High-dimensional genomic data bias correction and data integration using MANCIE

Nature Communications

Volumn 7, Issue , 2016, Pages

  Zang, Chongzhi ^a,b   Wang, Tao ^c   Deng, Ke ^d   Li, Bo ^a,b,e   Hu, Sheng'en ^f   Qin, Qian ^f   Xiao, Tengfei ^a,b   Zhang, Shihua ^g   Meyer, Clifford A ^a,b   He, Housheng Hansen ^a,b,h   Brown, Myles ^b   Liu, Jun S ^e   Xie, Yang ^c   Liu, X Shirley ^a,b  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b DANA FARBER CANCER INSTITUTE   (United States)

^c UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

^d TSINGHUA UNIVERSITY   (China)

^e HARVARD UNIVERSITY   (United States)

^f TONGJI UNIVERSITY   (China)

^g CHINESE ACADEMY OF SCIENCES   (China)

^h UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN ANALYSIS; CANCER; CELLS AND CELL COMPONENTS; CORRECTION; EXPERIMENTAL STUDY; GENOME; MATRIX; MOLECULAR ANALYSIS; PRINCIPAL COMPONENT ANALYSIS;

ANALYTIC METHOD; ARTICLE; BINDING SITE; BREAST CANCER; COMPARATIVE STUDY; CONTROLLED STUDY; COPY NUMBER VARIATION; DNA BASE COMPOSITION; ENHANCER REGION; GENE EXPRESSION; GENETIC ANALYSIS; HUMAN; HUMAN CELL; INTRON; MATRIX ANALYSIS AND NORMALIZATION BY CONCORDANT INFORMATION ENHANCEMENT; NEXT GENERATION SEQUENCING; NOISE REDUCTION; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PROMOTER REGION; TRANSCRIPTION INITIATION SITE; GENETIC DATABASE; GENETICS; GENOMICS; NEOPLASM; PROCEDURES; SOFTWARE; STATISTICS; TUMOR CELL LINE;

CELL LINE, TUMOR; DATABASES, GENETIC; GENOMICS; HUMANS; NEOPLASMS; SOFTWARE; STATISTICS AS TOPIC;

EID: 84963960017     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms11305     Document Type: Article

References (35)

1. Thurman, R. E. et al. The accessible chromatin landscape of the human genome. Nature 488, 75-82 (2013).
2. The Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543-550 (2014).
3. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346-352 (2012).
4. Barretina, J. et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603-307 (2013).
5. Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733-739 (2010).
6. Meyer, C. A. & Liu, X. S. Identifying and mitigating bias in next-generation sequencing methods for chromatin biology. Nat. Rev. Genet. 15, 1-13 (2014).
7. Dohm, J. C., Lottaz, C., Borodina, T. & Himmelbauer, H. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic. Acids. Res. 36, e105-e105 (2008).
8. Zou, H., HASTIE, T. & Tibshirani, R. Sparse principal component analysis. J. Comput. Graph. Stat. 15, 265-286 (2006).
9. Leek, J. T. & Storey, J. D. capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, e161-12 (2007).
10. Wang, B. et al. Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333-337 (2014).
11. Gross, D. S. & Garrard, W. T. Nuclease hypersensitive sites in chromatin. Annu. Rev. Biochem. 57, 159-197 (1988).
12. Felsenfeld, G. & Groudine, M. Controlling the double helix. Nature 421, 448-453 (2003).
13. Sabo, P. J. et al. Genome-scale mapping of DNase I sensitivity in vivo using tiling DNA microarrays. Nat. Methods 3, 511-518 (2006).
14. Heintzman, N. D. et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat. Genet. 39, 311-318 (2007).
15. Boyle, A. P. et al. High-resolution mapping and characterization of open chromatin across the genome. Cell 132, 311-322 (2008).
16. Stergachis, A. B. et al. Developmental fate and cellular maturity encoded in human regulatory DNA Landscapes. Cell 154, 888-903 (2013).
17. Luyten, A., Zang, C., Liu, X. S. & Shivdasani, R. A. Active enhancers are delineated de novo during hematopoiesis, with limited lineage fidelity among specified primary blood cells. Genes Dev. 28, 1827-1839 (2014).
18. Rand, W. M. Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66, 846-850 (1971).
19. Ouyang, W. et al. The Ets transcription factor ERM is Th1-specific and induced by IL-12 through a Stat4-dependent pathway. Proc. Natl Acad. Sci. USA 96, 3888-3893 (1999).
20. Chang, H.-C. et al. The transcription factor PU.1 is required for the development of IL-9-producing T cells and allergic inflammation. Nat. Immunol. 11, 527-534 (2010).
21. Beer, D. G. et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 8, 816-824 (2002).
22. Guo, L. Constructing molecular classifiers for the accurate prognosis of lung adenocarcinoma. Clin. Cancer Res. 12, 3344-3354 (2006).
23. Larsen, J. E. et al. Gene expression signature predicts recurrence in lung adenocarcinoma. Clin. Cancer Res. 13, 2946-2954 (2007).
24. Roepman, P. et al. An immune response enriched 72-gene prognostic profile for early-stage non-small-cell lung cancer. Clin. Cancer Res. 15, 284-290 (2009).
25. Xie, Y. et al. Robust gene expression signature from formalin-fixed paraffinembedded samples predicts prognosis of non-small-cell lung cancer patients. Clin. Cancer Res. 17, 5705-5714 (2011).
26. Lu, Y. et al. A gene expression signature predicts survival of patients with stage i non-small cell lung cancer. PLoS Med. 3, 2229-2243 (2006).
27. Bair, E. & Tibshirani, R. Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol. 2, e108 (2004).
28. Andersen, P. K. & Gill, R. D. Cox's regression model for counting processes: a large sample study. Ann. Stat. 10, 1100-1120 (1982).
29. Yang, W. et al. Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic. Acids Res. 41, D955-D961 (2012).
30. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137-R139 (2008).
31. Seok, J., Xu, W., Gao, H., Davis, R. W. & Xiao, W. JETTA: junction and exon toolkits for transcriptome analysis. Bioinformatics 28, 1274-1275 (2012).
32. Lupien, M. et al. FoxA1 translates epigenetic signatures into enhancer-driven lineage-specific transcription. Cell 132, 958-970 (2008).
33. Liu, T. et al. Cistrome: an integrative platform for transcriptional regulation studies. Genome Biol. 12, R83 (2011).
34. Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 58, 267-288 (1996).
35. Cox, D. R. Regression models and life-tables. J. R. Stat. Soc. Ser. B Stat. Methodol. 34, 187-220 (1972).