Reference-free cell mixture adjustments in analysis of DNA methylation data.
Reference-free cell mixture adjustments in analysis of DNA methylation data.
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DOI：
10.1093/bioinformatics/btu029
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发表时间：
2014-05-15
期刊：
影响因子：
--
通讯作者：
Marsit CJ
中科院分区：
文献类型：
--
作者：
Houseman EA;Molitor J;Marsit CJ
Motivation: Recently there has been increasing interest in the effects of cell mixture on the measurement of DNA methylation, specifically the extent to which small perturbations in cell mixture proportions can register as changes in DNA methylation. A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies. However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect. For some tissues such as placenta, saliva, adipose or tumor tissue, the relevant underlying cell types may not be known. Results: We propose a method for conducting epigenome-wide association studies analysis when a reference dataset is unavailable, including a bootstrap method for estimating standard errors. We demonstrate via simulation study and several real data analyses that our proposed method can perform as well as or better than methods that make explicit use of reference datasets. In particular, it may adjust for detailed cell type differences that may be unavailable even in existing reference datasets. Availability and implementation: Software is available in the R package RefFreeEWAS. Data for three of four examples were obtained from Gene Expression Omnibus (GEO), accession numbers GSE37008, GSE42861 and GSE30601, while reference data were obtained from GEO accession number GSE39981. Contact: andres.houseman@oregonstate.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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DOI：
10.1111/j.1467-9868.2004.00439.x
发表时间：
2004-01-01
影响因子：
5.8
作者：
Storey, JD;Taylor, JE;Siegmund, D
通讯作者：
Siegmund, D
DOI：
10.1073/pnas.1121249109
发表时间：
2012-10-16
影响因子：
11.1
作者：
Lam, Lucia L.;Emberly, Eldon;Kobor, Michael S.
通讯作者：
Kobor, Michael S.
通过替代变量分析捕获基因表达研究中的异质性。
影响因子：
4.5
作者：
Leek, Jeffrey T.;Storey, John D.
通讯作者：
Storey, John D.
DOI：
10.1158/1055-9965.epi-12-0361
发表时间：
2012-08
期刊：
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
影响因子：
--
作者：
Koestler DC;Marsit CJ;Christensen BC;Accomando W;Langevin SM;Houseman EA;Nelson HH;Karagas MR;Wiencke JK;Kelsey KT
通讯作者：
Kelsey KT
影响因子：
64.5
作者：
Lee, TI;Jenner, RG;Young, RA
通讯作者：
Young, RA