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Case/Control Prediction from Illumina Methylation Microarray’s β and Two-Color Channels in the Presence of Batch Effects

  • Fabrice Colas
  • Jeanine J. Houwing-Duistermaat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7548)

Abstract

Among the published studies that submitted Illumina BeadArray 27k methylation datasets to the Gene Expression Omnibus (GEO), more than nine out of ten analyse β, thus making β a de facto standard. Further, as β combines the two color channels M and U into the ratio M/(M + U), we also assume, maybe naively, that β conveys more biologically relevant information than a single color taken alone. As well, a fourth of the GEO studies do not report any analysis step to cancel for non-biological variation. Here, we farther assess the validity of β as a micro array methylation analysis measure by testing empirically whether β predicts more accurately the case/control status than the two color channels taken independently. In addition, we consider whether cancelling the non-biological effects due to the genotyping protocol influences the prediction accuracy. Our results show that M alone predicts better than β and U, interpreting that U’s low prediction impacts negatively the one of β. We also confirm that without proper batch effect cancellation, non-biological variance hides the biological signal, making impractical the prediction of case status.

Keywords

DNA Methylation Microarray Batch Effect Prediction 

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References

  1. 1.
    Fraga, M.F., Ballestar, E., Paz, M.F., Ropero, S., Setien, F., Ballestar, M.L., Heine-Suñer, D., Cigudosa, J.C., Urioste, M., Benitez, J., Boix-Chornet, M., Sanchez-Aguilera, A., Ling, C., Carlsson, E., Poulsen, P., Vaag, A., Stephan, Z., Spector, T.D., Wu, Y.-Z., Plass, C., Esteller, M.: Epigenetic differences arise during the lifetime of monozygotic twins. Proceedings of the National Academy of Sciences of the United States of America 102, 10604–10609 (2005)CrossRefGoogle Scholar
  2. 2.
    Wikipedia. Epigenetics (2011) (online accessed April 14, 2011)Google Scholar
  3. 3.
    Wood, A.J., Oakey, R.J.: Genomic imprinting in mammals: emerging themes and established theories. PLoS Genetics 2, e147 (2006)CrossRefGoogle Scholar
  4. 4.
    Jones, P.A., Laird, P.W.: Cancer epigenetics comes of age. Nature Genetics 21, 163–167 (1999)CrossRefGoogle Scholar
  5. 5.
    Feinberg, A.P., Tycko, B.: The history of cancer epigenetics. Nature Reviews. Cancer 4, 143–153 (2004)CrossRefGoogle Scholar
  6. 6.
    Pembrey, M.E., Bygren, L.O., Kaati, G., Edvinsson, S., Northstone, K., Sjöström, M., Golding, J.: Sex-specific, male-line transgenerational responses in humans. European Journal of Human Genetics: EJHG 14, 159–166 (2006)CrossRefGoogle Scholar
  7. 7.
    Bibikova, M., Lin, Z., Zhou, L., Chudin, E., Garcia, E.W., Wu, B., Doucet, D., Thomas, N.J., Wang, Y., Vollmer, E., Goldmann, T., Seifart, C., Jiang, W., Barker, D.L., Chee, M.S., Floros, J., Fan, J.-B.: High-throughput dna methylation profiling using universal bead arrays. Genome Research 16, 383–393 (2006)CrossRefGoogle Scholar
  8. 8.
    Weisenberger, D., Van Den Berg, D., Pan, F., Berman, B., Laird, P.W.: Comprehensive dna methylation analysis on the illumina infinium assay platform (2008)Google Scholar
  9. 9.
    Edgar, R., Domrachev, M., Lash, A.E.: Gene expression omnibus: Ncbi gene expression and hybridization array data repository. Nucleic Acids Research 30, 207–210 (2002)CrossRefGoogle Scholar
  10. 10.
    Barrett, T., Troup, D.B., Wilhite, S.E., Ledoux, P., Evangelista, C., Kim, I.F., Tomashevsky, M., Marshall, K.A., Phillippy, K.H., Sherman, P.M., Muertter, R.N., Holko, M., Ayanbule, O., Yefanov, A., Soboleva, A.: Ncbi geo: archive for functional genomics data sets–10 years on. Nucleic Acids Research 39, D1005–D1010 (2011)CrossRefGoogle Scholar
  11. 11.
    Cleveland, W.S.: LOWESS: A Program for Smoothing Scatterplots by Robust Locally Weighted Regression. The American Statistician 35(1) (1981)Google Scholar
  12. 12.
    Bolstad, B.M., Irizarry, R.A., Astrand, M., Speed, T.P.: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003)CrossRefGoogle Scholar
  13. 13.
    Laird, P.W.: Principles and challenges of genome-wide dna methylation analysis. Nature Reviews. Genetics 11, 191–203 (2010)CrossRefGoogle Scholar
  14. 14.
    Teschendorff, A.E., Menon, U., Gentry-Maharaj, A., Ramus, S.J., Weisenberger, D.J., Shen, H., Campan, M., Noushmehr, H., Bell, C.G., Maxwell, P., Savage, D.A., Mueller-Holzner, E., Marth, C., Kocjan, G., Gayther, S.A., Jones, A., Beck, S., Wagner, W., Laird, P.W., Jacobs, I.J., Widschwendter, M.: Age-dependent dna methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Research 20, 440–446 (2010)CrossRefGoogle Scholar
  15. 15.
    R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2010) ISBN 3-900051-07-0Google Scholar
  16. 16.
    Gentleman, R., Carey, V., Bates, D., 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 Biology 5(10), R80 (2004)Google Scholar
  17. 17.
    Evan Johnson, W., Li, C., Rabinovic, A.: Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8, 118–127 (2007)CrossRefGoogle Scholar
  18. 18.
    Langfelder, P., Horvath, S.: Wgcna: an r package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008)CrossRefGoogle Scholar
  19. 19.
    Oldham, M., Langfelder, P., Horvath, S.: Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease. BMC Systems Biology 6(1), 63 (2012), http://www.biomedcentral.com/1752-0509/6/63, doi:10.1186/1752-0509-6-63CrossRefGoogle Scholar
  20. 20.
    Du, P., Kibbe, W.A., Lin, S.M.: Lumi: a pipeline for processing illumina microarray. Bioinformatics 24, 1547–1548 (2008)CrossRefGoogle Scholar
  21. 21.
    Carey, V., Gentleman, R., Mar, J., contributions from Vertrees, J.: MLInterfaces: Uniform interfaces to R machine learning procedures for data in Bioconductor containers. R package version 1.30.0Google Scholar
  22. 22.
    Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D., Weingessel, A.: e1071: Misc Functions of the Department of Statistics (e1071), TU Wien, R package version 1.5-24 (2010)Google Scholar
  23. 23.
    Illumina. Normalization and Differential Analysis (2008)Google Scholar
  24. 24.
    Vapnik, V.N.: The Nature of Statistical Theory. In: Information Science and Statistics. Springer (1995)Google Scholar
  25. 25.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001)Google Scholar
  26. 26.
    Fan, R.-E., Chen, P.-H., Lin, C.-J.: Working set selection using second order information for training support vector machines. J. Mach. Learn. Res. 6, 1889–1918 (2005)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Du, P., Zhang, X., Huang, C.-C., Jafari, N., Kibbe, W.A., Hou, L., Lin, S.M.: Comparison of beta-value and m-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11, 587 (2010)CrossRefGoogle Scholar
  28. 28.
    Liu, J., Zhang, Z., Bando, M., Itoh, T., Deardorff, M.A., Li, J.R., Clark, D., Kaur, M., Tatsuro, K., Kline, A.D., Chang, C., Vega, H., Jackson, L.G., Spinner, N.B., Shirahige, K., Krantz, I.D.: Genome-wide dna methylation analysis in cohesin mutant human cell lines. Nucleic Acids Research 38, 5657–5671 (2010)CrossRefGoogle Scholar
  29. 29.
    Fang, F., Turcan, S., Rimner, A., Kaufman, A., Giri, D., Morris, L.T., Shen, R., Seshan, V., Mo, Q., Heguy, A., Baylin, S.B., Ahuja, N., Viale, A., Massague, J., Norton, L., Vahdat, L.T., Moynahan, M.E., Chan, T.A.: Breast cancer methylomes establish an epigenomic foundation for metastasis. Science Translational Medicine 3, 75ra25 (2011)Google Scholar
  30. 30.
    Boks, M.P., Derks, E.M., Weisenberger, D.J., Strengman, E., Janson, E., Sommer, I.E., Kahn, R.S., Ophoff, R.A.: The relationship of dna methylation with age, gender and genotype in twins and healthy controls. PLoS One 4(8), 6767 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fabrice Colas
    • 1
  • Jeanine J. Houwing-Duistermaat
    • 1
  1. 1.MEDSTATSLeiden University Medical CenterLeidenThe Netherlands

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