Detecting differentially methylated loci for multiple treatments based on high-throughput methylation data

Data-Driven-Based Approach to Identifying Differentially Methylated Regions Using Modified 1D Ising Model

Background

DNA methylation is essential for regulating gene expression, and the changes of DNA methylation status are commonly discovered in disease. Therefore, identification of differentially methylation patterns, especially differentially methylated regions (DMRs), in two different groups is important for understanding the mechanism of complex diseases. Few tools exist for DMR identification through considering features of methylation data, but there is no comprehensive integration of the characteristics of DNA methylation data in current methods.

Results

Accounting for the characteristics of methylation data, such as the correlation characteristics of neighboring CpG sites and the high heterogeneity of DNA methylation data, we propose a data-driven approach for DMR identification through evaluating the energy of single site using modified 1D Ising model. Applied to both simulated and publicly available datasets, our approach is compared with other popular methods in terms of performance. Simulated results show that our method is more sensitive than competing methods. Applied to the real data, our method can identify more common DMRs than DMRcate, ProbeLasso, and Wang's methods with a high overlapping ratio. Also, the necessity of integrating the heterogeneity and correlation characteristics in identifying DMR is shown through comparing results with only considering mean or variance signals and without considering relationship of neighboring CpG sites, respectively. Through analyzing the number of DMRs identified in real data located in different genomic regions, we find that about 90% DMRs are located in CGI which always regulates the expression of genes. It may help us understand the functional effect of DNA methylation on disease.

Simultaneous statistical inference for epigenetic data

Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.

Identification of functionally methylated regions based on discriminant analysis through integrating methylation and gene expression data

DNA methylation is essential not only in cellular differentiation but also in diseases.

A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study

Background

Combining information from different studies is an important and useful practice in bioinformatics, including genome-wide association study, rare variant data analysis and other set-based analyses. Many statistical methods have been proposed to combine p-values from independent studies. However, it is known that there is no uniformly most powerful test under all conditions; therefore, finding a powerful test in specific situation is important and desirable.

Results

In this paper, we propose a new statistical approach to combining p-values based on gamma distribution, which uses the inverse of the p-value as the shape parameter in the gamma distribution.

Conclusions

Simulation study and real data application demonstrate that the proposed method has good performance under some situations.