OmicsARules

OmicsARules applies association rule mining to integrate multi-omics datasets and identify concurrent gene alterations across DNA methylation and RNA-seq to uncover mechanistic connections in biological regulation.

Key Features:

• Association Rules Mining: Uses association rules mining to identify recurrent and associated patterns across diverse omics data types.
• Lamda3 Measure: Incorporates the Lamda3 rule-interestingness measure to evaluate and prioritize biologically significant association rules.
• Multi-Omics Data Integration: Supports integration of data from various sequencing platforms and samples, enabling analysis across omics layers such as DNA methylation and RNA-seq.
• Visualization Capabilities: Employs R packages arules for association rule mining and ggplot2 for visualization to illustrate mechanistic connections between omics datasets.

Scientific Applications:

• Cancer research: Applied to DNA methylation and RNA-seq datasets from breast invasive carcinoma (BRCA), esophageal carcinoma (ESCA), and lung adenocarcinoma (LUAD) to uncover mechanistic connections between methylation and transcription and to elucidate complex regulatory networks in oncology.

Methodology:

Association rule mining of frequently altered genes using the arules R package with rule-interestingness evaluated by the Lamda3 measure, applied to integrated DNA methylation and RNA-seq datasets and visualized with ggplot2.