Batch effects and pathway analysis: Two potential perils in cancer studies involving DNA methylation array analysis

Cancer Epidemiology Biomarkers and Prevention

Volumn 22, Issue 6, 2013, Pages 1052-1060

  Harper, Kristin N ^a   Peters, Brandilyn A ^a   Gamble, Mary V ^a  

^a Department of Medicine   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARSENIC; DNA; DRINKING WATER;

ARTICLE; COMPUTER PROGRAM; CONTROLLED STUDY; CPG ISLAND; DNA METHYLATION; DNA MICROARRAY; HUMAN; HUMAN CELL; MICROARRAY ANALYSIS; PRIORITY JOURNAL; SIMULATION;

ALGORITHMS; ARSENIC; BATCH CELL CULTURE TECHNIQUES; CPG ISLANDS; DIETARY SUPPLEMENTS; DNA METHYLATION; EPIGENESIS, GENETIC; FOLIC ACID; GENOME, HUMAN; HUMANS; LEUKOCYTES, MONONUCLEAR; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; RANDOMIZED CONTROLLED TRIALS AS TOPIC; SOFTWARE;

EID: 84879015632     PISSN: 10559965     EISSN: None     Source Type: Journal    
DOI: 10.1158/1055-9965.EPI-13-0114     Document Type: Article

References (32)

1. Marsit C, Koestler D, Christensen B, Karagas M, Houseman E, Kelsey K. DNA methylation array analysis identifies profiles of blood-derived DNA methylation associated with bladder cancer. J Clin Oncol 2011;29:1133-9.
2. Teschendorff A, Menon U, Gentry-Maharaj A, Ramus S, Weisenberger D, Shen H, et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res 2010;20:440-6.
3. Hansen K, Timp W, Bravo H, Sabunciyan S, Langmead B, McDonald O, et al. Increased methylation variation in epigenetic domains across cancer types. Nat Genet 2011;43:768-75.
4. Breitling L, Yang R, Korn B, Burwinkel B, Brenner H. Tobacco-smoking- related differential DNA methylation: 27K discovery and replication. Am J Hum Genet 2011;88:450-7.
5. Leek J, Scharpf R, Bravo H, Simcha D, Langmead B, Johnson W, et al. Tackling the widespread and critical impact of batch effects in high throughput data. Nat Rev Genet 2010;11:733-9.
6. Sun Z, Chai H, Wu Y, White W, Donkena K, Klein C, et al. Batch effect correction for genome-wide methylation data with Illumina Infinium platform. BMC Med Genomics 2011;4:84.
7. Wang D, Zhang Y, Huang Y, Li P, Wang M, Wu R, et al. Comparison of different normalization assumptions for analyses of DNA methylation data from the cancer genome. Gene 2012;506:36-42.
8. Johnson W, Rabinovic A, Li C. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2006;8:118-27. (Pubitemid 44906106)
9. Yan L, Ma C, Wang D, Hu Q, Qin M, Conroy J, et al. OSAT: a tool for sample-to-batch allocations in genomics experiments. BMC Genomics 2012;13:689.
10. Baggerly K, Coombes K, Neeley E. Run batch effects potentially compromise the usefulness of genomic signatures for ovarian cancer. J Clin Oncol 2008;26:1186-7.
11. Dressman H, Berchuck A, Chan G, Zhai J, Bild A, Sayer R, et al. An integrated genomic-based approach to individualized treatment of patients with advanced-stage ovarian cancer. J Clin Oncol 2007;25: 517-25. (Pubitemid 350002958)
12. Spielman R, Bastone L, Burdick J, Morley M, Ewens W, Cheung V. Common genetic variants account for differences in gene expression among ethnic groups. Nat Genet 2007;39:226-31. (Pubitemid 46184354)
13. Akey J, Biswas S, Leek J, Storey J. On the design and analysis of gene expression studies in human populations. Nat Genet 2007;39:807-8. (Pubitemid 47014480)
14. Daily J, Scanfeld D, Pochet N, Roch KL, Plouffe D, Kamal M, et al. Distinct physiological states of Plasmodium falciparum in malaria infected patients. Nature 2007;450:1091-5. (Pubitemid 350273605)
15. Lemieux J, Feller A, Holmes C, Newbold C. Reply to Wirth et al.: in vivo profiles show continuous variation between two cellular populations. PNAS 2009;106:E71-E2.
16. Einstein F, Thompson R, Bhagat T, Fazzari M, Verma A, Barzilai N, et al. Cytosine methylation dysregulation in neonates following intrauterine growth restriction. PLoS ONE 2010;5:e8887.
17. Kim S, Kelly W, Fu A, Haines K, Hoffman A, Zheng T, et al. Genome-wide methylation analysis identifies involvement of TNF-alpha mediated cancer pathways in prostate cancer. Cancer Lett 2011;302:47-53.
18. Sadikovic B, Yoshimoto M, Al-Romaih K, Maire G, Zielenska M, Squire J. In vitro analysis of integrated global high-resolution DNA Methylation profiling with genomic imbalance and gene expression in osteosarcoma. PLoS One 2008;3:e2834.
19. Thompson R, Atzmon G, Gheorghe C, Liang H, Lowes C, Greally J, et al. Tissue-specific dysregulation of DNA methylation in aging. Aging Cell 2010;9:506-18.
20. Zhu Y, Stevens R, Hoffman A, Tjonneland A, Vogel U, Zheng T, et al. Epigenetic impact of long-term shiftwork: pilot evidence from circadian genes and whole-genome methylation analysis. Chronobiol Int 2011;28:852-61.
21. Lokk K, Vooder T, Kolde R, Välk K, Võsa U, Roosipuu R, et al. Methylation markers of early-stage non-small cell lung cancer. PLoS ONE 2012;7:e39813.
22. Novakovic B, Yuen R, Gordon L, Penaherrera M, Sharkey A, Moffett A, et al. Evidence for widespread changes in promoter methylation profile in human placenta in response to increasing gestational age and environmental/stochastic factors. BMC Genomics 2011; 12:529.
23. Leek J, Johnson W, Parker H, Jaffe A, Storey J. SVA: surrogate variable analysis. R package version 3.2.1. 2012.
24. Du P, Zhang X, Huang C, Jafari N, Kibbe W, Hou L, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 2010;11:587.
25. Illumina. Infinium Human Methylation 450 Bead Chip Kit. 2012; Available from: http://www.illumina.com/products/methylation-450-beadchip-kits.ilmn
26. Scherer A. Batch effects and noise in microarray experiments: sources and solutions. Hoboken, NJ: John Wiley & Sons; 2009.
27. Affymetrix. GeneChip Human Promoter 1.0R Array. [cited 2013 Mar 18]; Available from: http://www.affymetrix.com/estore/browse/products.jsp?productId= 131461#1-1.
28. Huang D, Sherman B, Lempicki R. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protoc 2009;4:44-57.
29. Houseman E, Accomando W, Koestler D, Christensen B, Marsit C, Nelson H, et al. DNA methylation arrays as surrogate measure of cell mixture distribution. BMC Bioinformatics 2012;13:86.
30. Liu Y, Aryee M, Padyukov L, Fallin M, Hesselberg E, Runarsson A, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol 2013;31:142-7.
31. Chen Y, Choufani S, Ferreira J, Grafodatskaya D, Butcher D, Weksberg R. Sequence overlap between autosomal and sex-linked probes on the Illumina Human Methylation 27 microarray. Genomics 2011;97:214-22.
32. Houseman E. Biostatisical methods in epigenetic epidemiology. In: Michels K, editor. Epigenetic epidemiology. New York, NY: Springer Verlag; 2012. p. 57-76.