Metadata-Version: 2.1
Name: NetAnalyzer
Version: 1.0.0
Summary: Python package for network analysis, operations and priorization.
Home-page: https://github.com/seoanezonjic/NetAnalyzer/
Author: seoanezonjic
Author-email: seoanezonjic@uma.es
License: MIT
Project-URL: Documentation, https://github.com/seoanezonjic/NetAnalyzer/
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Description-Content-Type: text/x-rst; charset=UTF-8
License-File: LICENSE.txt
Requires-Dist: NetworkX
Requires-Dist: numpy
Requires-Dist: scipy ==1.10.1
Requires-Dist: statsmodels
Requires-Dist: graphviz
Requires-Dist: mako
Requires-Dist: matplotlib
Requires-Dist: cdlib
Requires-Dist: scikit-learn
Requires-Dist: py-semtools
Requires-Dist: umap-learn
Requires-Dist: py-report-html
Requires-Dist: pecanpy
Requires-Dist: typing-extensions
Requires-Dist: py-cmdtabs
Requires-Dist: py-exp-calc
Requires-Dist: clusim
Requires-Dist: importlib-metadata ; python_version < "3.8"
Provides-Extra: testing
Requires-Dist: setuptools ; extra == 'testing'
Requires-Dist: pytest ; extra == 'testing'
Requires-Dist: pytest-cov ; extra == 'testing'

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===========
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.
