Antibody Microarrays and Multiplexing

  • Jerry Zhou
  • Larissa Belov
  • Nicola Armstrong
  • Richard I. Christopherson
Part of the Translational Bioinformatics book series (TRBIO, volume 3)


This chapter presents a range of statistical methods for antibody microarray normalization and data analysis. Commonly used techniques for cluster generation, differential analysis, and classification are covered. The focus is on the implementation of each technique to the technology and its suitability in relation to sample types and experiment design.


Antibody microarray Bioinformatics Data variability Normalization Unsupervised clustering techniques Supervised differential analysis Multiple testing Classification 


  1. Angenendt P, Glokler J, Murphy D, Lehrach H, Cahill DJ. Toward optimized antibody microarrays: a comparison of current microarray support materials. Anal Biochem. 2002;309:253–60.PubMedCrossRefGoogle Scholar
  2. Armstrong NJ, van de Wiel MA. Microarray data analysis: from hypotheses to conclusions using gene expression data. Cell Oncol: Official J Int Soc Cell Oncol. 2004;26:279–90.Google Scholar
  3. Barrios-Rodiles M, Brown KR, Ozdamar B, Bose R, Liu Z, Donovan RS, Shinjo F, Liu Y, Dembowy J, Taylor IW, et al. High-throughput mapping of a dynamic signaling network in mammalian cells. Science. 2005;307:1621–5.PubMedCrossRefGoogle Scholar
  4. Belov L, de la Vega O, dos Remedios CG, Mulligan SP, Christopherson RI. Immunophenotyping of leukemias using a cluster of differentiation antibody microarray. Cancer Res. 2001;61:4483–9.PubMedGoogle Scholar
  5. Belov L, Mulligan SP, Barber N, Woolfson A, Scott M, Stoner K, Chrisp JS, Sewell WA, Bradstock KF, Bendall L, et al. Analysis of human leukaemias and lymphomas using extensive immunophenotypes from an antibody microarray. Br J Haematol. 2006;135:184–97.PubMedCrossRefGoogle Scholar
  6. Benjamini Y, Hochberg Y. Controlling the false discovery rate – a practical and powerful approach to multiple testing. J R Stat Soc Ser B-Methodol. 1995;57:289–300.Google Scholar
  7. Bishop CM. Neural networks for pattern recognition. Oxford: Clarendon; 1995.Google Scholar
  8. Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A, et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature. 2000;406:536–40.PubMedCrossRefGoogle Scholar
  9. 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;19:185–93.PubMedCrossRefGoogle Scholar
  10. Breitling R, Herzyk P. Rank-based methods as a non-parametric alternative of the T-statistic for the analysis of biological microarray data. J Bioinform Comput Biol. 2005;3:1171–89.PubMedCrossRefGoogle Scholar
  11. Breitling R, Armengaud P, Amtmann A, Herzyk P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 2004;573:83–92.PubMedCrossRefGoogle Scholar
  12. Carlsson A, Wingren C, Ingvarsson J, Ellmark P, Baldertorp B, Ferno M, Olsson H, Borrebaeck CA. Serum proteome profiling of metastatic breast cancer using recombinant antibody microarrays. Eur J Cancer. 2008;44:472–80.PubMedCrossRefGoogle Scholar
  13. Carlsson A, Wuttge DM, Ingvarsson J, Bengtsson AA, Sturfelt G, Borrebaeck CA, Wingren C. Serum protein profiling of systemic lupus erythematosus and systemic sclerosis using recombinant antibody microarrays. Mol Cell Proteomic: MCP. 2011;10:M110 005033.Google Scholar
  14. Chatziioannou A, Moulos P, Kolisis FN. Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB. BMC Bioinformatics. 2009;10:354.PubMedCrossRefGoogle Scholar
  15. Cristianini N, Shawe-Taylor J. An introduction to support vector machines: and other kernel-based learning methods. Cambridge: Cambridge University Press; 2000.CrossRefGoogle Scholar
  16. Efron B, Tibshirani R. Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol. 2002;23:70–86.PubMedCrossRefGoogle Scholar
  17. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998;95:14863–8.PubMedCrossRefGoogle Scholar
  18. Friedman N. Inferring cellular networks using probabilistic graphical models. Science. 2004;303:799–805.PubMedCrossRefGoogle Scholar
  19. Friedman N, Linial M, Nachman I, Pe’er D. Using Bayesian networks to analyze expression data. J Comput Biol J Comput Mol Cell Biol. 2000;7:601–20.CrossRefGoogle Scholar
  20. Gao WM, Kuick R, Orchekowski RP, Misek DE, Qiu J, Greenberg AK, Rom WN, Brenner DE, Omenn GS, Haab BB, Hanash SM. Distinctive serum protein profiles involving abundant proteins in lung cancer patients based upon antibody microarray analysis. BMC Cancer. 2005;5:110.PubMedCrossRefGoogle Scholar
  21. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5:R80.PubMedCrossRefGoogle Scholar
  22. Gruvberger S, Ringner M, Chen Y, Panavally S, Saal LH, Borg A, Ferno M, Peterson C, Meltzer PS. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res. 2001;61:5979–84.PubMedGoogle Scholar
  23. Gulmann C, Sheehan KM, Kay EW, Liotta LA, Petricoin 3rd EF. Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer. J Pathol. 2006;208:595–606.PubMedCrossRefGoogle Scholar
  24. Hallborn J, Carlsson R. Automated screening procedure for high-throughput generation of antibody fragments. BioTechniques. 2002;(Suppl):30–7Google Scholar
  25. Hamelinck D, Zhou H, Li L, Verweij C, Dillon D, Feng Z, Costa J, Haab BB. Optimized normalization for antibody microarrays and application to serum-protein profiling. Mol Cell Proteomic MCP. 2005;4:773–84.CrossRefGoogle Scholar
  26. Hanes J, Schaffitzel C, Knappik A, Pluckthun A. Picomolar affinity antibodies from a fully synthetic naive library selected and evolved by ribosome display. Nat Biotechnol. 2000;18:1287–92.PubMedCrossRefGoogle Scholar
  27. Hastie T, Tibshirani R, Eisen MB, Alizadeh A, Levy R, Staudt L, Chan WC, Botstein D, Brown P. ‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol. 2000; 1:RESEARCH0003.Google Scholar
  28. Ingvarsson J, Wingren C, Carlsson A, Ellmark P, Wahren B, Engstrom G, Harmenberg U, Krogh M, Peterson C, Borrebaeck CA. Detection of pancreatic cancer using antibody microarray-based serum protein profiling. Proteomics. 2008;8:2211–19.PubMedCrossRefGoogle Scholar
  29. Jafari P, Azuaje F. An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors. BMC Med Inform Decis Mak. 2006;6:27.PubMedCrossRefGoogle Scholar
  30. Joliffe T. Principle components analysis. Berlin: Springer; 1986.CrossRefGoogle Scholar
  31. Kerr MK, Churchill GA. Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proc Natl Acad Sci U S A. 2001;98:8961–5.PubMedCrossRefGoogle Scholar
  32. Kerr MK, Churchill GA. Statistical design and the analysis of gene expression microarray data. Genet Res. 2007;89:509–14.PubMedCrossRefGoogle Scholar
  33. Kerr MK, Martin M, Churchill GA. Analysis of variance for gene expression microarray data. J Comput Biol J Comput Mol Cell Biol. 2000;7:819–37.CrossRefGoogle Scholar
  34. Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, Meltzer PS. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001;7:673–9.PubMedCrossRefGoogle Scholar
  35. Kohonen T. Self organizing maps. Berlin: Springer; 1995.CrossRefGoogle Scholar
  36. Kukar T, Eckenrode S, Gu Y, Lian W, Megginson M, She JX, Wu D. Protein microarrays to detect protein-protein interactions using red and green fluorescent proteins. Anal Biochem. 2002;306:50–4.PubMedCrossRefGoogle Scholar
  37. Kusnezow W, Jacob A, Walijew A, Diehl F, Hoheisel JD. Antibody microarrays: an evaluation of production parameters. Proteomics. 2003;3:254–64.PubMedCrossRefGoogle Scholar
  38. Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, Vandesompele J. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009;10:R64.PubMedCrossRefGoogle Scholar
  39. Miller JC, Zhou H, Kwekel J, Cavallo R, Burke J, Butler EB, Teh BS, Haab BB. Antibody microarray profiling of human prostate cancer sera: antibody screening and identification of potential biomarkers. Proteomics. 2003;3:56–63.PubMedCrossRefGoogle Scholar
  40. Mor G, Visintin I, Lai Y, Zhao H, Schwartz P, Rutherford T, Yue L, Bray-Ward P, Ward DC. Serum protein markers for early detection of ovarian cancer. Proc Natl Acad Sci U S A. 2005;102:7677–82.PubMedCrossRefGoogle Scholar
  41. Noble WS. How does multiple testing correction work? Nat Biotechnol. 2009;27:1135–7.PubMedCrossRefGoogle Scholar
  42. Olle EW, Sreekumar A, Warner RL, McClintock SD, Chinnaiyan AM, Bleavins MR, Anderson TD, Johnson KJ. Development of an internally controlled antibody microarray. Mol Cell Proteomic MCP. 2005;4:1664–72.CrossRefGoogle Scholar
  43. Pan W. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics. 2002;18:546–54.PubMedCrossRefGoogle Scholar
  44. 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:research0022.Google Scholar
  45. Pan W, Lin J, Le CT. A mixture model approach to detecting differentially expressed genes with microarray data. Funct Integr Genomics. 2003;3:117–24.PubMedCrossRefGoogle Scholar
  46. Pollard HB, Ji XD, Jozwik C, Jacobowitz DM. High abundance protein profiling of cystic fibrosis lung epithelial cells. Proteomics. 2005;5:2210–26.PubMedCrossRefGoogle Scholar
  47. Pollard HB, Srivastava M, Eidelman O, Jozwik C, Rothwell SW, Mueller GP, Jacobowitz DM, Darling T, Guggino WB, Wright J, et al. Protein microarray platforms for clinical proteomics. Proteomics Clin Appl. 2007;1:934–52.PubMedCrossRefGoogle Scholar
  48. Poon TC, Yip TT, Chan AT, Yip C, Yip V, Mok TS, Lee CC, Leung TW, Ho SK, Johnson PJ. Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes. Clin Chem. 2003;49:752–60.PubMedCrossRefGoogle Scholar
  49. Quackenbush J. Computational analysis of microarray data. Nat Rev Genet. 2001;2:418–27.PubMedCrossRefGoogle Scholar
  50. Raychaudhuri S, Stuart JM, Altman RB. Principal components analysis to summarize microarray experiments: application to sporulation time series. Pacific Symp Biocomput. 2000:455–66.Google Scholar
  51. Ringner M, Peterson C, Khan J. Analyzing array data using supervised methods. Pharmacogenomics. 2002;3:403–15.PubMedCrossRefGoogle Scholar
  52. Ruxton GD. The unequal variance t-test is an underused alternative to Student’s t-test and the Mann–Whitney U test. Behav Ecol. 2006;17:688–90.CrossRefGoogle Scholar
  53. Saal LH, Troein C, Vallon-Christersson J, Gruvberger S, Borg A, Peterson C. BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data. Genome Biol. 2002; 3:SOFTWARE0003.Google Scholar
  54. Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan GP. Causal protein-signaling networks derived from multiparameter single-cell data. Science. 2005;308:523–9.PubMedCrossRefGoogle Scholar
  55. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, et al. TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003;34:374–8.PubMedGoogle Scholar
  56. Saldanha AJ. Java Treeview–extensible visualization of microarray data. Bioinformatics. 2004;20:3246–8.PubMedCrossRefGoogle Scholar
  57. Schweitzer B, Wiltshire S, Lambert J, O’Malley S, Kukanskis K, Zhu Z, Kingsmore SF, Lizardi PM, Ward DC. Immunoassays with rolling circle DNA amplification: a versatile platform for ultrasensitive antigen detection. Proc Natl Acad Sci U S A. 2000;97:10113–19.PubMedCrossRefGoogle Scholar
  58. Schwender H, Krause A, Ickstadt K. Comparison of the empirical Bayes and the significance analysis of microarrays. Technical Report // Universität Dortmund, SFB 475 Komplexitäts­reduktion in Multivariaten Datenstrukturen, 2003;44.
  59. Shannon WD, Watson MA, Perry A, Rich K. Mantel statistics to correlate gene expression levels from microarrays with clinical covariates. Genet Epidemiol. 2002;23:87–96.PubMedCrossRefGoogle Scholar
  60. Shannon W, Culverhouse R, Duncan J. Analyzing microarray data using cluster analysis. Pharmacogenomics. 2003;4:41–52.PubMedCrossRefGoogle Scholar
  61. Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004; 3:Article3.Google Scholar
  62. Srivastava M, Eidelman O, Jozwik C, Paweletz C, Huang W, Zeitlin PL, Pollard HB. Serum proteomic signature for cystic fibrosis using an antibody microarray platform. Mol Genet Metab. 2006;87:303–10.PubMedCrossRefGoogle Scholar
  63. Story CM, Papa E, Hu CC, Ronan JL, Herlihy K, Ploegh HL, Love JC. Profiling antibody responses by multiparametric analysis of primary B cells. Proc Natl Acad Sci U S A. 2008;105:17902–7.PubMedCrossRefGoogle Scholar
  64. Thomas JG, Olson JM, Tapscott SJ, Zhao LP. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res. 2001;11:1227–36.PubMedCrossRefGoogle Scholar
  65. Toronen P, Kolehmainen M, Wong G, Castren E. Analysis of gene expression data using self-organizing maps. FEBS Lett. 1999;451:142–6.PubMedCrossRefGoogle Scholar
  66. Troyanskaya OG, Garber ME, Brown PO, Botstein D, Altman RB. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics. 2002;18:1454–61.PubMedCrossRefGoogle Scholar
  67. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001;98:5116–21.PubMedCrossRefGoogle Scholar
  68. Wang Y, Wu TR, Cai S, Welte T, Chin YE. Stat1 as a component of tumor necrosis factor alpha receptor 1-TRADD signaling complex to inhibit NF-kappaB activation. Mol Cell Biol. 2000;20:4505–12.PubMedCrossRefGoogle Scholar
  69. Welch BL. The generalisation of student’s problems when several different population variances are involved. Biometrika. 1947;34:28–35.Google Scholar
  70. White CN, Chan DW, Zhang Z. Bioinformatics strategies for proteomic profiling. Clin Biochem. 2004;37:636–41.PubMedCrossRefGoogle Scholar
  71. White SL, Belov L, Barber N, Hodgkin PD, Christopherson RI. Immunophenotypic changes induced on human HL60 leukaemia cells by 1alpha,25-dihydroxyvitamin D3 and 12-O-tetradecanoyl phorbol-13-acetate. Leuk Res. 2005;29:1141–51.PubMedCrossRefGoogle Scholar
  72. 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 J Comput Mol Cell Biol. 2001;8:625–37.CrossRefGoogle Scholar
  73. Wu X, Liu H, Liu J, Haley KN, Treadway JA, Larson JP, Ge N, Peale F, Bruchez MP. Immunofluorescent labeling of cancer marker Her2 and other cellular targets with semiconductor quantum dots. Nat Biotechnol. 2003;21:41–6.PubMedCrossRefGoogle Scholar
  74. Wylie D, Shelton J, Choudhary A, Adai AT. A novel mean-centering method for normalizing microRNA expression from high-throughput RT-qPCR data. BMC Res Notes. 2011;4:555.PubMedCrossRefGoogle Scholar
  75. Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002;30:e15.PubMedCrossRefGoogle Scholar
  76. Yang JY, Zong CS, Xia W, Wei Y, Ali-Seyed M, Li Z, Broglio K, Berry DA, Hung MC. MDM2 promotes cell motility and invasiveness by regulating E-cadherin degradation. Mol Cell Biol. 2006;26:7269–82.PubMedCrossRefGoogle Scholar
  77. Zhou J, Belov L, Solomon MJ, Chan C, Clarke SJ, Christopherson RI. Colorectal cancer cell surface protein profiling using an antibody microarray and fluorescence multiplexing. J. Vis. Exp. 2011;(55):e3322. DOI:10.3791/3322.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Jerry Zhou
    • 1
  • Larissa Belov
    • 1
  • Nicola Armstrong
    • 2
    • 3
  • Richard I. Christopherson
    • 1
  1. 1.School of Molecular BioscienceUniversity of SydneySydneyAustralia
  2. 2.Cancer Research ProgramGarvan Institute of Medical ResearchDarlinghurstAustralia
  3. 3.School of Mathematics and Statistics and Prince of Wales Clinical SchoolUniversity of New South WalesRensingtonAustralia

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