lionessR

lionessR infers single-sample molecular interaction networks from population-level data using linear interpolation to capture network heterogeneity for applications such as precision medicine.

Key Features:

• Single-Sample Network Reconstruction: Extracts individual sample networks from population data instead of producing only aggregate networks.
• Integration with Existing Network Inference Algorithms: Operates with network inference algorithms that output complete, weighted adjacency matrices.
• Linear Interpolation-Based Estimation: Uses linear interpolation to derive per-sample edge weights from population-level network estimates.

Scientific Applications:

• Precision Medicine: Enables identification of differential correlation patterns between patient groups, such as those observed in cancer studies, to support individualized analysis.
• Gene Expression Studies: Can model networks from correlated gene expression data, as demonstrated on a bone cancer dataset.
• Network Heterogeneity Analysis: Facilitates exploration of how biological entities interact uniquely across samples or conditions.

Methodology:

Operates by leveraging linear interpolation to estimate per-sample networks from population-level network estimates and requires network inference algorithms that output complete, weighted adjacency matrices.