dmGsea

dmGsea provides efficient gene set enrichment analysis specifically for DNA methylation data, addressing probe dependency and varying probe numbers per gene.

Key Features:

• Probe dependency correction: Corrects for probe dependency bias in DNA methylation data during gene set enrichment analysis.
• Probe number bias correction: Addresses bias arising from varying probe numbers per gene.
• gsGene method: Aggregates association signals at gene level while correcting for probe dependency and probe number bias.
• gsPG method: Conducts gene set enrichment using summary statistics for independent probe groups based on gene annotation, mitigating biases from multi-mapping probes.
• Beta distribution fitting: Implements a novel beta distribution fitting strategy to improve enrichment P-value estimation as a computationally efficient alternative to traditional permutation-based methods.
• Array platform support: Supports Illumina 450K, EPIC, and mouse methylation arrays.
• Custom annotation support: Can be extended to other omics data with user-provided probe-to-gene mapping annotations.

Scientific Applications:

• DNA methylation pathway analysis: Interprets DNA methylation data by associating differentially methylated sites with biological pathways.
• Large-scale epigenomic studies: Enables computationally efficient gene set enrichment analysis for large-scale DNA methylation datasets.
• Cross-platform omics analysis: Applies to other omics data types through custom probe-to-gene mapping annotations.

Methodology:

The gsGene method aggregates association signals at gene level while correcting for probe dependency and probe number bias. The gsPG method conducts gene set enrichment using summary statistics for independent probe groups based on gene annotation. A novel beta distribution fitting strategy improves enrichment P-value estimation.