Normalization and quantification of differential expression in gene expression microarrays

Briefings in Bioinformatics

Volumn 7, Issue 2, 2006, Pages 166-177

  Steinhoff, Christine ^a   Vingron, Martin ^b  

^a MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS   (Germany)

^b NONE

Author keywords

Differential gene expression; Low level analysis; Microarray; Normalization

Indexed keywords

MESSENGER RNA;

ANALYTICAL ERROR; BAYES THEOREM; COMPUTER PROGRAM; DATA ANALYSIS; GENE EXPRESSION PROFILING; GENE IDENTIFICATION; INFORMATION PROCESSING; INTERNET; KRUSKAL WALLIS TEST; LINEAR REGRESSION ANALYSIS; LINEAR SYSTEM; MATHEMATICAL COMPUTING; MAXIMUM LIKELIHOOD METHOD; MICROARRAY ANALYSIS; NONPARAMETRIC TEST; PROBABILITY; QUANTITATIVE ANALYSIS; RANK SUM TEST; REFERENCE VALUE; REVIEW; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE; ALGORITHM; CALIBRATION; DNA MICROARRAY; INTERNATIONAL COOPERATION; METHODOLOGY; PRACTICE GUIDELINE; STANDARD;

ALGORITHMS; CALIBRATION; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; GUIDELINES; INTERNATIONALITY; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REFERENCE VALUES;

EID: 33748445546     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbl002     Document Type: Review

References (82)

1. Smyth GK, Speed TP. Normalization of cDNA microarray data. Methods 2003;31:265-73.
2. Huber W, Heydebreck Av, Vingron M. Analysis of Microarray Gene Expression Data. Chichester: John Wiley & Sons, 2003.
3. Steinhoff C, Vingron M. Normalization Strategies for Microarray Data Analysis. Taylor & Francis Group, 2005.
4. Scheid S, Spang R. Microarray data analysis: Differential gene expression. Taylor & Francis Group, 2005.
5. Smyth GK, Yang YH, Speed TP. Statistical Issues in cDNA Microarray Data Analysis. Totowa: Humana Press, 2002.
6. Pan W. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 2002;18(4):546-54.
7. Elo L, Aittokallio T, Filen S, et al. The effect of replication on gene rankings: A practical comparison of methods for detecting differential expression in microarray experiments, In: Bioinformatics Research and Education Workshop, Berlin, 2005.
8. Ihaka R, Gentleman R. R: A language for data analysis and graphics. J Comput Graph Stat 1996;5:299-314.
9. Gentleman R, Carey V, Bates D, et al. Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology 2004;5(10):R80.
10. Speed T. Statistical Analysis of Gene Expression Microarray Data. CRC Press, 2003.
11. Simon R, Korn EL, McShane LM, et al. Design and Analysis of DNA Microarray Investigations. Springer, 2003.
12. Parmigiani G, Garrett ES, Irizarry R (eds). The Analysis of Gene Expression Data. Springer, 2003.
13. Lee M-LT. Analysis of Microarray Gene Expresson Data. Kluwer Academic Publishers, 2004.
14. Gentleman R, Carey V, Huber W, et al. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer, 2005.
15. Nuber UA. DNA Microarrays. Taylor & Francis Group: Garland Science Publishing, 2005.
16. Dudoit S, Yang YH, Callow MJ, et al. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 2002;12: 111-39.
17. Yang IV, Chen E, Hasseman JP, et al. Within the fold: Assessing differential expression measures and reproducibility in microarray assays. Genome Biol 2002; 3(11):rEsearch0062.1-0062.12.
18. Huber W, Heydebreck Av, Vingron M. Low-Level analysis of microarray experiments. Wiley-VCH, 2005.
19. Cui X, Kerr MK, Churchill GA. Transformation for cDNA Microarray Data. Stat Appl Genet Mol Biol 2003;2(1): Article 4.
20. Kreil DP. There is no silver Bullet - a guide to low-level data transforms and normalisation methods for microarray data. Brief Bioinform 2005;6(1):86-97.
21. Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003;4(2): 249-64.
22. Irizarry RA, Bolstad BM, Collin F, et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 2003;31(4):e15.
23. Wu Z, Irizarry RA, Gentleman R, et al. A model-based background adjustment for oligonucleotide expression arrays. J Am Stat Assoc 2004;99(468):909-17.
24. Li C, Wong WH. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. PNAS 2001;98:31-6.
25. Choe SE, Boutros M, Michelson AM, et al. Preferred analysis methods for Affymetrix GeneChips revealed by a wholly definer control dataset. BMC Bioinformatics 2005;6:R16.
26. Yang YH, Buckley MJ, Dudoit S, et al. Comparison of methods for image analysis on cDNA microarray data. J Comput Graph Statist 2002;11(1):108-36.
27. Shena M, Shalon D, Davis RW, et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270(5235):467-70.
28. DeRisi JL, Iyer VR, Brown PO. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 1997;278(5338):680-6.
29. Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 1999;286:531-7.
30. Virtaneva K, Wright FA, Tanner SM, et al. Expression profiling reveals fundamental biological differences in acute myeloid leukemia with isolated trisomy 8 and normal cytogenetics. PNAS 2001;98:1124-9.
31. Yang YH, Dudoit S, Luu P, et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 2002;30(4):e15.
32. Workman C, Jensen LJ, Jarmer H, et al. A new non-linear normalization method for reducing variability in DNA microarray experiments. Genome Biol 2002;3(9): Research0048.1-0048.16.
33. Bolstad BM, Irizarry RA, Astrand M, et al. A Comparison of normalization methods for high density oligonucleotide array based on variance and bias. Bioinformatics 2003;19(2): 185-93.
34. Rocke DM, Durbin BP. A model for measurement error for gene expression arrays. J Computat Biol 2001;8(6): 557-69.
35. Huber W, Heydebreck Av, Sültmann H, et al. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002;18(S1):S96-104.
36. Durbin BP, Hardin JS, Hawkins DM, et al. A variance stabilizing transformation for gene-expression microarray data. Bioinformatics 2002;18(Suppl1):S96-104.
37. Chen Y, Dougherty ER, Bittner ML. Ratio based decisions and the quantitative analysis of cDNA microarray images. J Biomed Opt 1997;2:364-74.
38. Kerr MK, Martin M, Churchill GA. Analysis of variances from gene expression microarray data. J Comput Biol 2000; 7(6):819-37.
39. Kepler TB, Crosby L, Morgan KT. Normalization and analysis of DNA microarray data by self-consistency and local regression. Genome Biol 2002;3(7): Research0037.1-0037.12.
40. Newton MA, Kendziorski CM, Richmond CS, et al. On Differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data. J Comput Biol 2001;8(1): 37-52.
41. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004;3(1).
42. Troyanskaya OG, Garber ME, Brown PO, et al. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 2002; 18(11):1454-61.
43. Lönnstedt I, Speed TP. Replicated microarray data. Statistica Sinica 2002;12:31-46.
44. Tusher V, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. PNAS 2001;98:5116-24.
45. Efron B, Tibshirani R, Storey JD, et al. Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 2001; 96(456):1151-60.
46. Broberg P. Statistical methods for ranking differentially expressed genes. Genome Biol 2003;4:R41.
47. Tibshirani R, Chu G, Hastie T. The samr Package. Bioconductor, 2005.
48. Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes. Bioinformatics 2001; 17(6):509-19.
49. Thomas JG, Olson JM, Tapscott SJ, et al. An efficient approach and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res 2001;11(7): 1227-36.
50. Lin DM, Yang YH, Scolnick JA, et al. Spatial patterns of gene expression in the olfactory bulb. PNAS 2004; 101(34):12718-23.
51. Jain N, Thatte J, Braciale T, et al. Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays. Bioinformatics 2003;19(15): 1945-51.
52. Yan X, Deng M, Fung WK, et al. Detecting differentially expressed genes by relative entropy. J Theor Biol 2005;234: 395-402.
53. Westfall PH, Young SS. Re-Sampling Based Multiple Testing. New York: Wiley, 1993.
54. Bar-Joseph Z. Analyzing time series gene expression data. Bioinformatics 2004;20(16):2493-503.
55. Storey JD, Xiao W, Leek JT, et al. Significance analysis of time course microarray experiments. PNAS 2005, in press.
56. Guo X, Qi H, Verfaillie CM, et al. Statistical significance analysis of longitudinal gene expression data. Bioinformatics 2003;19(13):1628-35.
57. Pan W, Lin J, Le CT. A mixture model approach to detecting differentially expressed genes with microarray data. Funct Integr Genomics 2003;3(3):117-24.
58. Xu XL, Olson JM, Zhao LP. A regression-based method to identify differentially expressed genes in microarray time course studies and its application in an inducible Huntington's disease transgenic model. Hum Mol Genet 2002;11(17):1977-85.
59. Zhao LP, Prentice RL, Breeden L. Statistical modeling of large microarray data sets to identify stimulus-response profiles. PNAS 2001;98:5631-6.
60. Bar-Joseph Z, Gerber G, Jaakkola T, et al. Comparing the continous representation of time series expression profiles to identify differentially expressed genes. PNAS 2003;100: 10146-51.
61. Schliep A, Costa IG, Steinhoff C, et al. Analyzing gene expression time-courses. IEEE Trans Comput Biol 2005;2(3): 179-93.
62. Pounds S, Cheng C. Statistical development and evaluation of microarray gene expression data filters. J Comput Biol 2005;12(4):482-95.
63. Dudoit S, Shaffer JP, Boldrick JC. Multiple hypothesis testing in microarray experiments. Stat Sci 2003;18(1):71-103.
64. Scheid S, Spang R. A stochastic downhill search algorithm for estimating the local false discovery rate. IEEE Trans Comput Biol 2004;1(3):98-108.
65. Tsai CA, Hsueh HM, Chen JJ. Estimation of false discovery rates and multiple testing: Application to gene microarray data. Biometrics 2003;59(4):1071-81.
66. Holm S. A simple sequentially rejective multiple test procedure. Scand J Statist 1979;6:65-70.
67. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc B 1995;57(1):289-300.
68. Benjamini Y, Hochberg Y. On the adaptive control of the false discovery rate in multiple testing with independent statistics. J Educ Behav Stat 2000;25(1):60-83.
69. Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple hypothesis testing under dependencies. Ann Statist 2001;29(4):1165-88.
70. Storey JD. The positive false discovery rate: A bayesian interpretation and the Q-Value. Ann Math Statist 2003; 31(6):2013-35.
71. Benjamini Y, Liu W. A step-down multiple hypothesis testing procedure that controls the false discovery rate under independence. J Stat Plan Infer 1999;82:163-70.
72. Yekutieli D, Benjamini Y. Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. J Stat Plan Infer 1999;82(1-2): 171-96.
73. Allison DB, Gadbury GL, Heo M, et al. A mixture model approach for the analysis of microarray gene expression data. Comput Statist Data Anal 2002;39(1):1-20.
74. Pounds S, Morris SW. Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of P-values. Bioinformatics 2003;19(10).
75. Reiner A, Yekutieli D, Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 2003;19(3): 368-75.
76. Pounds S, Cheng C. Improving false discovery rate estimation. Bioinformatics 2004;20(11):1737-45.
77. Storey JD, Tibshirani R. Statistical significance for genome-wide studies. PNAS 2003;100(16):9440-5.
78. Storey JD. A direct approach to false discovery rates. J R Statist Soc B 2002;64(3):479-98.
79. Troyanskaya OG. Putting microarrays in a context: Integrated analysis of diverse biological data. Brief Bioinformat 2005;6(1):34-43.
80. Stuart JM, Segal A, Koller D, et al. A gene-coexpression network for global discovery of conserved genetic modules. Science 2003;302:249-54.
81. McCarroll SA, Murphy CT, Zou S, et al. Comparing genomic expression patterns across species identifies shared transcriptional profile in aging. Nature Genet 2004;36(2):197-204.
82. Duboit S, Yang YH. Bioconductor R packages for exploratory analysis and normalisation of cDNA microarray data. New York: Springer; 2002.