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Microarray Meta-Analysis: From Data to Expression to Biological Relationships

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Computational Medicine

Abstract

Since the introduction of microarray technology, it has become the workhorse for mRNA expression profiling. Its application ranges from investigating gene function, regulation, and co-expression, to clinical use in diagnosis and prognosis. Over the last decade, a large number of microarray experiments have become available in public repositories often addressing similar or related hypotheses. The large compendia of gene expression data provide the opportunity to conduct meta-analyses by combining data from various independent but related studies. Such data integration has the potential to enhance the reliability and generalizability of the results of individual microarray studies.

This chapter describes the meta-analysis process including objectives, data collection, annotation, analysis methods, and visualizations. For each step we present a selection of tools available and discuss associated problems and difficulties.

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References

  • Adams MD, Kelley JM, Gocayne JD, Dubnick M, Polymeropoulos MH, Xiao H, Merril CR, Wu A, Olde B, Moreno RF et al (1991) Complementary DNA sequencing: expressed sequence tags and human genome project. Science 252(5013):1651–1656

    Article  PubMed  CAS  Google Scholar 

  • Alibes A, Yankilevich P, Canada A, Diaz-Uriarte R (2007) IDconverter and IDClight: conversion and annotation of gene and protein IDs. BMC Bioinformatics 8:9

    Article  PubMed  CAS  Google Scholar 

  • Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J Jr, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769):503–511

    Article  PubMed  CAS  Google Scholar 

  • Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410

    PubMed  CAS  Google Scholar 

  • Anders M, Fehlker M, Wang Q, Wissmann C, Pilarsky C, Kemmner W, Hocker M (2011) Microarray meta-analysis defines global angiogenesis-related gene expression signatures in human carcinomas. Mol Carcinog (Epub ahead of print)

    Google Scholar 

  • Arasappan D, Tong W, Mummaneni P, Fang H, Amur S (2011) Meta-analysis of microarray data using a pathway-based approach identifies a 37-gene expression signature for systemic lupus erythematosus in human peripheral blood mononuclear cells. BMC Med 9:65

    Article  PubMed  CAS  Google Scholar 

  • Ball CA, Brazma A, Causton H, Chervitz S, Edgar R, Hingamp P, Matese JC, Parkinson H, Quackenbush J, Ringwald M, Sansone SA, Sherlock G, Spellman P, Stoeckert C, Tateno Y, Taylor R, White J, Winegarden N (2004) Submission of microarray data to public repositories. PLoS Biol 2(9):E317

    Article  PubMed  CAS  Google Scholar 

  • Baron D, Bihouee A, Teusan R, Dubois E, Savagner F, Steenman M, Houlgatte R, Ramstein G (2011) MADGene: retrieval and processing of gene identifier lists for the analysis of heterogeneous microarray datasets. Bioinformatics 27(5):725–726

    Article  PubMed  CAS  Google Scholar 

  • Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Holko M, Ayanbule O, Yefanov A, Soboleva A (2011) NCBI GEO: archive for functional genomics data sets–10 years on. Nucleic Acids Res 39(Database issue):D1005–D1010

    Article  PubMed  Google Scholar 

  • Bisognin A, Coppe A, Ferrari F, Risso D, Romualdi C, Bicciato S, Bortoluzzi S (2009) A-MADMAN: annotation-based microarray data meta-analysis tool. BMC Bioinformatics 10:201

    Article  PubMed  CAS  Google Scholar 

  • Blanchard AP, Kaiser RJ, Hood LE (1996) High-density oligonucleotide arrays. Biosens Bioelectron 11(6/7):687–690

    Article  CAS  Google Scholar 

  • Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2):185–193

    Article  PubMed  CAS  Google Scholar 

  • Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29(4):365–371

    Article  PubMed  CAS  Google Scholar 

  • Breitling R, Armengaud P, Amtmann A, Herzyk P (2004) Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett 573(1–3):83–92

    Article  PubMed  CAS  Google Scholar 

  • Brors B (2005) Microarray annotation and biological information on function. Methods Inf Med 44(3):468–472

    PubMed  CAS  Google Scholar 

  • Bullinger L, Dohner K, Bair E, Frohling S, Schlenk RF, Tibshirani R, Dohner H, Pollack JR (2004) Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med 350(16):1605–1616

    Article  PubMed  CAS  Google Scholar 

  • Burgess JK (2001) Gene expression studies using microarrays. Clin Exp Pharmacol Physiol 28(4):321–328

    Article  PubMed  CAS  Google Scholar 

  • Bushman BJ (1994) Vote-counting procedures in meta-analysis. In: Cooper H, Hedges LV (eds) The handbook of research synthesis, vol 236, 1st edn. Russell Sage, New York, pp 193–213

    Google Scholar 

  • Cahan P, Rovegno F, Mooney D, Newman JC, St Laurent G 3rd, McCaffrey TA (2007) Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization. Gene 401(1–2):12–18

    Article  PubMed  CAS  Google Scholar 

  • Callow MJ, Dudoit S, Gong EL, Speed TP, Rubin EM (2000) Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. Genome Res 10(12):2022–2029

    Article  PubMed  CAS  Google Scholar 

  • Campain A, Yang YH (2010) Comparison study of microarray meta-analysis methods. BMC Bioinformatics 11:408

    Article  PubMed  CAS  Google Scholar 

  • Chang CW, Cheng WC, Chen CR, Shu WY, Tsai ML, Huang CL, Hsu IC (2011) Identification of human housekeeping genes and tissue-selective genes by microarray meta-analysis. PLoS One 6(7):e22859

    Article  PubMed  CAS  Google Scholar 

  • Cheng WC, Tsai ML, Chang CW, Huang CL, Chen CR, Shu WY, Lee YS, Wang TH, Hong JH, Li CY, Hsu IC (2010) Microarray meta-analysis database (M(2)DB): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database. BMC Bioinformatics 11:421

    Article  PubMed  CAS  Google Scholar 

  • Cheng WC, Chang CW, Chen CR, Tsai ML, Shu WY, Li CY, Hsu IC (2011) Identification of reference genes across physiological states for qRT-PCR through microarray meta-analysis. PLoS One 6(2):e17347

    Article  PubMed  CAS  Google Scholar 

  • Cheung VG, Morley M, Aguilar F, Massimi A, Kucherlapati R, Childs G (1999) Making and reading microarrays. Nat Genet 21(Suppl 1):15–19

    Article  PubMed  CAS  Google Scholar 

  • Choi JK, Yu U, Kim S, Yoo OJ (2003) Combining multiple microarray studies and modeling interstudy variation. Bioinformatics 19(Suppl 1):i84–i90

    Article  PubMed  Google Scholar 

  • Choi H, Shen R, Chinnaiyan AM, Ghosh D (2007) A latent variable approach for meta-analysis of gene expression data from multiple microarray experiments. BMC Bioinformatics 8:364

    Article  PubMed  CAS  Google Scholar 

  • Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum Associates, Hillsdale

    Google Scholar 

  • Cui X, Churchill GA (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome Biol 4(4):210

    Article  PubMed  Google Scholar 

  • Dan S, Tsunoda T, Kitahara O, Yanagawa R, Zembutsu H, Katagiri T, Yamazaki K, Nakamura Y, Yamori T (2002) An integrated database of chemosensitivity to 55 anticancer drugs and gene expression profiles of 39 human cancer cell lines. Cancer Res 62(4):1139–1147

    PubMed  CAS  Google Scholar 

  • Daves MH, Hilsenbeck SG, Lau CC, Man TK (2011) Meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors. BMC Med Genomics 4:56

    Article  PubMed  Google Scholar 

  • Davis S, Meltzer PS (2007) GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 23(14):1846–1847

    Article  PubMed  CAS  Google Scholar 

  • de Leon J, Susce MT, Murray-Carmichael E (2006) The AmpliChip CYP450 genotyping test: Integrating a new clinical tool. Mol Diagn Ther 10(3):135–151

    Article  PubMed  Google Scholar 

  • DeConde RP, Hawley S, Falcon S, Clegg N, Knudsen B, Etzioni R (2006) Combining results of microarray experiments: a rank aggregation approach. Stat Appl Genet Mol Biol 5(1), Article 15

    Google Scholar 

  • Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4(5):P3

    Article  PubMed  Google Scholar 

  • DeRisi JL, Iyer VR, Brown PO (1997) Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278(5338):680–686

    Article  PubMed  CAS  Google Scholar 

  • DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7(3):177–188

    Article  PubMed  CAS  Google Scholar 

  • R Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/

  • Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM (2001) Delineation of prognostic biomarkers in prostate cancer. Nature 412(6849):822–826

    Article  PubMed  CAS  Google Scholar 

  • Diehn M, Sherlock G, Binkley G, Jin H, Matese JC, Hernandez-Boussard T, Rees CA, Cherry JM, Botstein D, Brown PO, Alizadeh AA (2003) SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Res 31(1):219–223

    Article  PubMed  CAS  Google Scholar 

  • Dudoit S, Yang YH, Callow MJ, Speed TP (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 12(1):111–139

    Google Scholar 

  • Dupuy A, Simon RM (2007) Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 99(2):147–157

    Article  PubMed  Google Scholar 

  • Dwork C, Kumar R, Naor M, Sivakumar D (2001) Rank aggregation methods for the Web. In: Shen VY, Saito N, Lyu MR, Zurko ME (eds) The tenth international world wide web conference, Hong Kong, 1–5 May 2001, pp 613–622

    Google Scholar 

  • Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95(25):14863–14868

    Article  PubMed  CAS  Google Scholar 

  • Fagin R, Kumar R, Sivakumar D (2003) Comparing top k lists. SIAM J Discr Math 17(1):134

    Article  Google Scholar 

  • Faith JJ, Driscoll ME, Fusaro VA, Cosgrove EJ, Hayete B, Juhn FS, Schneider SJ, Gardner TS (2008) Many microbe microarrays database: uniformly normalized affymetrix compendia with structured experimental metadata. Nucleic Acids Res 36(Database issue):D866–D870

    PubMed  CAS  Google Scholar 

  • Fisher RA (1932) Statistical methods for research workers, 4th edn. Oliver & Boyd, Edinburgh

    Google Scholar 

  • Furlong EE, Andersen EC, Null B, White KP, Scott MP (2001) Patterns of gene expression during Drosophila mesoderm development. Science 293(5535):1629–1633

    Article  PubMed  CAS  Google Scholar 

  • Gagarin D, Yang Z, Butler J, Wimmer M, Du B, Cahan P, McCaffrey TA (2005) Genomic profiling of acquired resistance to apoptosis in cells derived from human atherosclerotic lesions: potential role of STATs, cyclinD1, BAD, and Bcl-XL. J Mol Cell Cardiol 39(3):453–465

    Article  PubMed  CAS  Google Scholar 

  • Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 11(12):4241–4257

    PubMed  CAS  Google Scholar 

  • Gautier L, Cope L, Bolstad BM, Irizarry RA (2004a) Affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20(3):307–315

    Article  PubMed  CAS  Google Scholar 

  • Gautier L, Moller M, Friis-Hansen L, Knudsen S (2004b) Alternative mapping of probes to genes for Affymetrix chips. BMC Bioinformatics 5:111

    Article  PubMed  CAS  Google Scholar 

  • Gentleman R (2005) Bioinformatics and computational biology solutions using R and bioconductor. Springer, New York

    Book  Google Scholar 

  • Gershon D (2002) Microarray technology: an array of opportunities. Nature 416(6883):885–891

    Article  PubMed  CAS  Google Scholar 

  • Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–537

    Article  PubMed  CAS  Google Scholar 

  • Griffith OL, Melck A, Jones SJ, Wiseman SM (2006) Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers. J Clin Oncol 24(31):5043–5051

    Article  PubMed  CAS  Google Scholar 

  • Grutzmann R, Boriss H, Ammerpohl O, Luttges J, Kalthoff H, Schackert HK, Kloppel G, Saeger HD, Pilarsky C (2005) Meta-analysis of microarray data on pancreatic cancer defines a set of commonly dysregulated genes. Oncogene 24(32):5079–5088

    Article  PubMed  CAS  Google Scholar 

  • Guo L, Lobenhofer EK, Wang C, Shippy R, Harris SC, Zhang L, Mei N, Chen T, Herman D, Goodsaid FM, Hurban P, Phillips KL, Xu J, Deng X, Sun YA, Tong W, Dragan YP, Shi L (2006) Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol 24(9):1162–1169

    Article  PubMed  CAS  Google Scholar 

  • Harbig J, Sprinkle R, Enkemann SA (2005) A sequence-based identification of the genes detected by probesets on the Affymetrix U133 plus 2.0 array. Nucleic Acids Res 33(3):e31

    Article  PubMed  CAS  Google Scholar 

  • Hedges LV, Olkin I (1985) Statistical methods for meta-analysis. Academic, New York

    Google Scholar 

  • Hedges LV, Cooper H, Bushman BJ (1992) Testing the null hypothesis in meta-analysis: a comparison of combined probability and confidence interval procedures. Psychol Bull 111(1):188–194

    Article  Google Scholar 

  • Hong F, Breitling R (2008) A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinformatics 24(3):374–382

    Article  PubMed  CAS  Google Scholar 

  • Hong F, Breitling R, McEntee CW, Wittner BS, Nemhauser JL, Chory J (2006) RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis. Bioinformatics 22(22):2825–2827

    Article  PubMed  CAS  Google Scholar 

  • Hruz T, Laule O, Szabo G, Wessendorp F, Bleuler S, Oertle L, Widmayer P, Gruissem W, Zimmermann P (2008) Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes. Adv Bioinformatics 2008:420747

    PubMed  Google Scholar 

  • Hu P, Greenwood CMT, Beyene J (2006) Statistical methods for meta-analysis of microarray data: a comparative study. Inform Syst Front 8(1):9–20

    Article  Google Scholar 

  • Hubbell E, Liu WM, Mei R (2002) Robust estimators for expression analysis. Bioinformatics 18(12):1585–1592

    Article  PubMed  CAS  Google Scholar 

  • Hubble J, Demeter J, Jin H, Mao M, Nitzberg M, Reddy TB, Wymore F, Zachariah ZK, Sherlock G, Ball CA (2009) Implementation of GenePattern within the Stanford microarray database. Nucleic Acids Res 37(Database issue):D898–D901

    Article  PubMed  CAS  Google Scholar 

  • Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M (2002) Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18(Suppl 1):S96–S104

    Article  PubMed  Google Scholar 

  • Ikeo K, Ishi-i J, Tamura T, Gojobori T, Tateno Y (2003) CIBEX: center for information biology gene expression database. C R Biol 326(10–11):1079–1082

    Article  PubMed  CAS  Google Scholar 

  • Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2):249–264

    Article  PubMed  Google Scholar 

  • Irizarry RA, Warren D, Spencer F, Kim IF, Biswal S, Frank BC, Gabrielson E, Garcia JG, Geoghegan J, Germino G, Griffin C, Hilmer SC, Hoffman E, Jedlicka AE, Kawasaki E, Martinez-Murillo F, Morsberger L, Lee H, Petersen D, Quackenbush J, Scott A, Wilson M, Yang Y, Ye SQ, Yu W (2005) Multiple-laboratory comparison of microarray platforms. Nat Methods 2(5):345–350

    Article  PubMed  CAS  Google Scholar 

  • Ivanova NB, Dimos JT, Schaniel C, Hackney JA, Moore KA, Lemischka IR (2002) A stem cell molecular signature. Science 298(5593):601–604

    Article  PubMed  CAS  Google Scholar 

  • Ivliev AE, t Hoen PA, Villerius MP, den Dunnen JT, Brandt BW (2008) Microarray retriever: a web-based tool for searching and large scale retrieval of public microarray data. Nucleic Acids Res 36(Web Server issue):W327–W331

    Google Scholar 

  • Jafari P, Azuaje F (2006) An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors. BMC Med Inform Decis Mak 6:27

    Article  PubMed  Google Scholar 

  • Jelier R, t Hoen PA, Sterrenburg E, den Dunnen JT, van Ommen GJ, Kors JA, Mons B (2008) Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease. BMC Bioinformatics 9:291

    Google Scholar 

  • Kauffmann A, Rayner TF, Parkinson H, Kapushesky M, Lukk M, Brazma A, Huber W (2009) Importing ArrayExpress datasets into R/bioconductor. Bioinformatics 25(16):2092–2094

    Article  PubMed  CAS  Google Scholar 

  • Kendall MG (1938) A new measure of rank correlation. Biometrika 30(1–2):81–93

    Google Scholar 

  • Kennedy GC, Matsuzaki H, Dong S, Liu WM, Huang J, Liu G, Su X, Cao M, Chen W, Zhang J, Liu W, Yang G, Di X, Ryder T, He Z, Surti U, Phillips MS, Boyce-Jacino MT, Fodor SP, Jones KW (2003) Large-scale genotyping of complex DNA. Nat Biotechnol 21(10):1233–1237

    Article  PubMed  CAS  Google Scholar 

  • Kilpinen S, Autio R, Ojala K, Iljin K, Bucher E, Sara H, Pisto T, Saarela M, Skotheim RI, Bjorkman M, Mpindi JP, Haapa-Paananen S, Vainio P, Edgren H, Wolf M, Astola J, Nees M, Hautaniemi S, Kallioniemi O (2008) Systematic bioinformatic analysis of expression levels of 17,330 human genes across 9,783 samples from 175 types of healthy and pathological tissues. Genome Biol 9(9):R139

    Article  PubMed  CAS  Google Scholar 

  • Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA (2009) Circos: an information aesthetic for comparative genomics. Genome Res 19(9):1639–1645

    Article  PubMed  CAS  Google Scholar 

  • LaCroix-Fralish ML, Austin JS, Zheng FY, Levitin DJ, Mogil JS (2011) Patterns of pain: meta-analysis of microarray studies of pain. Pain 152(8):1888–1898

    Article  PubMed  Google Scholar 

  • Lalkhen AG, McCluskey A (2008) Statistics V: Introduction to clinical trials and systematic reviews. Continuing Education in Anaesthesia Critical Care Pain 8(4):143–146

    Google Scholar 

  • Larkin JE, Frank BC, Gavras H, Sultana R, Quackenbush J (2005) Independence and reproducibility across microarray platforms. Nat Methods 2(5):337–344

    Article  PubMed  CAS  Google Scholar 

  • Larsson O, Sandberg R (2006) Lack of correct data format and comparability limits future integrative microarray research. Nat Biotechnol 24(11):1322–1323

    Article  PubMed  CAS  Google Scholar 

  • Larsson O, Wennmalm K, Sandberg R (2006) Comparative microarray analysis. OMICS 10(3):381–397

    Article  PubMed  CAS  Google Scholar 

  • Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P (2004) Coexpression analysis of human genes across many microarray data sets. Genome Res 14(6):1085–1094

    Article  PubMed  CAS  Google Scholar 

  • Lee Y, Tsai J, Sunkara S, Karamycheva S, Pertea G, Sultana R, Antonescu V, Chan A, Cheung F, Quackenbush J (2005) The TIGR gene indices: clustering and assembling EST and known genes and integration with eukaryotic genomes. Nucleic Acids Res 33(Database issue):D71–D74

    Article  PubMed  CAS  Google Scholar 

  • Lewis S, Clarke M (2001) Forest plots: trying to see the wood and the trees. BMJ 322(7300):1479–1480

    Article  PubMed  CAS  Google Scholar 

  • Li C, Hung Wong W (2001) Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biol 2(8), RESEARCH0032

    Google Scholar 

  • Li C, Wong WH (2001) Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA 98(1):31–36

    Article  PubMed  CAS  Google Scholar 

  • Li X, Quigg RJ, Zhou J, Gu W, Nagesh Rao P, Reed EF (2008) Clinical utility of microarrays: current status, existing challenges and future outlook. Curr Genomics 9(7):466–474

    Article  PubMed  CAS  Google Scholar 

  • Lipshutz RJ, Fodor SP, Gingeras TR, Lockhart DJ (1999) High density synthetic oligonucleotide arrays. Nat Genet 21(Suppl 1):20–24

    Article  PubMed  CAS  Google Scholar 

  • Lo K, Gottardo R (2007) Flexible empirical Bayes models for differential gene expression. Bioinformatics 23(3):328–335

    Article  PubMed  CAS  Google Scholar 

  • Loughin T (2004) A systematic comparison of methods for combining p-values from independent tests. Comput Stat Data Anal 47(3):467–485

    Article  Google Scholar 

  • Lu T, Pan Y, Kao SY, Li C, Kohane I, Chan J, Yankner BA (2004) Gene regulation and DNA damage in the ageing human brain. Nature 429(6994):883–891

    Article  PubMed  CAS  Google Scholar 

  • Lusa L, Gentleman RC, Ruschhaupt M (2006) GeneMeta: metaanalysis for high throughput experiments. http://www.bioconductor.org/packages/2.8/bioc/html/GeneMeta.html

  • Marot G, Foulley JL, Mayer CD, Jaffrezic F (2009) Moderated effect size and P-value combinations for microarray meta-analyses. Bioinformatics 25(20):2692–2699

    Article  PubMed  CAS  Google Scholar 

  • Marshall E (2004) Getting the noise out of gene arrays. Science 306(5696):630–631

    Article  PubMed  CAS  Google Scholar 

  • McDonald MJ, Rosbash M (2001) Microarray analysis and organization of circadian gene expression in Drosophila. Cell 107(5):567–578

    Article  PubMed  CAS  Google Scholar 

  • Michiels S, Koscielny S, Hill C (2005) Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 365(9458):488–492

    Article  PubMed  CAS  Google Scholar 

  • Moreau Y, Aerts S, De Moor B, De Strooper B, Dabrowski M (2003) Comparison and meta-analysis of microarray data: from the bench to the computer desk. Trends Genet 19(10):570–577

    Article  PubMed  CAS  Google Scholar 

  • Nadon R, Shoemaker J (2002) Statistical issues with microarrays: processing and analysis. Trends Genet 18(5):265–271

    Article  PubMed  CAS  Google Scholar 

  • Newman JC, Weiner AM (2005) L2L: a simple tool for discovering the hidden significance in microarray expression data. Genome Biol 6(9):R81

    Article  PubMed  CAS  Google Scholar 

  • Normand SL (1999) Meta-analysis: formulating, evaluating, combining, and reporting. Stat Med 18(3):321–359

    Article  PubMed  CAS  Google Scholar 

  • Ntzani EE, Ioannidis JP (2003) Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment. Lancet 362(9394):1439–1444

    Article  PubMed  CAS  Google Scholar 

  • Ondov BD, Bergman NH, Phillippy AM (2011) Interactive metagenomic visualization in a Web browser. BMC Bioinformatics 12:385

    Article  PubMed  Google Scholar 

  • Owzar K, Barry WT, Jung SH, Sohn I, George SL (2008) Statistical challenges in preprocessing in microarray experiments in cancer. Clin Cancer Res 14(19):5959–5966

    Article  PubMed  Google Scholar 

  • Pan Q, Shai O, Misquitta C, Zhang W, Saltzman AL, Mohammad N, Babak T, Siu H, Hughes TR, Morris QD, Frey BJ, Blencowe BJ (2004) Revealing global regulatory features of mammalian alternative splicing using a quantitative microarray platform. Mol Cell 16(6):929–941

    Article  PubMed  CAS  Google Scholar 

  • Parkinson H, Kapushesky M, Kolesnikov N, Rustici G, Shojatalab M, Abeygunawardena N, Berube H, Dylag M, Emam I, Farne A, Holloway E, Lukk M, Malone J, Mani R, Pilicheva E, Rayner TF, Rezwan F, Sharma A, Williams E, Bradley XZ, Adamusiak T, Brandizi M, Burdett T, Coulson R, Krestyaninova M, Kurnosov P, Maguire E, Neogi SG, Rocca-Serra P, Sansone SA, Sklyar N, Zhao M, Sarkans U, Brazma A (2009) ArrayExpress update – from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res 37(Database issue):D868–D872

    Article  PubMed  CAS  Google Scholar 

  • Parmigiani G, Garrett ES, Anbazhagan R, Gabrielson E (2002) A statistical framework for expression-based molecular classification in cancer. J R Stat Soc Ser B Stat Methodol 64(4):717–736

    Article  Google Scholar 

  • Parmigiani G, Garrett-Mayer ES, Anbazhagan R, Gabrielson E (2004) A cross-study comparison of gene expression studies for the molecular classification of lung cancer. Clin Cancer Res 10(9):2922–2927

    Article  PubMed  CAS  Google Scholar 

  • Patterson TA, Lobenhofer EK, Fulmer-Smentek SB, Collins PJ, Chu TM, Bao W, Fang H, Kawasaki ES, Hager J, Tikhonova IR, Walker SJ, Zhang L, Hurban P, de Longueville F, Fuscoe JC, Tong W, Shi L, Wolfinger RD (2006) Performance comparison of one-color and two-color platforms within the MicroArray quality control (MAQC) project. Nat Biotechnol 24(9):1140–1150

    Article  PubMed  CAS  Google Scholar 

  • Perou CM, Jeffrey SS, van de Rijn M, Rees CA, Eisen MB, Ross DT, Pergamenschikov A, Williams CF, Zhu SX, Lee JC, Lashkari D, Shalon D, Brown PO, Botstein D (1999) Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci USA 96(16):9212–9217

    Article  PubMed  CAS  Google Scholar 

  • Pihur V, Datta S, Datta S (2008) Finding common genes in multiple cancer types through meta-analysis of microarray experiments: a rank aggregation approach. Genomics 92(6):400–403

    Article  PubMed  CAS  Google Scholar 

  • Pihur V, Datta S, Datta S (2009) RankAggreg, an R package for weighted rank aggregation. BMC Bioinformatics 10:62

    Article  PubMed  Google Scholar 

  • Pollack JR, Perou CM, Alizadeh AA, Eisen MB, Pergamenschikov A, Williams CF, Jeffrey SS, Botstein D, Brown PO (1999) Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nat Genet 23(1):41–46

    Article  PubMed  CAS  Google Scholar 

  • Pruitt KD, Tatusova T, Klimke W, Maglott DR (2009) NCBI reference sequences: current status, policy and new initiatives. Nucleic Acids Res 37(Database issue):D32–D36

    Article  PubMed  CAS  Google Scholar 

  • Ramalho-Santos M, Yoon S, Matsuzaki Y, Mulligan RC, Melton DA (2002) “Stemness”: transcriptional profiling of embryonic and adult stem cells. Science 298(5593):597–600

    Article  PubMed  CAS  Google Scholar 

  • Ramasamy A, Mondry A, Holmes CC, Altman DG (2008) Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med 5(9):e184

    Article  PubMed  CAS  Google Scholar 

  • Ramaswamy S, Ross KN, Lander ES, Golub TR (2003) A molecular signature of metastasis in primary solid tumors. Nat Genet 33(1):49–54

    Article  PubMed  CAS  Google Scholar 

  • Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM (2002) Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res 62(15):4427–4433

    PubMed  CAS  Google Scholar 

  • Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM (2004) Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA 101(25):9309–9314

    Article  PubMed  CAS  Google Scholar 

  • Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, Barrette TR, Anstet MJ, Kincead-Beal C, Kulkarni P, Varambally S, Ghosh D, Chinnaiyan AM (2007) Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia 9(2):166–180

    Article  PubMed  CAS  Google Scholar 

  • Saeed AI, Bhagabati NK, Braisted JC, Liang W, Sharov V, Howe EA, Li J, Thiagarajan M, White JA, Quackenbush J (2006) TM4 microarray software suite. Methods Enzymol 411:134–193

    Article  PubMed  CAS  Google Scholar 

  • Sahai H, Ageel MI (2000) The analysis of variance: fixed, random, and mixed models. Birkhäuser, Boston

    Google Scholar 

  • Schadt EE, Li C, Ellis B, Wong WH (2001) Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. J Cell Biochem Suppl 37:120–125

    Article  PubMed  CAS  Google Scholar 

  • Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235):467–470

    Article  PubMed  CAS  Google Scholar 

  • Shalon D, Smith SJ, Brown PO (1996) A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res 6(7):639–645

    Article  PubMed  CAS  Google Scholar 

  • Shen R, Ghosh D, Chinnaiyan AM (2004) Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data. BMC Genomics 5(1):94

    Article  PubMed  CAS  Google Scholar 

  • Shi L, Tong W, Fang H, Scherf U, Han J, Puri RK, Frueh FW, Goodsaid FM, Guo L, Su Z, Han T, Fuscoe JC, Xu ZA, Patterson TA, Hong H, Xie Q, Perkins RG, Chen JJ, Casciano DA (2005) Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential. BMC Bioinformatics 6(Suppl 2):S12

    Article  PubMed  CAS  Google Scholar 

  • Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de Longueville F, Kawasaki ES, Lee KY, Luo Y, Sun YA, Willey JC, Setterquist RA, Fischer GM, Tong W, Dragan YP, Dix DJ, Frueh FW, Goodsaid FM, Herman D, Jensen RV, Johnson CD, Lobenhofer EK, Puri RK, Schrf U, Thierry-Mieg J, Wang C, Wilson M, Wolber PK, Zhang L, Amur S, Bao W, Barbacioru CC, Lucas AB, Bertholet V, Boysen C, Bromley B, Brown D, Brunner A, Canales R, Cao XM, Cebula TA, Chen JJ, Cheng J, Chu TM, Chudin E, Corson J, Corton JC, Croner LJ, Davies C, Davison TS, Delenstarr G, Deng X, Dorris D, Eklund AC, Fan XH, Fang H, Fulmer-Smentek S, Fuscoe JC, Gallagher K, Ge W, Guo L, Guo X, Hager J, Haje PK, Han J, Han T, Harbottle HC, Harris SC, Hatchwell E, Hauser CA, Hester S, Hong H, Hurban P, Jackson SA, Ji H, Knight CR, Kuo WP, LeClerc JE, Levy S, Li QZ, Liu C, Liu Y, Lombardi MJ, Ma Y, Magnuson SR, Maqsodi B, McDaniel T, Mei N, Myklebost O, Ning B, Novoradovskaya N, Orr MS, Osborn TW, Papallo A, Patterson TA, Perkins RG, Peters EH, Peterson R, Philips KL, Pine PS, Pusztai L, Qian F, Ren H, Rosen M, Rosenzweig BA, Samaha RR, Schena M, Schroth GP, Shchegrova S, Smith DD, Staedtler F, Su Z, Sun H, Szallasi Z, Tezak Z, Thierry-Mieg D, Thompson KL, Tikhonova I, Turpaz Y, Vallanat B, Van C, Walker SJ, Wang SJ, Wang Y, Wolfinger R, Wong A, Wu J, Xiao C, Xie Q, Xu J, Yang W, Zhang L, Zhong S, Zong Y, Slikker W Jr (2006) The MicroArray quality control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 24(9):1151–1161

    Article  PubMed  CAS  Google Scholar 

  • Shimizu D, Ishikawa T, Ichikawa Y, Togo S, Hayasizaki Y, Okazaki Y, Shimada H (2004) Current progress in the prediction of chemosensitivity for breast cancer. Breast Cancer 11(1):42–48

    Article  PubMed  Google Scholar 

  • Shippy R, Fulmer-Smentek S, Jensen RV, Jones WD, Wolber PK, Johnson CD, Pine PS, Boysen C, Guo X, Chudin E, Sun YA, Willey JC, Thierry-Mieg J, Thierry-Mieg D, Setterquist RA, Wilson M, Lucas AB, Novoradovskaya N, Papallo A, Turpaz Y, Baker SC, Warrington JA, Shi L, Herman D (2006) Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nat Biotechnol 24(9):1123–1131

    Article  PubMed  CAS  Google Scholar 

  • Singh-Gasson S, Green RD, Yue Y, Nelson C, Blattner F, Sussman MR, Cerrina F (1999) Maskless fabrication of light-directed oligonucleotide microarrays using a digital micromirror array. Nat Biotechnol 17(10):974–978

    Article  PubMed  CAS  Google Scholar 

  • Slodkowska EA, Ross JS (2009) MammaPrint 70-gene signature: another milestone in personalized medical care for breast cancer patients. Expert Rev Mol Diagn 9(5):417–422

    Article  PubMed  Google Scholar 

  • Smith DD, Saetrom P, Snove O Jr, Lundberg C, Rivas GE, Glackin C, Larson GP (2008) Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate regulation. BMC Bioinformatics 9:63

    Article  PubMed  CAS  Google Scholar 

  • Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3, Article3

    Google Scholar 

  • Southern E, Mir K, Shchepinov M (1999) Molecular interactions on microarrays. Nat Genet 21(Suppl 1):5–9

    Article  PubMed  CAS  Google Scholar 

  • Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15:72–101

    Article  Google Scholar 

  • Staunton JE, Slonim DK, Coller HA, Tamayo P, Angelo MJ, Park J, Scherf U, Lee JK, Reinhold WO, Weinstein JN, Mesirov JP, Lander ES, Golub TR (2001) Chemosensitivity prediction by transcriptional profiling. Proc Natl Acad Sci USA 98(19):10787–10792

    Article  PubMed  CAS  Google Scholar 

  • Stouffer SA (1949) The American soldier, vol 2. Princeton University Press, Princeton

    Google Scholar 

  • Stuart JM, Segal E, Koller D, Kim SK (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302(5643):249–255

    Article  PubMed  CAS  Google Scholar 

  • Sturn A, Quackenbush J, Trajanoski Z (2002) Genesis: cluster analysis of microarray data. Bioinformatics 18(1):207–208

    Article  PubMed  CAS  Google Scholar 

  • Suarez E, Burguete A, McLachlan GJ (2009) Microarray data analysis for differential expression: a tutorial. P R Health Sci J 28(2):89–104

    PubMed  Google Scholar 

  • Suarez-Farinas M, Noggle S, Heke M, Hemmati-Brivanlou A, Magnasco MO (2005) Comparing independent microarray studies: the case of human embryonic stem cells. BMC Genomics 6:99

    Article  PubMed  CAS  Google Scholar 

  • Teh MT, Blaydon D, Chaplin T, Foot NJ, Skoulakis S, Raghavan M, Harwood CA, Proby CM, Philpott MP, Young BD, Kelsell DP (2005) Genomewide single nucleotide polymorphism microarray mapping in basal cell carcinomas unveils uniparental disomy as a key somatic event. Cancer Res 65(19):8597–8603

    Article  PubMed  CAS  Google Scholar 

  • Thurstone LL (1931) Rank order as a psycho-physical method. J Exp Psychol 14(3):187–201

    Article  Google Scholar 

  • Troyanskaya OG (2005) Putting microarrays in a context: integrated analysis of diverse biological data. Brief Bioinform 6(1):34–43

    Article  PubMed  CAS  Google Scholar 

  • Tsai J, Sultana R, Lee Y, Pertea G, Karamycheva S, Antonescu V, Cho J, Parvizi B, Cheung F, Quackenbush J (2001) RESOURCERER: a database for annotating and linking microarray resources within and across species. Genome Biol 2(11): SOFTWARE0002

    Google Scholar 

  • Tukey JW (1977) Exploratory data analysis, vol 2. Addison Wesley, Boston

    Google Scholar 

  • Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98(9):5116–5121

    Article  PubMed  CAS  Google Scholar 

  • van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536

    Google Scholar 

  • Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (1995) Serial analysis of gene expression. Science 270(5235):484–487

    Article  PubMed  CAS  Google Scholar 

  • Venn J (1880) On the diagrammatic and mechanical representation of propositions and reasonings. Philos Mag J Sci 9(59):1–18

    Article  Google Scholar 

  • Vierlinger K, Mansfeld MH, Koperek O, Nohammer C, Kaserer K, Leisch F (2011) Identification of SERPINA1 as single marker for papillary thyroid carcinoma through microarray meta analysis and quantification of its discriminatory power in independent validation. BMC Med Genomics 4:30

    Article  PubMed  Google Scholar 

  • Wang J, Coombes KR, Highsmith WE, Keating MJ, Abruzzo LV (2004) Differences in gene expression between B-cell chronic lymphocytic leukemia and normal B cells: a meta-analysis of three microarray studies. Bioinformatics 20(17):3166–3178

    Article  PubMed  CAS  Google Scholar 

  • Wang Y, Miao ZH, Pommier Y, Kawasaki ES, Player A (2007) Characterization of mismatch and high-signal intensity probes associated with Affymetrix genechips. Bioinformatics 23(16):2088–2095

    Article  PubMed  CAS  Google Scholar 

  • Wennmalm K, Wahlestedt C, Larsson O (2005) The expression signature of in vitro senescence resembles mouse but not human aging. Genome Biol 6(13):R109

    Article  PubMed  CAS  Google Scholar 

  • White KP, Rifkin SA, Hurban P, Hogness DS (1999) Microarray analysis of Drosophila development during metamorphosis. Science 286(5447):2179–2184

    Article  PubMed  CAS  Google Scholar 

  • Whitlock MC (2005) Combining probability from independent tests: the weighted Z-method is superior to Fisher’s approach. J Evol Biol 18(5):1368–1373

    Article  PubMed  CAS  Google Scholar 

  • Wilkinson L, Friendly M (2009) The history of the cluster heat map. Am Stats 63(2):179–184

    Article  Google Scholar 

  • Wren JD (2009) A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide. Bioinformatics 25(13):1694–1701

    Article  PubMed  CAS  Google Scholar 

  • Zembutsu H, Ohnishi Y, Tsunoda T, Furukawa Y, Katagiri T, Ueyama Y, Tamaoki N, Nomura T, Kitahara O, Yanagawa R, Hirata K, Nakamura Y (2002) Genome-wide cDNA microarray screening to correlate gene expression profiles with sensitivity of 85 human cancer xenografts to anticancer drugs. Cancer Res 62(2):518–527

    PubMed  CAS  Google Scholar 

  • Zhou XJ, Kao MC, Huang H, Wong A, Nunez-Iglesias J, Primig M, Aparicio OM, Finch CE, Morgan TE, Wong WH (2005) Functional annotation and network reconstruction through cross-platform integration of microarray data. Nat Biotechnol 23(2):238–243

    Article  PubMed  CAS  Google Scholar 

  • Zhu Y, Davis S, Stephens R, Meltzer PS, Chen Y (2008) GEOmetadb: powerful alternative search engine for the Gene Expression Omnibus. Bioinformatics 24(23):2798–2800

    Article  PubMed  CAS  Google Scholar 

  • Zintzaras E, Ioannidis JP (2008) Meta-analysis for ranked discovery datasets: theoretical framework and empirical demonstration for microarrays. Comput Biol Chem 32(1):38–46

    Article  PubMed  CAS  Google Scholar 

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Authors and Affiliations

  1. North West Cancer Research Fund Institute, Bangor University, Bangor, Gwynedd, LL572UW, UK

    Julia Feichtinger

  2. Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010, Graz, Austria

    Julia Feichtinger

  3. Institute for Genomics and Bioinformatics, Graz University of Technology, Graz, Austria

    Gerhard G. Thallinger

  4. Core Facility Bioinformatics, Austrian Centre of Industrial Biotechnology (ACIB GmbH), Graz, Austria

    Gerhard G. Thallinger

  5. North West Cancer Research Fund Institute, Bangor University, Bangor, Wales, UK

    Ramsay J. McFarlane

  6. Cranfield Health, Cranfield University, Cranfield, Bedfordshire, UK

    Lee D. Larcombe

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  4. Lee D. Larcombe
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    Zlatko Trajanoski

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Feichtinger, J., Thallinger, G.G., McFarlane, R.J., Larcombe, L.D. (2012). Microarray Meta-Analysis: From Data to Expression to Biological Relationships. In: Trajanoski, Z. (eds) Computational Medicine. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0947-2_4

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