petalNet

petalNet constructs biologically meaningful and statistically robust gene co-expression networks from high-throughput gene expression datasets such as RNA-seq.

Key Features:

• No normality assumption: Operates without assuming data normality, making it appropriate for RNA-seq and other high-throughput gene expression data.
• Statistical robustness: Produces statistically robust co-expression network models from expression data.
• Network architecture enforcement: Constructs networks with scale-free and small-world architectures to reflect biological organization.
• Whole-systems analysis: Designed to handle very large datasets to capture system-level, whole-genome interactions.
• Reproducibility: Promotes reproducibility across studies by using consistent network construction criteria.

Scientific Applications:

• Gene co-expression network analysis: Modeling gene–gene co-expression patterns to study transcriptional organization.
• Systems biology: Investigating system-level interactions and network properties in biological systems.
• Functional association: Associating genes with biological processes and uncovering functional insights from expression data.
• Subnetwork identification: Identifying research-dependent subnetworks for targeted biological investigations.
• Whole-genome experiments: Applying to whole-genome RNA-seq experiments to generate biologically relevant network models.

Methodology:

Constructs co-expression networks without assuming data normality, enforces scale-free and small-world network architectures, and is implemented in R.