Opportunities and Challenges of Multiplex Assays: A Machine Learning Perspective

  • Junfang Chen
  • Emanuel SchwarzEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1546)


Multiplex assays that allow the simultaneous measurement of multiple analytes in small sample quantities have developed into a widely used technology. Their implementation spans across multiple assay systems and can provide readouts of similar quality as the respective single-plex measures, albeit at far higher throughput. Multiplex assay systems are therefore an important element for biomarker discovery and development strategies but analysis of the derived data can face substantial challenges that may limit the possibility of identifying meaningful biological markers. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, in particular from the perspective of machine learning aimed at identification of predictive biological signatures.

Key words

Biomarker discovery Machine learning Confounding Bias Multiplex 



This study was supported by the DFG Emmy-Noether-Program SCHW 1768/1-1.


  1. 1.
    Gutstein HB, Morris JS, Annangudi SP, Sweedler JV (2008) Microproteomics: analysis of protein diversity in small samples. Mass Spectrom Rev 27:316–330CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM et al (2011) High density DNA methylation array with single CpG site resolution. Genomics 98:288–295CrossRefPubMedGoogle Scholar
  3. 3.
    van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536CrossRefGoogle Scholar
  4. 4.
    Elshal MF, McCoy JP (2006) Multiplex bead array assays: performance evaluation and comparison of sensitivity to ELISA. Methods 38:317–323CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Dupont NC, Wang K, Wadhwa PD, Culhane JF, Nelson EL (2005) Validation and comparison of luminex multiplex cytokine analysis kits with ELISA: determinations of a panel of nine cytokines in clinical sample culture supernatants. J Reprod Immunol 66:175–191CrossRefPubMedGoogle Scholar
  6. 6.
    Tighe PJ, Ryder RR, Todd I, Fairclough LC (2015) ELISA in the multiplex era: potentials and pitfalls. Proteomics Clin Appl 9:406–422CrossRefPubMedGoogle Scholar
  7. 7.
    Ein-Dor L, Zuk O, Domany E (2006) Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci U S A 103:5923–5928CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Kim SY (2009) Effects of sample size on robustness and prediction accuracy of a prognostic gene signature. BMC Bioinformatics 10:147CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Ellington AA, Kullo IJ, Bailey KR, Klee GG (2009) Measurement and quality control issues in multiplex protein assays: a case study. Clin Chem 55:1092–1099CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Mollenhauer B Parnetti L, Rektorova I, Kramberger MG, Pikkarainen M, Schulz-Schaeffer WJ et al (2015) Biological confounders for the values of cerebrospinal fluid proteins in Parkinson's disease and related disorders. J Neurochem. doi:  10.1111/jnc.13390. [Epub ahead of print]
  11. 11.
    Schwarz E, Izmailov R, Spain M, Barnes A, Mapes JP, Guest PC et al (2010) Validation of a blood-based laboratory test to aid in the confirmation of a diagnosis of schizophrenia. Biomark Insights 12:39–47Google Scholar
  12. 12.
    Surinova S, Choi M, Tao S, Schüffler PJ, Chang CY, Clough T et al (2015) Prediction of colorectal cancer diagnosis based on circulating plasma proteins. EMBO Mol Med 7:1166–1178CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Martins-de-Souza D, Alsaif M, Ernst A, Harris LW, Aerts N, Lenaerts I et al (2012) The application of selective reaction monitoring confirms dysregulation of glycolysis in a preclinical model of schizophrenia. BMC Res Notes 5:146CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Hembrough T, Thyparambil S, Liao WL, Darfler MM, Abdo J, Bengali KM et al (2013) Application of selected reaction monitoring for multiplex quantification of clinically validated biomarkers in formalin-fixed, paraffin-embedded tumor tissue. J Mol Diagn 15:454–465CrossRefPubMedGoogle Scholar
  15. 15.
    Xie C, Kim HJ, Haw JG, Kalbasi A, Gardner BK, Li G et al (2011) A novel multiplex assay combining autoantibodies plus PSA has potential implications for classification of prostate cancer from non-malignant cases. J Transl Med 9:43CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Arjomandi A, Delanoy ML, Walker RP, Binder SR (2015) A novel algorithm to improve specificity in ovarian cancer detection. Clin Ovarian Other Gynecol Cancer DOI: Scholar
  17. 17.
    Califano A, Butte AJ, Friend S, Ideker T, Schadt E (2012) Leveraging models of cell regulation and GWAS data in integrative network-based association studies. Nat Genet 44:841–847CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Wang K, Li M, Hakonarson H (2010) Analysing biological pathways in genome-wide association studies. Nat Rev Genet 11:843–854CrossRefPubMedGoogle Scholar
  19. 19.
    Dobrin R, Zhu J, Molony C, Argman C, Parrish ML, Carlson S et al (2009) Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. Genome Biol 10:R55CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Zhao Z, Liu H (2007) Searching for interacting features. Published in: IJCAI’07 Proceedings of the 20th international joint conference on artifical intelligence. pp 1156–1161Google Scholar
  21. 21.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297Google Scholar
  22. 22.
    Ho TK (1995) Random decision forests. In: Document analysis and recognition, proceedings of the third international conference on IEEE, vol 1, pp 278–282Google Scholar
  23. 23.
    Stahl-Zeng J, Lange V, Ossola R, Eckhardt K, Krek W, Aebersold R et al (2007) High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol Cell Proteomics 6:1809–1817CrossRefPubMedGoogle Scholar
  24. 24.
    Kiyonami R, Schoen A, Prakash A, Zabrouskov V, Peterman S, Picotti P et al (2011) Increased selectivity, analytical precision, and throughput in targeted proteomics. Mol Cell Proteomics 10:M110–M002931. doi: 10.1074/mcp.M110.002931.EpubJul27 CrossRefPubMedGoogle Scholar
  25. 25.
    Skogstrand K, Thorsen P, Nørgaard-Pedersen B, Schendel DE, Sørensen LC, Hougaard DM (2005) Simultaneous measurement of 25 inflammatory markers and neurotrophins in neonatal dried blood spots by immunoassay with xMAP technology. Clin Chem 51:1854–1866CrossRefPubMedGoogle Scholar
  26. 26.
    Todd DJ, Knowlton N, Amato M, Frank MB, Schur PH, Izmailova ES et al (2011) Erroneous augmentation of multiplex assay measurements in patients with rheumatoid arthritis due to heterophilic binding by serum rheumatoid factor. Arthritis Rheum 63:894–903CrossRefPubMedGoogle Scholar
  27. 27.
    Churchman SM, Geiler J, Parmar R, Horner EA, Church LD, Emery P et al (2012) Multiplexing immunoassays for cytokine detection in the serum of patients with rheumatoid arthritis: lack of sensitivity and interference by rheumatoid factor. Clin Exp Rheumatol 30:534–542PubMedGoogle Scholar
  28. 28.
    Annesley TM (2003) Ion suppression in mass spectrometry. Clin Chem 49:1041–1044CrossRefPubMedGoogle Scholar
  29. 29.
    Grebe SK, Singh RJ (2011) LC-MS/MS in the clinical laboratory-where to from here. Clin Biochem Rev 32:5–31PubMedPubMedCentralGoogle Scholar
  30. 30.
    Zhang X, Simmerman K, Yen-Lieberman B, Daly TM (2013) Impact of analytical variability on clinical interpretation of multiplex pneumococcal serology assays. Clin Vaccine Immunol 20:957–961CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Hill HR, Pickering JW (2013) Reference laboratory agreement on multianalyte pneumococcal antibody results: an absolute must! Clin Vaccine Immunol 20:955–956CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Blankley RT, Fisher C, Westwood M, North R, Baker PN, Walker MJ et al (2013) A label-free selected reaction monitoring workflow identifies a subset of pregnancy specific glycoproteins as potential predictive markers of early-onset pre-eclampsia. Mol Cell Proteomics 12:3148–3159CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Clarke DC, Morris MK, Lauffenburger DA (2010) Normalization and statistical analysis of multiplexed bead-based immunoassay data using mixed-effects modeling. Mol Cell Proteomics 12:245–262CrossRefGoogle Scholar
  34. 34.
    Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE et al (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11:733–739CrossRefPubMedGoogle Scholar
  35. 35.
    Browne RW, Kantarci A, LaMonte MJ, Andrews CA, Hovey KM, Falkner KL et al (2013) Performance of multiplex cytokine assays in serum and saliva among community-dwelling postmenopausal women. PLoS One 8, e59498CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Soneson C, Gerster S, Delorenzi M (2014) Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation. PLoS One 9, e100335CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Parker HS, Corrada Bravo H, Leek JT (2014) Removing batch effects for prediction problems with frozen surrogate variable analysis. Peer J 2, e561CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Cham GK, Kurtis J, Lusingu J, Theander TG, Jensen AT, Turner L (2008) A semi-automated multiplex high-throughput assay for measuring IgG antibodies against Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) domains in small volumes of plasma. Malar J 7:108CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Pollack AZ, Perkins NJ, Mumford SL, Ye A, Schisterman EF (2013) Correlated biomarker measurement error: an important threat to inference in environmental epidemiology. Am J Epidemiol 177:84–92CrossRefPubMedGoogle Scholar
  40. 40.
    Mani A, Ravindran R, Mannepalli S, Vang D, Luciw PA, Hogarth M et al (2015) Data mining strategies to improve multiplex microbead immunoassay tolerance in a mouse model of infectious diseases. PLoS One 10:e0116262CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty MannheimHeidelberg UniversityMannheimGermany

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