InterSIM

InterSIM simulates multiple interrelated omic datasets—DNA methylation, mRNA gene expression, and protein expression—to enable integrative genomics analyses such as molecular subtype classification in cancer.

Key Features:

• Realistic multi-omic simulation: Generates datasets that mimic intra- and inter-omic relationships, including pathway-level correlations and cross-layer effects such as gene silencing between DNA methylation and gene expression.
• Application to cancer genomics: Leverages real-world data patterns from the TCGA ovarian cancer study to reflect biological complexities observed in cancer research.
• Method comparison and benchmarking: Produces related datasets suitable for comparing the performance of new integrative bioinformatics methods against established approaches.
• Visualization output: Generates visual representations such as heatmaps to aid assessment and interpretation of simulated datasets.
• Implementation: Implemented in R (statistical programming language).

Scientific Applications:

• Integrative genomics: Supports development and evaluation of methods that combine DNA methylation, mRNA expression, and protein expression data.
• Systems biology: Enables study of intra- and inter-omic regulatory relationships and their effects on molecular phenotypes.
• Method development and validation: Provides realistic test data for benchmarking and validating new integrative analytical methods.
• Cancer molecular subtype analysis: Facilitates simulation-based studies relevant to molecular subtype classification across cancer histologies using TCGA-derived patterns.

Methodology:

Implemented in R; simulates DNA methylation, mRNA gene expression, and protein expression preserving intra- and inter-omic correlations including gene silencing; uses TCGA ovarian cancer study patterns as a template and produces heatmaps.