CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis

Minoo Ashtiani; Mehdi Mirzaie; Mohieddin Jafari

doi:10.1093/bioinformatics/bty819

CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis

Bioinformatics. 2019 Apr 15;35(8):1436-1437. doi: 10.1093/bioinformatics/bty819.

Authors

Minoo Ashtiani¹, Mehdi Mirzaie², Mohieddin Jafari^{1

3}

Affiliations

¹ Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.
² Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.
³ Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Finland.

PMID: 30239607
DOI: 10.1093/bioinformatics/bty819

Abstract

Motivation: Centrality analysis involves a series of ambiguities in that there are numerous well-known centrality measures with differing algorithms for establishing which nodes in a network are essential. There is no clearly preferred measure or means of deciding which measure is most germane to a given network with respect to node essentiality vis-à-vis topological features. Our aim here was to develop an instrument that enables comparisons among potentially appropriate centrality measures to be made with respect to network structure and thereby to support the identification of the most informative measure according to dimensional reduction methods.

Methods: The Central Informative Nodes in Network Analysis (CINNA) package introduced herein gathers all required functions for centrality analysis in weighted/unweighted and directed/undirected networks. Then, it compares, assorts and visualizes centrality measures to select which best describes the node importance.

Availability and implementation: CINNA is available in CRAN, including a tutorial. URL: https://cran.r-project.org/web/packages/CINNA/index.html.

MeSH terms

Algorithms*
Software*