NetAnalyzer

Python library for network analysis, operations and priorization.

This package is designed to perform various steps in network analysis and processing through a modular design. Key features include:

• Randomization: Enables randomization of both clustered and individual nodes or edges within networks.

• Projections: Simplifies network complexity by reducing the number of layers based on connections from an excluded layer. For example, it can transition from a Phenotype-Patient-Mutation network to a Patient-Mutation network, connecting layers based on common nodes between patients and mutations.

• Topological Analysis: Computes various topological metrics for nodes (e.g., degree, betweenness) and provides summary statistics for entire networks.

• Cluster analysis: Performs metrics on predefined clusters and applies clustering algorithms based on the cdlib library.

• Embedding of networks (Kernels and node2vec): Defines node similarity using methods for processing context information in networks, including classical Kernel approaches and node2vec. It also supports integration of multiple layers.

• Prioritization: Applies propagation algorithms to prioritize nodes based on similarity metrics, such as the adjacency matrix, and a set of seed nodes.

• Net plotting: Provides several tools for graphing networks from different net plotter packages (igraph, cytoscape, graphviz).

Please, cite this library as: Rojano E., Seoane-Zonjic P., Bueno-Amorós A., Perkins JR., and Ranea JAG. Revealing the Relationship Between Human Genome Regions and Pathological Phenotypes Through Network Analysis. Lecture Notes in Computer Science, DOI: 10.1007/978-3-319-56148-6_17.

See https://github.com/seoanezonjic/NetAnalyzer