Analysis of oligonucleotide array experiments with repeated measures using mixed models

BMC Bioinformatics

Volumn 5, Issue , 2004, Pages

  Li, Hao ^a   Wood, Constance L ^a   Getchell, Thomas V ^b,c   Getchell, Marilyn L ^b,c   Stromberg, Arnold J ^a  

^a UNIVERSITY OF KENTUCKY   (United States)

^b UNIVERSITY OF KENTUCKY   (United States)

^c UNIVERSITY OF KENTUCKY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENTIALLY EXPRESSED GENE; LEAST SIGNIFICANT DIFFERENCE TESTS; MICROARRAY DATA ANALYSIS; MULTIPLE TESTING PROBLEMS; OLIGONUCLEOTIDE ARRAYS; REPEATED MEASUREMENTS; SOURCES OF VARIABILITY; TREATMENT COMBINATIONS;

BIT ERROR RATE; BRAIN; GENES; OLIGONUCLEOTIDES; STATISTICAL TESTS;

EXPERIMENTS;

BETA 2 MICROGLOBULIN; CATHEPSIN; DNA; HIGH MOBILITY GROUP N2 PROTEIN; SCLEROPROTEIN; TUMOR SUSCEPTIBILITY GENE 101 PROTEIN;

AGED; ALZHEIMER DISEASE; ANALYTIC METHOD; ANALYTICAL ERROR; ARTICLE; AUTOPSY; BRAIN REGION; CEREBELLUM; CLINICAL ARTICLE; CONTROLLED STUDY; CORRELATION ANALYSIS; DATA ANALYSIS; DNA MICROARRAY; DOWN REGULATION; FACTORIAL ANALYSIS; FEMALE; GENE EXPRESSION; GENE INTERACTION; GENETIC ANALYSIS; GENETIC VARIABILITY; HUMAN; HUMAN CELL; HUMAN TISSUE; MATHEMATICAL ANALYSIS; OLFACTORY BULB; SEQUENCE ANALYSIS; STATISTICAL MODEL; UPREGULATION; ALGORITHM; BIOLOGICAL MODEL; BIOLOGY; COMPUTER PROGRAM; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; METABOLISM; MULTIVARIATE ANALYSIS; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; STATISTICAL ANALYSIS;

MARTES PENNANTI;

ALGORITHMS; ALZHEIMER DISEASE; CEREBELLUM; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DOWN-REGULATION; GENE EXPRESSION PROFILING; HUMANS; MODELS, BIOLOGICAL; MODELS, GENETIC; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; OLFACTORY BULB; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REPRODUCIBILITY OF RESULTS; SOFTWARE; UP-REGULATION;

EID: 13244259309     PISSN: 14712105     EISSN: None     Source Type: Journal    
DOI: 10.1186/1471-2105-5-209     Document Type: Article

References (44)

1. Verbeke G, Molenberghs G: Linear Mixed models for longitudinal data. Springer, New York, NY. 2000.
2. Lockhart D, Dong H, Byrne M, Follettie M, Gallo M, Chee M, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL: Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996, 14:1675.
3. Fisher RA: The Design of Experiments, 6th edn. Edinburgh: Oliver and Boyd Ltd. 1951.
4. 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 USA 2000, 97:9834-9839.
5. Kerr MK, Martin M, Churchill GA: Analysis of variance for gene expression microarray data. J Comput Biol 2001, 7: 19-837.
6. Kerr MK, Churchill GA: Statistical design and the analysis of gene expression microarray data. Genet Res 2001, 77: 23-128.
7. Huang X, Pan W: Comparing three methods for variance estimation with duplicated high density oligonucleotide arrays. Funct Integr Genomics 2002, 2:126-133.
8. Šášik R, Calvo E, Corveil J: Statistical analysis of high-density oligonucleotide arrays: a multiplicative noise model. Bioinformatics 2002, 18:1633-1640.
9. Chu TM, Weir B, Wolfinger R: A systematic statistical linear modeling approach to oligonucleotide array experiments. Math Biosci 2002, 176:35-51.
10. Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS: Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol, 2001, 8:625-637.
11. Wernisch L, Kendall SL, Soneji S, Wietzorrek A, Parish T, Hinds J, Butcher PD, Stocker NG: Analysis of whole-genome microarray replicates using mixed models. Bioinformatics 2003, 19: 3-61.
12. Cui X, Churchill GA: Statistical tests for differential expression in cDNA microarray experiments. Genome Bio 2003, 4:210.
13. Duncan DB: Multiple range and multiple F tests. Biometrics 1955, 11:1.
14. Westfall PH, Young SS: p-value adjustment for multiple tests in multivariate binomial models. J Am Stat Assoc 1989, 84:780-786.
15. Shaffer JP: Multiple hypothesis testing. Annu Rev Psychol 1995, 46:561-724.
16. Hsu JC: Multiple comparisons. London: Chapman and Hall. 1996.
17. Dudoit S, Yang YH, Callow MJ, Speed TP: Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sinica 2002, 12:111-139.
18. Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. JRoy Stat Soc 1995, B:289-300.
19. Yekutieli D, Benjamini Y: Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. J Stat Plan Infer 1999, 82:171-196.
20. Abramovich F, Benjamini Y, Donoho D, Johnstone I: Adapting to unknown sparsity by controlling the false discovery rate. Technical Report No 2000-19 Department of Statistics, Stanford University 2000.
21. Benjamini Y, Yekutieli D: The control of the false discovery rate under dependency. Ann Stat 2001, 29: 165-1188.
22. Efron B, Tibshirani R, Storey JD, Tusher V: Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 2001, 96:1151-1160.
23. Storey JD: The positive false discovery rate: A Bayesian interpretation and the Q-Value. Technical Report 2001-12. Department of Statistics, Stanford University. 2001.
24. Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001, 98:5116-5121.
25. Sabatti C, Karsten SL, Geschwind DH: Thresholding rules for recovering a sparse signal from microarray experiments. Math Biosci 2002, 176:17-34.
26. Murphy C, Gilmore MM, Seery CS, Salmon DP, Lasker BR: Olfactory thresholds are associated with degree of dementia in Alzheimer's disease. Neurobiol Aging 1990, 11:465-469.
27. Cataldo AM, Hamilton DJ, Barnett JL, Paskevich PA, Nixon RA: Properties of the endosomal-lysosomal system in the human central nervous system: Disturbances mark most neurons in populations at risk to degenerate in Alzheimer's disease. JNeurosci 1996, 16: 86-199.
28. McLachlan DR, Lukiw WJWL, Bergeron C, Bech-Hansen NT: Selective messenger RNA reduction in Alzheimer's disease. Brain Res 1988, 427:255-261.
29. Tissir F, Goffinet AM: Reelin and brain development. Nature Rev Neurosci 2003, 4:496-505.
30. Reiss CS, Plakhov IV, Komatsu T: Viral replication in olfactory receptor neurons and entry into the olfactory bulb and brain. Ann N Y Acad Sci 1998, 855:751-761.
31. Schwob JE, Saha S, Youngentob SL, Jubelt B: Intranasal inoculation with the olfactory bulb line variant of mouse hepatitis virus causes extensive destruction of the olfactory bulb and accelerated turnover of neurons in the olfactory epithelium of mice. Chem Senses 2001, 26:937-952.
32. AJ Hayter AJ: The maximum familywise error rate of Fisher's least significant difference test. J Am Stat Assoc 1986, 81:1000-1004.
33. Getchell ML, Shah DS, Buch SK, Davis DG, Getchell TV: 3-Nitrotyrosine immunoreactivity in olfactory receptor neurons of patients with Alzheimer's disease: implication for impaired odor sensitivity. Aging 2003, 24:663-673.
34. Affymetrix: Statistical algorithms reference guide,. Technical report, Affymetrix 2001.
35. Li C, Wong WH: Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error applications. Genome Biol 2001, 2:1-11.
36. Schadt E, Li C, Eliss B, Wong WH: Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. J Cell Biochem 2002, 84:120-125.
37. Bolstad BM, Irizarry RA, Åstrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19:185-193.
38. Littell RC, Milliken GA, Stroup WW, Wolfinger RD: SAS system for mixed models, SAS institute Inc. 1996.
39. Scheffé H: The analysis of variance. Wiley classics library edition pub. Pp66. John Wiley & Sons, INC. 1999.
40. Kenward MG, Roger JH: Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 1997, 53:983-997.
41. Keselman HJ, Algina J, Kowalchuk RK, Wolfinger RD: A comparison of two approaches for selecting covariance structures in the anlaysis of repeated measurements. Comm Stat-SimComp 1998, 27:591-604.
42. Boik RJ: Interaction, partial interactions, and interaction contrasts in the analysis of variance. Psych Bulletin 1979, 68:1084-1089.
43. Hochberg Y, Tamhane AC: Multiple comparison procedures. New York: Wiley 1987.
44. Lix LM, Keselman HJ: Interaction contrasts in repeated measures designs. British J Math Stat Psych 1996, 49: 47-162.