4.7 Article

Differential methylation values in differential methylation analysis

期刊

BIOINFORMATICS
卷 35, 期 7, 页码 1094-1097

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OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty778

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  1. National Institute of Environmental Health Sciences of the National Institutes of Health [P30-ES006096]

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Motivation Both -value and M-value have been used as metrics to measure methylation levels. The M-value is more statistically valid for the differential analysis of methylation levels. However, the -value is much more biologically interpretable and needs to be reported when M-value method is used for conducting differential methylation analysis. There is an urgent need to know how to interpret the degree of differential methylation from the M-value. In M-value linear regression model, differential methylation M-value M can be easily obtained from the coefficient estimate, but it is not straightforward to get the differential methylation -value, since it cannot be obtained from the coefficient alone. Results To fill the gap, we have built a bridge to connect the statistically sound M-value linear regression model and the biologically interpretable . In this article, three methods were proposed to calculate differential methylation values, from M-value linear regression model and compared with the directly obtained from -value linear regression model. We showed that under the condition that M-value linear regression model is correct, the method M-model-coef is the best among the four methods. M-model-M-mean method works very well too. If the coefficients 0,2,...p are not given (as MethLAB' package), the M-model-M-mean method should be used. The directly obtained from -value linear regression model can give very biased results, especially when M-values are not in (-2, 2) or -values are not in (0.2, 0.8). Availability and implementation The dataset for example is available at the National Center for Biotechnology Information Gene Expression Omnibus repository, GSE104778. Supplementary information Supplementary data are available at Bioinformatics online.

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