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An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies.

ABSTRACT: BACKGROUND:Many different methods exist to adjust for variability in cell-type mixture proportions when analyzing DNA methylation studies. Here we present the result of an extensive simulation study, built on cell-separated DNA methylation profiles from Illumina Infinium 450K methylation data, to compare the performance of eight methods including the most commonly used approaches. RESULTS:We designed a rich multi-layered simulation containing a set of probes with true associations with either binary or continuous phenotypes, confounding by cell type, variability in means and standard deviations for population parameters, additional variability at the level of an individual cell-type-specific sample, and variability in the mixture proportions across samples. Performance varied quite substantially across methods and simulations. In particular, the number of false positives was sometimes unrealistically high, indicating limited ability to discriminate the true signals from those appearing significant through confounding. Methods that filtered probes had consequently poor power. QQ plots of p values across all tested probes showed that adjustments did not always improve the distribution. The same methods were used to examine associations between smoking and methylation data from a case-control study of colorectal cancer, and we also explored the effect of cell-type adjustments on associations between rheumatoid arthritis cases and controls. CONCLUSIONS:We recommend surrogate variable analysis for cell-type mixture adjustment since performance was stable under all our simulated scenarios.

SUBMITTER: McGregor K

PROVIDER: S-EPMC4855979 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies.

McGregor Kevin K Bernatsky Sasha S Colmegna Ines I Hudson Marie M Pastinen Tomi T Labbe Aurélie A Greenwood Celia M T CM

Genome biology 20160503

<h4>Background</h4>Many different methods exist to adjust for variability in cell-type mixture proportions when analyzing DNA methylation studies. Here we present the result of an extensive simulation study, built on cell-separated DNA methylation profiles from Illumina Infinium 450K methylation data, to compare the performance of eight methods including the most commonly used approaches.<h4>Results</h4>We designed a rich multi-layered simulation containing a set of probes with true associations ...[more]

PMID: 27142380

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Project description:BACKGROUND:DNA methylation offers an excellent example for elucidating how epigenetic information affects gene expression. ? values and M values are commonly used to quantify DNA methylation. Statistical methods applicable to DNA methylation data analysis span a number of approaches such as Wilcoxon rank sum test, t-test, Kolmogorov-Smirnov test, permutation test, empirical Bayes method, and bump hunting method. Nonetheless, selection of an optimal statistical method can be challenging when different methods generate inconsistent results from the same data set. RESULTS:We compared six statistical approaches relevant to DNA methylation microarray analysis in terms of false discovery rate control, statistical power, and stability through simulation studies and real data examples. Observable differences were noticed between ? values and M values only when methylation levels were correlated across CpG loci. For small sample size (n=3 or 6 in each group), both the empirical Bayes and bump hunting methods showed appropriate FDR control and the highest power when methylation levels across CpG loci were independent. Only the bump hunting method showed appropriate FDR control and the highest power when methylation levels across CpG sites were correlated. For medium (n=12 in each group) and large sample sizes (n=24 in each group), all methods compared had similar power, except for the permutation test whenever the proportion of differentially methylated loci was low. For all sample sizes, the bump hunting method had the lowest stability in terms of standard deviation of total discoveries whenever the proportion of differentially methylated loci was large. The apparent test power comparisons based on raw p-values from DNA methylation studies on ovarian cancer and rheumatoid arthritis provided results as consistent as those obtained in the simulation studies. Overall, these results provide guidance for optimal statistical methods selection under different scenarios. CONCLUSIONS:For DNA methylation studies with small sample size, the bump hunting method and the empirical Bayes method are recommended when DNA methylation levels across CpG loci are independent, while only the bump hunting method is recommended when DNA methylation levels are correlated across CpG loci. All methods are acceptable for medium or large sample sizes.

Dataset Information

An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies.

Publications

An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies.

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