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.
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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)
Wikipedia. Epigenetics (2011) (online accessed April 14, 2011)
Wood, A.J., Oakey, R.J.: Genomic imprinting in mammals: emerging themes and established theories. PLoS Genetics 2, e147 (2006)
Jones, P.A., Laird, P.W.: Cancer epigenetics comes of age. Nature Genetics 21, 163–167 (1999)
Feinberg, A.P., Tycko, B.: The history of cancer epigenetics. Nature Reviews. Cancer 4, 143–153 (2004)
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)
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)
Weisenberger, D., Van Den Berg, D., Pan, F., Berman, B., Laird, P.W.: Comprehensive dna methylation analysis on the illumina infinium assay platform (2008)
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)
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)
Cleveland, W.S.: LOWESS: A Program for Smoothing Scatterplots by Robust Locally Weighted Regression. The American Statistician 35(1) (1981)
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)
Laird, P.W.: Principles and challenges of genome-wide dna methylation analysis. Nature Reviews. Genetics 11, 191–203 (2010)
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)
R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2010) ISBN 3-900051-07-0
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)
Evan Johnson, W., Li, C., Rabinovic, A.: Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8, 118–127 (2007)
Langfelder, P., Horvath, S.: Wgcna: an r package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008)
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:10.1186/1752-0509-6-63
Du, P., Kibbe, W.A., Lin, S.M.: Lumi: a pipeline for processing illumina microarray. Bioinformatics 24, 1547–1548 (2008)
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 1.30.0
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)
Illumina. Normalization and Differential Analysis (2008)
Vapnik, V.N.: The Nature of Statistical Theory. In: Information Science and Statistics. Springer (1995)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001)
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)
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)
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)
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)
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)
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Colas, F., Houwing-Duistermaat, J.J. (2012). Case/Control Prediction from Illumina Methylation Microarray’s β and Two-Color Channels in the Presence of Batch Effects. In: Biganzoli, E., Vellido, A., Ambrogi, F., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2011. Lecture Notes in Computer Science(), vol 7548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35686-5_18
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DOI: https://doi.org/10.1007/978-3-642-35686-5_18
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