BioNetStat: A Tool for Biological Networks Differential Analysis

Abstract

The study of interactions among biological components can be carried out by using methods grounded on network theory. Most of these methods focus on the comparison of two biological networks (e.g., control vs. disease). However, biological systems often present more than two biological states (e.g., tumor grades). To compare two or more networks simultaneously, we developed , a package with a user-friendly graphical interface. compares correlation networks based on the probability distribution of a feature of the graph (e.g., centrality measures). The analysis of the structural alterations on the network reveals significant modifications in the system. For example, the analysis of centrality measures provides information about how the relevance of the nodes changes among the biological states. We evaluated the performance of in both, toy models and two case studies. The latter related to gene expression of tumor cells and plant metabolism. Results based on simulated scenarios suggest that the statistical power of is less sensitive to the increase of the number of networks than Gene Set Coexpression Analysis ( ). Also, besides being able to identify nodes with modified centralities, identified altered networks associated with signaling pathways that were not identified by other methods.

Corresponding Assessment Scenarios in Laboratory and on-Court Tests: Centrality Measurements by Complex Networks Analysis in Young Basketball Players

“…Analysis by complex networks was performed using software (Gephi 0.9.2) after data processing by specific algorithm for this purpose in the MATLAB environment. The networks presented 25 nodes and, like a threshold as generally purposed on correlation networks construction 20 , their edges were obtained by the statistically significant "r" values (P < 0.05), from all results evaluated by Pearson correlation in each scenario, laboratory (L) and on-court (C), added to the common parameters in both (see below in statistical analysis). The notion of centrality originated from the analyses of social networks and is currently being used for metric interpretations of networks involving different actors.…”

Melatonin-Index as a biomarker for predicting the distribution of presymptomatic and asymptomatic SARS-CoV-2 carriers

“…Considering that melatonin orchestrates different cellular and intercellular processes and its role changes according to cell phenotypes, we used a bioinformatics tool that integrates the connectivity between the most expressed genes, allowing us to compare the same set of genes under different situations. Thus, instead of evaluating the most or less expressed genes or enriched gene sets previously curated, this method discloses cohesive subgroups of variables in one of the states and evaluates whether these groups change their correlation patterns among states (26). The method is found useful to identify influential spreaders in a network, as instead of considering only the most highly connected or the most central genes, it considers those that are located in the core of the network, serving to different levels of organization (32).…”

“…The nodes represented the genes selected, and the correlation levels represented the links. This analysis was carried out by BioNetStat package (26), and the complex network parameters degree centrality and degree distribution were evaluated.…”

“…The structures of networks generated with the COVID-19-Signature genes were evaluated in three MEL-Index subgroups (High, Medium, and Low MEL-Index). Degrees of centrality and distribution were analyzed in these groups using BioNetStat Bioconductor package (26).…”

Comparing Statistical Tests for Differential Network Analysis of Gene Modules

“…(2) Identifying pairs of genes whose correlation differs between two or more groups ( Liu et al, 2010 ; Dawson et al, 2012 ; Fukushima, 2013 ; Ha et al, 2015 ; McKenzie et al, 2016 ; Siska et al, 2016 ), i.e., the focus is on the connection between only two genes at a time. (3) The last category, and the focus of this paper, attempts to identify subsets of co-expressed genes, called modules (also referred to as clusters or communities; Petereit et al, 2016 ) whose connections differ between phenotypes ( Watson, 2006 ; Choi and Kendziorski, 2009 ; Gill et al, 2010 ; Tesson et al, 2010 ; Langfelder et al, 2011 ; Rahmatallah et al, 2014 ; Jardim et al, 2019 ). Modules are groups of multiple genes that interact in a coordinated manner, e.g., their expression levels are correlated.…”