Abstract
Advances in microarray technology open new challenges in data collection, analysis and interpretation. In this review paper, we focus on issues related to obtaining trustworthy normalized data and results of differential expression analysis. In particular, we briefly summarize discussions on sources of biological and technological variation, experimental design, design of arrays, normalization and error models, differential expression and multiple comparison issues. These issues remain major bottlenecks to developing standards and obtaining useful and applicable results of future analysis such as cluster analysis, network modeling, etc.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amaratunga, D, Cabrera, J. A Resistant Walk through the Microarray Data Minefield. Presentation at Microarray Data Analysis Using Statistics and Standards to Navigate the Microarray Minefield, http://www.healthtech.com/200I/mda/(2001).
Baldi, P, Long, AD. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17(6) (Jun 2001): 509–19.
Bartosiewicz, M, Trounstine, M, Barker, D, Johnston, R, Buckpitt, A. Development of a toxicological gene array and quantitative assessment of this technology. Arch Biochem Biophys 376 (2000): 66–73.
Bassett, DE Jr., Eisen, MB, Boguski, MS. Gene Expression Informatics-It’s All in Your Mine. Nature Genetics 21(supplement) (1999): 51–55.
Benjamini, Y, Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B. 57 (1995): 289–300.
Bier, FF, Kleinjung, F, Fresenius, J. Feature-size limitations of microarray technology-a critical review. Anal Chem 371(2) (Sep 2001): 151–6.
Brazma, A, Vilo, J. Gene expression data analysis. Microbes Infect 3(10) (Aug 2001): 823–9.
Brown, CS, Goodwin, PC, Sorger, PK. Image metrics in the statistical analysis of DNA microarray data. Proc Natl Acad Sci U S A 98(16) (Jul 31 2001): 8944–9.
Casella, G, Berger, RL. Statistical inference. Belmont, CA: Wadsworth Publishing Company, 1990.
Churchill, GA, Oliver, B. Sex, flies and microarrays. Nature Genetics 29(4) (Dec 2001): 355–6.
Cochran, WG, Cox, GM. Experimental Designs. New York: Wiley, 1992.
Craig, BA, Vitek, O, Black, MA, Tanurdzik, M, Doerge, RW. Proceedings of the 2001 Kansas State University Conference on Applied Statistics in Agriculture. 2001.
Dudoit, S, Yang, YH, Callow, MJ, Speed, TP. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. http://www.stat.berkeley.edu/users/terry/zarray/TechReport/578.pdf (2000).
Efron, B, Tibshirani, R, Storey, JD, Tusher, V. Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association 96 (2001): 1151–1160.
Elashoff, M. Ensuring Good Microarray Data. Presentation at Microarray Data Analysis Using Statistics and Standards to Navigate the Microarray minefield. http://www.healthtech.com/2001/mda/ (2001).
Fisher, RA. The Design of Experiments, 6th edition. London: Oliver and Boyd, 1951.
Gould, W, Rogers, WH. Quantile regression as an alternative to robust regression. Proceedings of the Statistical Computing Section. Alexandria, VA: American Statistical Association, 1994.
Hess, KR, Zhang, W, Baggerly, KA, Stivers, DN, Coombes, KR, Zhang, W. Microarrays: handling the deluge of data and extracting reliable information. Trends Biotechnol 19(11) (Nov 2001): 463–8.
Houts, T. Towards the quantitative microarray analysis pitfalls and Progress. Presentation at Microarray Data Analysis Using Statistics and Standards to Navigate the Microarray Minefield. http://www.healthtech.com/2001/mda/(2001).
Hughes,, TR, Marton, MJ, Jones, AR, Roberts, CJ, Stoughton, R, Armour, CD, Bennett, HA, Coffey, E, Dai, H, He, YD, Kidd, MJ, King, AM, Meyer, MR, Slade, D, Lum, PY, Stepaniants, SB, Shoemaker, DD, Gachotte, D, Chakraburtty, K, Simon, J, Bard, M, Friend, SH. Functional Discovery via a Compendium of Expression Profiles. Cell 102 (2000), 109–126.
Jin, W, Riley, RM, Wolfinger, RD, White, KP, Passador-Gurgel, G, Gibson, G. The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster Nature Genetics 29(4) (Dec 2001): 389–95.
Kalnin, N. Personal communication. Clontech, 2001.
Kepler, T, Crosby, L, Morgan, KT. Normalization and analysis of DNA microarray data by self-consistency and local regression. Nucleic Acids Research (Submitted 2000): Santa Fe Institute preprint 00-09-055.
Kerr, MK, Churchill, GA. Experimental Design for Gene Expression Microarrays. Biostatistics 2(2) (2001), 183–201.
Kerr, MK, Churchill, GA. Statistical Design and the Analysis of Gene Expression Microarray Data. Genetical Research 77 (2001): 123–128.
Kerr, MK, Leiter, EH, Picard, L, Churchill, GA. Analysis of a designed microarray experiment. Proceedings of the IEEE-Eurasip Nonlinear Signal and Image Processing Workshop (June 3–6 2001).
Kerr, MK, Afshari, CA, Bennett, L, Bushel, P, Martinez, J, Walker, NJ, Churchill, GA. Statistical analysis of a gene expression microarray experiment with replication. Statistica Sinica (to appear 2001).
Kerr, MK, Martin, M, Churchill, GA. Analysis of variance for gene expression microarray data. J Comput Biol 7(6) (2000): 819–37.
Koenker, R, Bassett, G. Regression Quantiles. Econometrica 46 (1978): 33–50.
Lee, ML, Kuo, FC, Whitmore, GA, Sklar, J. Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc Natl Acad Sci U S A 97(18) (2000): 9834–9.
Long, AD, Mangalam, HJ, Chan, BY, Tolleri, L, Hatfield, GW, Baldi, P. Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12. J Biol Chem 276(23) (Jun 2001): 19937–44.
Mills, JC, Gordon, JI. A new approach for filtering noise from high-density oligonucleotide microarray datasets. Nucleic Acids Res 29(15) (Aug 2001): E72–2.
Piantadosi, S. Clinical Trials: A Methodological Perspective, New York: John Wiley, 1997.
Pritchard, CC, Hsu, L, Delrow, J, Nelson, PS. Project normal: Defining normal variance in mouse gene expression. Proc Natl Acad Sci U S A 98(23) (2001): 13266–71.
Sapir, M, Churchill, GA. Estimating the posteriorprobability of differential gene expression from microarray data. Poster: http://www.jax.org/research/churchill/(2000).
Samartzidou, H. Validating Microarray Results: Using Control Reagents and Software Tools to Analyse, Standardize, and Compare Microarray Data. Presentation at Microarray Data Analysis Using Statistics and Standards to Navigate the Microarray Minefield http://www.healthtech.com/2001/mda/ (2001).
Sen, Churchill, G. A Statistical framework for quantitative trait mapping, Genetics 159 (2001): 371–387.
Storey, JD, Tibshirani, R. Estimating false discovery rates under dependence, with applications to DNA microarrays. Submitted to Journal of the American Statistical Society. Technical Report 2001–28, Department of Statistics, Stanford University http://www-stat.stanford.edu/∼jstorey/papers/dep.pdf (2001)
Thomas, JG, Olson, JM, Tapscott, SJ, Zhao, LP. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res 11(7) (Jul 2001): 1227–36.
Tseng, GC, Oh, MK, Rohlin, L, Liao, JC, Wong, WH. Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res 29(12) (2001): 2549–57.
Wang, X, Ghosh, S, Guo, SW. Quantitative quality control in microarray image processing and data acquisition. Nucleic Acids Res 29(15) (2001): E75–5.
Westfal, P, Young, S. Resampling-based multiple testing. Whiley, 1993.
Wolfinger, RD, Gibson, G, Wolfinger, ED, Bennett, L, Hamadeh, H, Bushel, P, Afshari, C, Paules, RS. Assessing gene significance from cDNA microarray data via mixed models. Journal of Computational Biology 8(6) (2001): 625–637, http://brooks.statgen.ncsu.edu/ggibson/Pubs.htm
Wu, TD. Analysing gene expression data from DNA microarrays to identify candidate genes. Journal of Pathology 195(1) (Sep 2001): 53–65.
Yang, MC, Ruan, QG, Yang, JJ, Eckenrode, S, Wu, S, Mclndoe, RA, She, JX. A statistical method for flagging weak spots improves normalization and ratio estimates in microarrays. Physiol Genomics 7(1) (Oct 2001): 45–53.
Yang, YH, Dudoit, S, Luu, P, Speed, TP1. Normalization for cDNA Microarray Data. San Jose, California: SPIE BiOS, 2001.
Zien, A, Fluck, J, Zimmer, R, Lengauer, T. Microarrays: How Many Do You Need? Proceedings, RECOMB‘02, to appear: http://cartan.gmd.de/∼zien/paper/recomb02.pdf (2002).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Kluwer Academic Publishers
About this chapter
Cite this chapter
Bobashev, G.V., Das, S., Das, A. (2002). Experimental Design for Gene Microarray Experiments and Differential Expression Analysis. In: Lin, S.M., Johnson, K.F. (eds) Methods of Microarray Data Analysis II. Springer, Boston, MA. https://doi.org/10.1007/0-306-47598-7_3
Download citation
DOI: https://doi.org/10.1007/0-306-47598-7_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4020-7111-9
Online ISBN: 978-0-306-47598-6
eBook Packages: Springer Book Archive