Efficient gene set analysis for DNA methylation addressing probe dependency and bias

Abstract

Motivation Gene Set Enrichment Analysis (GSEA) is widely used to interpret DNA methylation data by associating differentially methylated sites with biological pathways. However, existing GSEA methods struggle with several challenges in methylation data, including probe dependency, probe number bias, and the complexity of gene-probe mapping. These limitations can lead to biased enrichment results, reduced statistical power, and computational inefficiencies. Results We introduce gsGene and gsPG, two novel GSEA methods specifically designed for DNA methylation data. gsGene aggregates association signals at gene level while correcting for probe dependency and probe number bias, enabling more biologically meaningful enrichment analysis. gsPG takes a different approach by conducting gene set enrichment using summary statistics for independent probe groups based on gene annotation, mitigating biases from multi-mapping probes. Both methods improve computational efficiency, enhance statistical power, and effectively control type I error rates. Comprehensive evaluations in two large datasets demonstrate superior performance compared to existing methods. Furthermore, we propose a novel beta distribution fitting strategy to improve enrichment P-value estimation, providing a computationally efficient alternative to traditional permutation-based gene set methods. Availability and implementation These methods are implemented in the R package dmGsea, which is freely available on GitHub and Bioconductor (DOI: 10.18129/B9.bioc.dmGsea). The package supports Illumina 450K, EPIC, and mouse methylation arrays and can be extended to other omics data with user-provided probe-to-gene mapping annotations.