Dataset Information

Differential methylation values in differential methylation analysis.

ABSTRACT: 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.

SUBMITTER: Xie C

PROVIDER: S-EPMC6449748 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Differential methylation values in differential methylation analysis.

Xie Changchun C Leung Yuet-Kin YK Chen Aimin A Long Ding-Xin DX Hoyo Catherine C Ho Shuk-Mei SM

Bioinformatics (Oxford, England) 20190401 7

<h4>Motivation</h4>Both β-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 β-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, ...[more]

PMID: 30184051

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Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .

Dataset Information

Differential methylation values in differential methylation analysis.

Publications

Differential methylation values in differential methylation analysis.

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