation-validated principal components analysis of microarray data. Genome Biol. 3, RESEARCH0019 (2002).
Troendle, J. F., Korn, E. L. & McShame, L. M. An example of slow convergence of the bootstrap in high dimensions. Am. Stat. 58, 25–29 (2004). This presents an excellent overview of the nuances of resampling methodology that is used in microarray research, and discusses the fact that such methods are not assumption-free panaceas that are valid under all circumstances.
Kennedy, P. E. & Cade, B. S. Randomization tests for multiple regression. Commun. Stat. 25, 923–936 (1996).
Gadbury, G. L., Page, G. P., Heo, M., Mountz, J. D. & Allison, D. B. Randomization tests for small samples: an application for genetic expression data. J. R. Stat. Soc. Ser. C 52, 365–376 (2003).
Yeung, K. Y., Haynor, D. R. & Ruzzo, W. L. Validating clustering for gene expression data. Bioinformatics 17, 309–318 (2001).
Datta, S. & Datta, S. Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19, 459–466 (2003).
Shih, J. H. et al. Effects of pooling mRNA in microarray class comparisons. Bioinformatics 20, 3318–3325 (2004).
Yeung, K. Y., Medvedovic, M. & Bumgarner, R. E. From co-expression to co-regulation: how many microarray experiments do we need? Genome Biol. 5, R48 (2004).
Bryan, J. Problems in gene clustering based on gene expression data. J. Multivariate Analysis 90, 44–66 (2004). This is an excellent overview of the methodological and conceptual challenges in the use of cluster analysis in gene-expression studies.
Kerr, M. K. & Churchill, G. A. Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proc. Natl Acad. Sci. USA 98, 8961–8965 (2001).
Zhang, K. & Zhao, H. Assessing reliability of gene clusters from gene expression data. Funct. Integr. Genomics 1, 156–173 (2000).
Tseng, G. C. & Wong, W. H. Tight clustering: a resampling-based approach for identifying stable and tight patterns in data. Biometrics 61, 10–16 (2005).
Horth, J. Computer Intensive Statistical Methods Validation, Model Selection and Boostrap (Chapman and Hall, London, 1994).
Ambroise, C. & McLachlan, G. J. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl Acad. Sci. USA 99, 6562–6566 (2002). This article addresses selection bias in the context of predictive error-estimation and cross-validation for microarray studies.
Furlanello, C., Serafini, M., Merler, S. & Jurman, G. Entropy-based gene ranking without selection bias for the predictive classification of microarray data. BMC Bioinformatics 4, 54 (2003).
Fu, W. J., Carroll, R. J. & Wang, S. Estimating misclassification error with small samples via bootstrap cross-validation. Bioinformatics 21, 1979–1986 (2005).
Dobbin, K. & Simon, R. Sample size determination in microarray experiments for class comparison and prognostic classification. Biostatistics 6, 27–38 (2005).
Hwang, D., Schmitt, W. A., Stephanopoulos, G. & Stephanopoulos, G. Determination of minimum sample size and discriminatory expression patterns in microarray data. Bioinformatics 18, 1184–1193 (2002).
Mukherjee, S. et al. Estimating dataset size requirements for classifying DNA microarray data. J. Comput. Biol. 10, 119–142 (2003).
Rajeevan, M. S., Ranamukhaarachchi, D. G., Vernon, S. D. & Unger, E. R. Use of real-time quantitative PCR to validate the results of cDNA array and differential display PCR technologies. Methods 25, 443–451 (2001).
Rockett, J. C. & Hellmann, G. M. Confirming microarray data — is it really necessary? Genomics 83, 541–549 (2004).
Rocke, D. M. & Durbin, B. Approximate variance-stabilizing transformations for gene-expression microarray data. Bioinformatics 19, 966–972 (2003).
Pounds, S. & Cheng, C. Statistical development and evaluation of microarray gene expression data filters. J. Comput. Biol. 12, 482–495 (2005).
US