Removing unwanted variation in a differential methylation analysis of Illumina Human Methylation 450 array data

Nucleic Acids Research

Volumn 43, Issue 16, 2015, Pages

  Maksimovic, Jovana ^a   Gagnon Bartsch, Johann A ^b   Speed, Terence P ^b,c,d   Oshlack, Alicia ^a,d  

^a ROYAL CHILDREN'S HOSPITAL   (Australia)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c WALTER AND ELIZA HALL INSTITUTE OF MEDICAL RESEARCH   (Australia)

^d UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC METHOD; ANALYTICAL PARAMETERS; ARTICLE; DATA ANALYSIS; DATA PROCESSING; DNA METHYLATION; FACTORIAL ANALYSIS; GENE EXPRESSION; GENETIC ANALYZER; GENETIC SCREENING; INTERMETHOD COMPARISON; PRIORITY JOURNAL; ADOLESCENT; AGING; DNA MICROARRAY; EVALUATION STUDY; FEMALE; GENETIC VARIATION; GENETICS; HUMAN; INFANT; MALE; NEOPLASM; NEWBORN; PROCEDURES; SMOKING; VERY ELDERLY;

ADOLESCENT; AGED, 80 AND OVER; AGING; DNA METHYLATION; FEMALE; GENETIC VARIATION; HUMANS; INFANT; INFANT, NEWBORN; MALE; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SMOKING;

EID: 84942243321     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkv526     Document Type: Article

References (62)

1. Rakyan, V., Down, T. and Thorne, N. (2008) An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs). Genome Res., 18, 1518-1529.
2. Esteller, M. (2007) Cancer epigenomics: DNA methylomes and histone-modification maps. Nat. Rev. Genet., 8, 286-298.
3. Koga, Y., Pelizzola, M. and Cheng, E. (2009) Genome-wide screen of promoter methylation identifies novel markers in melanoma. Genome Res., 19, 1462-1470.
4. Irizarry, R.A., Ladd-Acosta, C., Wen, B., Wu, Z., Montano, C., Onyango, P., Cui, H., Gabo, K., Rongione, M., Webster, M. et al. (2009) The human colon cancer methylome shows similar hypo-and hypermethylation at conserved tissue-specific CpG island shores. Nat. Genet., 41, 178-186.
5. Toperoff, G., Aran, D., Kark, J.D., Rosenberg, M., Dubnikov, T., Nissan, B., Wainstein, J., Friedlander, Y., Levy-Lahad, E., Glaser, B. et al. (2012) Genome-wide survey reveals predisposing diabetes type 2-related DNA methylation variations in human peripheral blood. Hum. Mol. Genet., 21, 371-383.
6. Liu, Y., Aryee, M.J., Padyukov, L., Fallin, M.D., Hesselberg, E., Runarsson, A., Reinius, L., Acevedo, N., Taub, M., Ronninger, M. et al. (2013) Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat. Biotechnol., 31, 142-147.
7. Shenker, N.S., Polidoro, S., van Veldhoven, K., Sacerdote, C., Ricceri, F., Birrell, M.A., Belvisi, M.G., Brown, R., Vineis, P. and Flanagan, J.M. (2013) Epigenome-wide association study in the European Prospective Investigation into Cancer and Nutrition (EPIC-Turin) identifies novel genetic loci associated with smoking. Hum. Mol. Genet., 22, 843-851.
8. Jaffe, A.E. and Irizarry, R.A. (2014) Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol., 15, R31.
9. Houseman, E.A., Molitor, J. and Marsit, C.J. (2014) Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics, 30, 1431-1439.
10. Zou, J., Lippert, C., Heckerman, D., Aryee, M. and Listgarten, J. (2014) Epigenome-wide association studies without the need for cell-type composition. Nat. Methods, 11, 309-311.
11. Adalsteinsson, B.T., Gudnason, H., Aspelund, T., Harris, T.B., Launer, L.J., Eiriksdottir, G., Smith, A.V. and Gudnason, V. (2012) Heterogeneity in white blood cells has potential to confound DNA methylation measurements. PLoS One, 7, e46705.
12. Bock, C. (2012) Analysing and interpreting DNA methylation data. Nat. Rev. Genet., 13, 705-19.
13. Houseman, E.A., Accomando, W.P., Koestler, D.C., Christensen, B.C., Marsit, C.J., Nelson, H.H., Wiencke, J.K. and Kelsey, K.T. (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics, 13, 86.
14. Lam, L.L., Emberly, E., Fraser, H.B., Neumann, S.M., Chen, E., Miller, G.E. and Kobor, M.S. (2012) Factors underlying variable DNA methylation in a human community cohort. Proc. Natl. Acad. Sci. U.S.A., 109(Supp. 2), 17253-17260.
15. Reinius, L.E., Acevedo, N., Joerink, M., Pershagen, G., Dahlén, S.-E., Greco, D., Söderhäll, C., Scheynius, A. and Kere, J. (2012) Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS One, 7, e41361.
16. Leek, J.T., Scharpf, R.B., Bravo, H.C., Simcha, D., Langmead, B., Johnson, W.E., Geman, D., Baggerly, K. and Irizarry, R.A. (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet., 11, 733-739.
17. Scherer, A. (2009) Batch effects and noise in microarray experiments: Sources and solutions. Wiley, Chichester.
18. Johnson, W., Li, C. and Rabinovic, A. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118-127.
19. Leek, J.T. and Storey, J.D. (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet., 3, 1724-1735.
20. Leek, J.T. and Storey, J.D. (2008) A general framework for multiple testing dependence. Proc. Natl. Acad. Sci. U.S.A., 105, 18718-18723.
21. Teschendorff, A.E., Zhuang, J. and Widschwendter, M. (2011) Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies. Bioinformatics, 27, 1496-1505.
22. Gagnon-Bartsch, J.A. and Speed, T.P. (2012) Using control genes to correct for unwanted variation in microarray data. Biostatistics, 13, 539-552.
23. Gagnon-Bartsch, J.A., Jacob, L. and Speed, T.P. (2013) Removing Unwanted Variation from High Dimensional Data with Negative Controls. Tech. Rep. 820, Department of Statistics, University of California, Berkeley, CA.
24. De Livera, A. and Dias, D. (2012) Normalizing and integrating metabolomics data. Anal. Chem., 84, 10768-10776.
25. Harper, K.N., Peters, B.A. and Gamble, M.V. (2013) Batch effects and pathway analysis: two potential perils in cancer studies involving DNA methylation array analysis. Cancer Epidemiol. Biomarkers Prev., 22, 1052-1060.
26. Marabita, F., Almgren, M., Lindholm, M.E., Ruhrmann, S., Fagerström-Billai, F., Jagodic, M., Sundberg, C.J., Ekström, T.J., Teschendorff, A.E., Tegnér, J. et al. (2013) An evaluation of analysis pipelines for DNA methylation profiling using the Illumina Human Methylation450 Bead Chip platform. Epigenetics, 8, 333-346.
27. Morris, T.J., Butcher, L.M., Feber, A., Teschendorff, A.E., Chakravarthy, A.R., Wojdacz, T.K. and Beck, S. (2014) ChAMP: 450k chip analysis methylation pipeline. Bioinformatics, 30, 428-430.
28. Jaffe, A.E., Murakami, P., Lee, H., Leek, J.T., Fallin, M.D., Feinberg, A.P. and Irizarry, R.A. (2012) Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int. J. Epidemiol., 41, 200-209.
29. Sun, Z., Chai, H.S., Wu, Y., White, W.M., Donkena, K.V., Klein, C.J., Garovic, V.D., Therneau, T.M. and Kocher, J.-P.A. (2011) Batch effect correction for genome-wide methylation data with Illumina Infinium platform. BMC Med. Genomics, 4, 84.
30. R Core Team. (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, http://www.R-project.org/.
31. Aryee, M.J., Jaffe, A.E., Corrada-Bravo, H., Ladd-Acosta, C., Feinberg, A.P., Hansen, K.D. and Irizarry, R.A. (2014) Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics, 30, 1363-1369.
32. Chen, Y., Lemire, M., Choufani, S., Butcher, D.T., Grafodatskaya, D., Zanke, B.W., Gallinger, S., Hudson, T.J. and Weksberg, R. (2013) Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium Human Methylation450 microarray. Epigenetics, 8, 203-209.
33. Du, P., Zhang, X., Huang, C.-C., Jafari, N., Kibbe, W.A., Hou, L. and Lin, S.M. (2010) Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics, 11, 587.
34. Smyth, G.K. (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol., 3, Article 3.
35. Smyth, G.K., Michaud, J. and Scott, H.S. (2005) Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics, 21, 2067-2075.
36. Johnson, W.E., Li, C. and Rabinovic, A. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118-127.
37. Leek, J.T., Johnson, W.E., Parker, H.S., Jaffe, A.E. and Storey, J.D. (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics, 28, 882-883.
38. Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B, 57, 289-500.
39. Martino, D., Loke, Y.J., Gordon, L., Ollikainen, M., Cruickshank, M.N., Saffery, R. and Craig, J.M. (2013) Longitudinal, genome-scale analysis of DNA methylation in twins from birth to 18 months of age reveals rapid epigenetic change in early life and pair-specific effects of discordance. Genome Biol., 14, R42.
40. Martino, D., Maksimovic, J., Joo, J.-H., Prescott, S.L. and Saffery, R. (2012) Genome-scale profiling reveals a subset of genes regulated by DNA methylation that program somatic T-cell phenotypes in humans. Genes Immun., 13, 388-398.
41. Heyn, H., Li, N., Ferreira, H.J., Moran, S., Pisano, D.G., Gomez, A. and Diez, J. (2012) Distinct DNA methylomes of newborns and centenarians. PNAS, 109, 10522-10527.
42. Maksimovic, J., Gordon, L. and Oshlack, A. (2012) SWAN: Subset-quantile within array normalization for illumina infinium Human Methylation450 Bead Chips. Genome Biol., 13, R44.
43. Touleimat, N. and Tost, J. (2012) Complete pipeline for Infinium Human Methylation 450K Bead Chip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics, 4, 325-341.
44. Cruickshank, M.N., Oshlack, A., Theda, C., Davis, P.G., Martino, D., Sheehan, P., Dai, Y., Saffery, R., Doyle, L.W. and Craig, J.M. (2013) Analysis of epigenetic changes in survivors of preterm birth reveals the effect of gestational age and evidence for a long term legacy. Genome Med., 5, 96.
45. Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y. et al. (2013) Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell, 49, 359-367.
46. Zeilinger, S., Kühnel, B., Klopp, N., Baurecht, H., Kleinschmidt, A., Gieger, C., Weidinger, S., Lattka, E., Adamski, J., Peters, A. et al. (2013) Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS One, 8, e63812.
47. Fortin, J., Labbe, A., Lemire, M. and Zanke, B. (2014) Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol., 15, 1-17.
48. Zheng, X., Zhao, Q., Wu, H.-J., Li, W., Wang, H., Meyer, C.A., Qin, Q., Xu, H., Zang, C., Jiang, P. et al. (2014) Methyl Purify: tumor purity deconvolution and differential methylation detection from single tumor DNA methylomes. Genome Biol., 15, 419.
49. Sadedin, S.P., Pope, B. and Oshlack, A. (2012) Bpipe: a tool for running and managing bioinformatics pipelines. Bioinformatics, 28, 1525-1526.
50. Feng, H., Conneely, K.N. and Wu, H. (2014) A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res., 42, e69.
51. Bird, A. (1985) CpG-rich islands and the function of DNA methylation. Nature, 321, 209-213.
52. Varley, K.E., Gertz, J., Bowling, K.M., Parker, S.L., Reddy, T.E., Pauli-Behn, F., Cross, M.K., Williams, B., Stamatoyannopoulos, J.A., Crawford, G.E. et al. (2013) Dynamic DNA methylation across diverse human cell lines and tissues. Genome Res., 23, 555-567.
53. Ghosh, S., Yates, A.J., Frühwald, M.C., Miecznikowski, J.C., Plass, C. and Smiraglia, D. (2010) Tissue specific DNA methylation of CpG islands in normal human adult somatic tissues distinguishes neural from non-neural tissues. Epigenetics, 5, 527-538.
54. Touleimat, N. and Tost, J. (2012) Complete pipeline for Infinium® Human Methylation 450K Bead Chip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics, 4, 325-341.
55. Heyn, H., Li, N. and Ferreira, H. (2012) Distinct DNA methylomes of newborns and centenarians. Proc. Natl. Acad. Sci. U.S.A., 109, 10522-10527.
56. Bolstad, B.M., Irizarry, R.A., Astrand, M. and Speed, T.P. (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19, 185-193.
57. Irizarry, R.A., Hobbs, B., Collin, F., Beazer-Barclay, Y.D., Antonellis, K.J., Scherf, U. and Speed, T.P. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4, 249-264.
58. Smyth, G.K. and Speed, T. (2003) Normalization of cDNA microarray data. Methods, 31, 265-273.
59. Joubert, B. and Håberg, S. (2012) 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy. Environ. Health Perspect., 120, 1425-1431.
60. Shenker, N.S., Polidoro, S., van Veldhoven, K., Sacerdote, C., Ricceri, F., Birrell, M.A., Belvisi, M.G., Brown, R., Vineis, P. and Flanagan, J.M. (2013) Epigenome-wide association study in the European Prospective Investigation into Cancer and Nutrition (EPIC-Turin) identifies novel genetic loci associated with smoking. Hum. Mol. Genet., 22, 843-851.
61. Dogan, M.V., Shields, B., Cutrona, C., Gao, L., Gibbons, F.X., Simons, R., Monick, M., Brody, G.H., Tan, K., Beach, S.R.H. et al. (2014) The effect of smoking on DNA methylation of peripheral blood mononuclear cells from African American women. BMC Genomics, 15, 151.
62. Jones, P.A. (1986) DNA methylation and cancer. Cancer Res., 46, 461-466.