Three methods for optimization of cross-laboratory and cross-platform microarray expression data

Nucleic Acids Research

Volumn 35, Issue 10, 2007, Pages

  Stafford, Phillip ^a   Brun, Marcel ^b  

^a ARIZONA STATE UNIVERSITY   (United States)

^b TRANSLATIONAL GENOMICS RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS OF VARIANCE; ANALYTIC METHOD; ANALYTICAL ERROR; ARTICLE; CALIBRATION; CONTROLLED STUDY; CORRELATION ANALYSIS; GENE EXPRESSION PROFILING; HUMAN; HUMAN TISSUE; INTERMETHOD COMPARISON; LABORATORY TEST; MICROARRAY ANALYSIS; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SENSITIVITY ANALYSIS; STATISTICAL PARAMETERS; DNA MICROARRAY; EVALUATION; LIVER; LUNG; METABOLISM; METHODOLOGY; QUALITY CONTROL; SPLEEN; STANDARD; STATISTICAL DISTRIBUTION;

CALIBRATION; GENE EXPRESSION PROFILING; HUMANS; LIVER; LUNG; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; QUALITY CONTROL; SPLEEN; STATISTICAL DISTRIBUTIONS;

EID: 34250872205     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkl1133     Document Type: Article

References (67)

1. Cobleigh,M.A., Tabesh,B., Bitterman,P., Baker,J., Cronin,M., Liu,M.L., Borchik,R., Mosquera,J.M., Walker,M.G. et al. (2005) Tumor gene expression and prognosis in breast cancer patients with 10 or more positive lymph nodes. Clin. Cancer Res., 11, 8623-8631.
2. Weigelt,B., Hu,Z., He,X., Livasy,C., Carey,L.A., Ewend,M.G., Glas,A.M., Perou,C.M. and van t Veer,L.J. (2005) Molecular portraits and 70-gene prognosis signature are preserved throughout the metastatic process of breast cancer. Cancer Res., 65, 9155-9158.
3. van't Veer,L.J., Dai,H., van de Vijver,M.J., He,Y.D., Hart,A.A., Mao,M., Peterse,H.L., van der Kooy,K., Marton,M.J. et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530-536.
4. van de Vijver,M.J., He,Y.D., van't Veer,L.J., Dai,H., Hart,A.A., Voskuil,D.W., Schreiber,G.J., Peterse,J.L., Roberts,C. et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med., 347, 1999-2009.
5. Kuo,W.P., Jennssen,T.-K., Butte,A.J., Ohno-Machado,L. and Kohane,I.S. (2002) Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics, 18, 405-412.
6. Shippy,R., Sendera,T.J., Lockner,R., Palaniappan,C., Kaysser-Kranich,T., Watts,G. and Alsobrook,J. (2004) Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations. BMC Genomics, 5, doi:10.1186/ 1471-2164-1185-1161.
7. Yauk,C.L., Berndt,M.L., Williams,A. and Douglas,G.R. (2004) Comprehensive comparison of six microarray technologies. Nucleic Acids Res., 32, doi:10.1093.
8. Park,P.J., Cao,Y.A., Lee,S.Y., Kim,J.-W., Chang,M.S., Hart,R. and Choi,S. (2004) Current issues for DNA microarrays: Platform comparison, double linear amplification, and universal RNA reference. J. Biotechnol., 112, 225-245.
9. Parmigiani,G., Garrett,E., Anbazhagan,R. and Gabrielson,E. (2004) Molecular classification of lung cancer: A cross-platform comparison of gene expression data sets. Chest, 125, 103S.
10. Parmigiani,G., Garrett-Mayer,E.S., Anbazhagan,R. and Gabrielson,E. (2004) A cross-study comparison of gene expression studies for the molecular classification of lung cancer. Clin. Cancer Res., 10 2922-2927.
11. Pan,F., Ma,X. and Zhou, X.J. (2006) Integrative differential gene expression analysis for cross-platform microarray datasets. J. Biomed. Inform., 22, 1665-1667.
12. Kuo,W.P., Liu,F., Trimarchi,J., Punzo,C., Lombardi,M., Sarang,J., Whipple,M.E., Maysuria,M., Serikawa,K. et al. (2006) A sequence oriented comparison of gene expression measurements across different hybridization-based technologies. Nat. Biotechnol., 24, 832-840.
13. Jong,K., Marchiori,E., van der Vaart,A., Chin,S.F., Carvalho,B., Tijssen,M., Eijk,P.P., van den Ijssel,P., Grabsch,H. et al. (2006) Cross-platform array comparative genomic hybridization metaanalysis separates hematopoietic and mesenchymal from epithelial tumors. Oncogene., doi: 10.1038/sj.onc.1209919.
14. Guo,L., Lobenhofer,E.K., Wang,C., Shippy,R., Harris,S.C., Zhang,L., Mei,N., Chen,T., Herman,D. et al. (2006) Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat. Biotechnol., 24, 1162-1169.
15. Warnat,P., Eils,R. and Brors,B. (2005) Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes. BMC Bioinformatics, 6, 265.
16. Shi,L., Tong,W., Fang,H., Scherf,U., Han,J., Puri,R.K., Frueh,F.W., Goodsaid,F.M., Guo,L. et al. (2005) Cross-platform comparability of microarray technology: Intra-platform consistency and appropriate data analysis procedures are essential. BMC Bioinformatics, 6 Suppl 2, S12.
17. Schlingemann,J., Habtemichael,N., Ittrich,C., Toedt,G., Kramer,H., Hambek,M., Knecht,R., Lichter,P., Stauber,R. et al. (2005) Patient-based cross-platform comparison of oligonucleotide microarray expression profiles. Lab. Invest., 85, 1024-1039.
18. Mecham,B.H., Klus,G.T., Strovel,J., Augustus,M., Byrne,D., Bozso,P., Wetmore,D.Z., Mariani,T.J., Kohane,I.S. et al. (2004) Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements. Nucleic Acids Res., 32, e74.
19. Hardiman,G. (2004) Microarray platforms-comparisons and contrasts. Pharmacogenomics, 5, 487-502.
20. Carter,S.L., Eklund,A.C., Mecham,B.H., Kohane,I.S. and Szallasi,Z. (2005) Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements. BMC Bioinformatics 6, 107.
21. Canales,R.D., Luo,Y., Willey,J.C., Austermiller,B., Barbacioru,C.C., Boysen,C., Hunkapiller,K., Jensen,R.V., Knight,C.R. et al. (2006) Evaluation of DNA microarray results with quantitative gene expression platforms. Nat. Biotechnol., 24, 1115-1122.
22. Larkin,J.E., Frank,B.C., Gavras,H., Sultana,R. and Quackenbush,J. (2005) Independence and reproducibility across microarray platforms. Nat. Methods, 2, 337-343.
23. Shi,L., Reid,L.H., Jones,W.D., Shippy,R., Warrington,J.A., Baker,S.C., Collins,P.J., de Longueville,F., Kawasaki,E.S. et al. (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol., 24, 1151-1161.
24. Tong,W., Lucas,A.B., Shippy,R., Fan,X., Fang,H., Hong,H., Orr,M.S., Chu,T.M., Guo,X. et al. (2006) Evaluation of external RNA controls for the assessment of microarray performance. Nat. Biotechnol., 24, 1132-1139.
25. Model,F., Konig,T., Piepenbrock,C. and Adorjan,P. (2002) Statistical process control for large scale microarray experiments. Bioinformatics, 18, 155S-163S.
26. Adolphson,A., Keighley,T., Field,J., Fisher,N., Israel,S., Phatak,A., Saunders,I., Sparks,R. and Veevers,A. (2002). Commonwealth Scientific and Industrial Research Organization, Mathematical and Information Sciences, Australia.
27. Crosier,R.B. (1986) A new two-sided cumulative sum quality control scheme. Technometrics, 28, 187-194.
28. Li,C. and Wong,W.H. (2001) Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Proc. Nat. Acad. Sci. U.S.A., 98, 31-36.
29. Wu,Z., Irizarry,R.A., Gentleman,R., Murillo,F.M. and Spencer,F. (2004) A model based background adjustment for oligonucleotide expression arrays. Johns Hopkins University, Dept. of Biostatistics Working Papers, Working Paper 1.
30. Irizarry,R.A., Warren,D., Spencer,F., Kim,I.F., Biswal,S., Frank,B.C., Gabrielson,E., Garcia,J.G., Geoghegan,J. et al. (2005) Multiple-laboratory comparison of microarray platforms. Nat. Methods 2, 329-330.
31. Irizarry,R.A., Hobbs,B., Collin,F., Beazer-Barclay,Y.D., Antonellis,K.J., Scherf,U. and Speed,T.P. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4, 249-264.
32. Irizarry,R.A., Bolstad,B.M., Collin,F., Cope,L.M., Hobbs,B. and Speed,T.P. (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res., 31, e15.
33. Irizarry,R.A. and Wu,Z. (2006). Affycomp ver. 1.4.3. Bioconductor package for R.
34. Xiao,J., Lucas,A.B., D'Andrade,P.N., Visitacion,M., Tangvoranuntakul,P. and Fulmer-Smentek,S.B. (2006) Performance of the Agilent microarray platform for one-color analysis of gene expression. Agilent Technical Note. www.chem.agilent.com/temp/Rad607C5/00057115.pdf.
35. Hornberger,J., Cosler,L.E. and Lyman,G.H. (2005) An economic analysis of targeting chemotherapy using a 21-gene RT-PCR assay Page 15 of 16 Nucleic Acids Research, 2007, Vol. 35, No. 10 e72 in lymph-node-negative, estrogen-receptor-positive, early-stage breast cancer. Am. J. Manag. Care, 11, 313-324.
36. Ahr,A., Holtrich,U. and Solback,C. (2001) Molecular classification of breast cancer patients by gene expression profiling. J. Pathol., 195, 312-320.
37. Ahr,A., Kam,T. and Solback,C. (2002) Identification of high risk breast-cancer patients by gene expression profiling. Lancet, 359, 131-132.
38. Detours,V., Durmont,J.E., Bersini,H. and Maenhaur,C. (2003) Integration and cross-validation of high-throughput gene expression data: Comparing heterogenous data sets. FEBS Lett., 546, 98-102.
39. Griffith,O.L., Pleasance,E.D., Fulton,D.L., Oveisi,H., Ester,M., Siddiqui,A.S. and Jones,S.J.M. (2005) Assessment and integration of publicly available SAGE, cDNA microarray, and oligonucleotide microarray expression data for global coexpression analyses. Genomics, 86 476-488.
40. Ghosh,D., Barette,T.R., Rhodes,D.R. and Chinnaiyan,A.M. (2004) Statistical issues and methods for meta-analysis of microarray data: A case study in prostate cancer. Funct. Integr. Genomics, 3, 180-188.
41. Shyamsundar,R., Kim,Y.H., Higgins,J.P., Montgomery,K., Jorden,M., Sethuraman,A., van de Rijn,M., Botstein,D., Brown,P.O. et al. (2005) A DNA microarray survey of gene expression in normal human tissues. Genome Biol., 6, r22.21-r22.29.
42. Lee,Y. and Lee,C.K. (2003) Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics, 19, 1132-1139.
43. Culhane,A.C., Perriere,G., Considine,E.C., Cotter,T.G. and Higgins,D.G. (2002) Between-group analysis of microarray data. Bioinformatics, 18, 1600-1608.
44. Cho,J.-H., Lee,D., Park,J.H., Kim,K. and Lee,I.-B. (2002) Optimal approach for classification of acute leukemia subtypes based on gene expression data. Biotechnol. Prog., 18, 847-854.
45. Hvidsten,T.R., Laegreid,A. and Komorowski,J. (2003) Learning rule-based models of biological process from gene expression time profiles using gene ontology. Bioinformatics, 19, 1116-1123.
46. He,Y.D., Dai,H., Schadt,E.E., Cavet,G., Edwards,S.W., Stepaniants,S.B., Duenwald,S., Kleinhanz,R., Jones,A.R. et al. (2003) Microarray standard data set and figures of merit for comparing data processing methods and experiment designs. Bioinformatics, 19, 956-965.
47. Rise,M.L., Douglas,S.E., Sakhrani,D., Williams,J., Ewart,K.V., Rise,M., Davidson,W.S., Koop,B.F. and Devlin,R.H. (2006) Multiple microarray platforms utilized for hepatic gene expression profiling of GH transgenic coho salmon with and without ration restriction. J. Mol. Endocrinol., 37, 259-282.
48. Edelman,E., Porrello,A., Guinney,J., Balakumaran,B., Bild,A., Febbo,P.G. and Mukherjee,S. (2006) Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles. Bioinformatics, 22, e108-e116.
49. Chudin,E., Kruglyak,S., Baker,S.C., Oeser,S., Barker,D. and McDaniel,T.K. (2006) A model of technical variation of microarray signals. J. Comput. Biol., 13, 996-1003.
50. Xu,L., Tan,A.C., Naiman,D.Q., Geman,D. and Winslow,R.L. (2005) Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data. Bioinformatics, 21, 3905-3911.
51. Culhane,A.C., Perriere,G. and Higgins,D.G. (2003) Cross-platform comparison and visualisation of gene expression data using co-inertia analysis. BMC Bioinformatics, 4, 59.
52. Bolstad,B.M., Irizarry,R.A., Astrand,M. and Speed,T.P. (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19, 185-193.
53. Rhodes,D.R., Yu,J., Shanker,K., Deshpande,N., Varambally,R., Ghosh,D., Barrette,T., Pandey,A. and Chinnaiyan,A.M. (2004) Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc. Nat. Acad. Sci. U.S.A., 101, 9309-9314.
54. Li,J., Spletter,M.L. and Johnson,J.A. (2005) Dissecting tBHQ induced ARE-driven gene expression through long and short oligonucleotide arrays. J. Physiol. Genom., 21, 43-58.
55. Bloom,G., Yang,I.V., Boulware,D., Kwong,K.Y., Coppola,D., Eschrich,S., Quackenbush,J. and Yeatman,T.J. (2004) Multiplatform, multi-site, microarray-based human tumor classification. Am. J. Pathol., 164.
56. Draghici,S. (2006) vortex.cs.wayne.edu/Projects.htm#Onto-Express.
57. Parrish,R.S. and Spencer,H.J. (2004) Effect of normalization on significance testing for oligonucleotide microarrays. J. Biopharm. Stat., 14, 575-589.
58. Sima,C. and Dougherty,E.R. (2006) What should be expected from feature selection in small-sample settings. Bioinformatics, 22, 2430-2436.
59. Xiong,M., Fang,X. and Zhao,J. (2001) Biomarker identification by feature wrappers. Genome Res., 11, 1878-1887.
60. Hua,J., Xiong,Z., Lowey,J., Suh,E. and Dougherty,E.R. (2005) Optimal number of features as a function of sample size for various classification rules. Bioinformatics, 21, 1509-1515.
61. Dobbin,K., Kawasaki,E., Petersen,D. and Simon,R. (2005) Characterizing dye bias in microarray experiments. Bioinformatics, 21, 2430-2437.
62. Bittner,M.L., Meltzer,M.R., Chen,Y., Jiang,Y., Seftor,E., Hendrix,M., Radmacher,M., Simon,R., Yakhini,Z. et al. (2000) Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature, 406, 536-540.
63. Wolber,P.K., Shannon,K.W., Fulmer-Smentek,S.B., Collins,P.J., Lenkov,K., Troup,C.D., Connell,S.D., Ghosh,S., D'Andrade,P.N. et al. (2002) Robust local normalization of gene expression microarray data. Agilent Technical Note, 1-4.
64. Fan,W., Pritchard,J.I., Olsen,J.M., Khalid,N. and Zhao,L.P. (2005) A class of models for analyzing GeneChip gene expression analysis array data. BMC Genomics, 6, doi:10.1186/1471-2164-1186-1116.
65. de Reynies,A., Geromin,D., Cayuela,J.-M., Petel,F., Dessen,P., Sigaux,F. and Rickman,D.S. (2006) Comparison of the latest commercial short and long oligonucleotide microarray technologies. BMC Genomics, 7 doi:10.1186/1471-2164-1187-1151.
66. Ben-Dor,A., Bruhn,L., Friedman,N., Nachman,I., Schummer,M. and Yakhini,Z. (2000) Tissue classification with gene expression profiles. J. Comput. Biol., 7, 559-583.
67. Dobbin,K. and Simon,R. (2002) Comparison of microarray designs for class comparison and class discovery. Bioinformatics, 18, 1438-1445.