Statistical considerations for analysis of microarray experiments

Clinical and Translational Science

Volumn 4, Issue 6, 2011, Pages 466-477

  Owzar, Kouros ^a   Barry, William T ^a   Jung, Sin Ho ^a  

^a DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Clinical trials; Microarrays; Multiple testing; Overfitting; Pathways; Power; Preprocessing; Software; Statistical inference; Supervised learning; Unsupervised learning; Validation

Indexed keywords

ALGORITHM; ANALYSIS OF VARIANCE; ARTICLE; BLOOD PRESSURE; COMPUTER PROGRAM; CORRELATION COEFFICIENT; GENE EXPRESSION PROFILING; GENETIC MARKER; GENOMICS; LUNG CANCER; MICROARRAY ANALYSIS; MOLECULAR GENETICS; MOLECULAR PROBE; NOISE; PHENOTYPE; PRIORITY JOURNAL; PROGNOSIS; REPRODUCIBILITY; SIMULATION; VALIDATION STUDY;

ALGORITHMS; CLINICAL TRIALS AS TOPIC; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, LEUKEMIC; GENE EXPRESSION REGULATION, NEOPLASTIC; GENETIC MARKERS; HUMANS; LEUKEMIA; LUNG NEOPLASMS; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PHENOTYPE; PROGNOSIS; SOFTWARE;

EID: 84855193909     PISSN: 17528054     EISSN: 17528062     Source Type: Journal    
DOI: 10.1111/j.1752-8062.2011.00309.x     Document Type: Article

References (57)

1. Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999; 286(5439): 531-537.
2. Beer DG, Kardia S, Huang C-C, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002; 8(8): 816-824.
3. Speed T. Statistical Analysis of Gene Expression Microarray Data. Boca Raton, FL: Chapman and Hall/CRC; 2003.
4. McLachlan G, Do K, Ambroise C. Analyzing Microarray Gene Expression Data. Hoboken, NJ: Wiley-Interscience; 2004.
5. Simon R, Korn EL, McShane LM, Radmacher MD, Wright GW, Zhao Y. Design and Analysis of DNA Microarray Investigations. New York: Springer Verlag; 2005.
6. Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003; 19(2): 185-193.
7. Irizarry A, Hobbs RB, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003; 4(2): 249-264.
8. Owzar K, Barry WT, Jung SH, Sohn I, George SL. Statistical challenges in preprocessing in -microarray experiments in cancer. Clin Cancer Res. 2008; 14(19): 5959-5966.
9. Cope LM, Irizarry RA, Jaffee HA, Wu Z, Speed TP. A benchmark for Affymetrix GeneChip expression measures. Bioinformatics. 2004; 20(3): 323-331.
10. Irizarry RA, Wu Z, Jaffee HA. Comparison of Affymetrix GeneChip expression measures. Bioinformatics. 2006; 22(7): 789-794.
11. Schmid R, Baum P, Ittrich C, et al. Comparison of normalization methods for Illumina -BeadChip HumanHT-12 v3. BMC Genomics. 2010; 11(1): 349.
12. Xie Y, Wang X, Story M. Statistical methods of background correction for Illumina BeadArray data. Bioinformatics. 2009; 25(6): 751-757.
13. Bhattacharyya GK, Johnson RA. Statistical Concepts and Methods. Hoboken, NJ: Wiley; 1977.
14. Cox DR. Regression models and life-tables. J Stat Soc Ser B. 1972; 34: 187-220.
15. Jung S-H, Owzar K, George SL. A multiple testing procedure to associate gene expression levels with survival. Stat Med. 2005; 24(20): 3077-3088.
16. Westfall PH, Young SS. Resampling-Based Multiple Testing: Examples and Methods for P-value Adjustment. Wiley Series in Probability & Mathematical Statistics: Applied Probability & Statistics. Hoboken, NJ: John Wiley & Sons; 1992.
17. Ge Y, Dudoit S, Speed TP. Resampling-based multiple testing for microarray data analysis. TEST 2003; 12(1): 1-77.
18. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful -approach to multiple testing. J Stat Soc Ser B. 1995; 57: 289-300.
19. Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. newblock Ann Statist. 2001; 29: 1165-1188.
20. Storey JD. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Statist. 2003; 31(6): 2013-2035.
21. Jung SH, Jang W. How accurately can we control the FDR in analyzing microarray data? -Bioinformatics 2006; 22: 1730-1736.
22. Mardia KV, Kent JT, Bibby JM. Multivariate Analysis. San Diego, CA: Academic Press; 1979.
23. Devroye L, Györfi L, Lugosi G. A Probabilistic Theory of Pattern Recognition. New York: -Springer; 1996.
24. Schoelkopf B, Smola AJ. Learning with Kernels. Cambridge, MA: MIT Press; 2002.
25. Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and Regression Trees. Chapman and Hall/CRC; 1984.
26. Breiman L. Random forests. Mach Learn 2001; 45: 5-32.
27. Ripley BD. Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press; 1996.
28. Tibshirani R. Regression shrinkage and selection via the LASSO. J Stat Soc Ser B. 1996; 58(1): 267-288.
29. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, -Inference, and Prediction. Second Edition. New York: Springer-Verlag; 2009.
30. Mootha VK, Lindgren CM, Eriksson KF, et al. PGC-1alpha-responsive genes involved in -oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003; 34(3): 267-273.
31. Efron B, Tibshirani R.On testing the significance of sets of genes. Ann Appl Stat. 2007; 1(1): 107-129.
32. Barry WT, Nobel AB, Wright FA. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics. 2005; 21(9): 1943-1949.
33. Barry WT, Nobel AB, Wright FA. A statistical framework for testing functional categories in microarray data. Ann Appl Stat. 2008; 2(1): 286-315.
34. Witte JS, Elston RC, Cardon LR. On the relative sample size required for multiple comparisons. Stat Med. 2000; 29: 369-372.
35. Wolfinger RD, Gibson G, Wolfinger ED, et al. Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol. 2001; 8: 625-637.
36. Black MA, Doerge RW. Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments. Bioinformatics. 2002; 18: 1609-1616.
37. Pan W, Lin J, Le CT. How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biol. 2002; 3(5): 1-10.
38. Cui X, Churchill GA. How many mice and how many arrays? Replication in mouse cDNA microarray experiments. In: Johnson KJ, Lin S (eds.), Methods of Microarray Data Analysis II. Norwell, MA: Kluwer Academic Publishers; 2003: 139-154.
39. Whitmore GA, Lee MLT. Power and sample size for DNA microarray studies. Stat Med. 2002; 21: 3543-3570.
40. Jung SH, Bang H, Young SS. Sample size calculation for multiple testing in microarray data analysis. Biostatics 2005; 6: 157-169.
41. Jung SH, Young SS. Power and sample size calculation for microarray studies. J Biopharm Stat. In press.
42. Jung SH. Sample size for FDR-control in microarray data analysis. Bioinformatics. 1995; 21: 3097-3103.
43. Pounds S, Cheng C. Sample size determination for the false discovery rate. Bioinformatics. 2005; 21: 4236-4271.
44. Liu P, Hwang JTG. Quick calculation for sample size while controlling false discovery rate with application to microarray analysis. Bioinformatics. 2007; 23: 739-746.
45. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas -distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001; 98: 10869-10874.
46. Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA. 2001; 98: 13790-13795.
47. Van 't Veer LJ, Dai H, Van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002; 415: 530-536.
48. R Development Core Team R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2006.
49. Gentleman RC, Carey VJ, Bates DM, et al. Bioconductor: open software development for -computational biology and bioinformatics. Genome Biol. 2004; 5: R80.
50. Buckner J, Wilson J, Seligman M, et al. The gputools package enables GPU computing in R. Bioinformatics. 2010; 26(1): 134-135.
51. Shterev ID, Jung SH, George SL, et al. permGPU: using graphics processing units in RNA microarray association studies. BMC Bioinformatics. 2010; 11(329).
52. Knuth DE. Literate Programming. California: Stanford University Center for the Study of Language and Information; 1992.
53. F. Leisch Sweave: Dynamic Generation of Statistical Reports Using Literate Data Analysis in Compstat 2002 - Proceedings in Computational Statistics. Heidelberg: Physica Verlag; 2002: 575-580.
54. Venables B, Ripley B. Modern Applied Statistics with S. Fourth Edition.New York: Springer Verlag; 2002.
55. Dalgaard P.Introductory Statistics with R. New York: Springer Verlag; 2002.
56. Gentleman R, Carey V, Huber W, et al. eds. Bioinformatics and Computational Biology -Solutions Using R and Bioconductor. New York: Springer Verlag; 2005.
57. Hahne F, Huber W, Gentleman R, et al. eds. Bioconductor Case Studies. New York: Springer Verlag; 2008.