graphsim

graphsim simulates gene expression data from graph-structured biological pathways by sampling multivariate normal distributions to model pathway-driven gene correlations for transcriptomic analyses.

Key Features:

• Simulation from Graph Structures: Graphsim uses graph-based representations of biological pathways to capture interdependencies and correlations among genes.
• Multivariate Normal Distribution Sampling: Simulated gene expression is obtained by sampling from a multivariate normal distribution derived from the pathway graph structure.
• Compatibility with igraph: Accepts graph objects described using the igraph package for pathway representation.
• Versatile Statistical Framework: Provides a statistical framework to generate correlated gene expression data that reflect pathway relationships.

Scientific Applications:

• Interpretation of Genomics Studies: Incorporates pathway information to explore regulatory mechanisms and gene network dynamics in transcriptomic data.
• Method Development and Comparison: Produces simulated datasets for validation and benchmarking of analytical methods that integrate gene expression with biological pathways.

Methodology:

Biological pathways are represented as graphs using the igraph package; a multivariate normal distribution is derived from the graph structure, and gene expression datasets are simulated by sampling from that distribution.