miniPEB 2021: Network Analysis Link to the course: learnNetSci. Network Science: Overview Network Science is broadly employed in many fields: from understanding how friends bond in a party to how animals interact; from how superheroes appear in the same comic books to how genes can be related to a specific biological process. Network analysis is especially beneficial for understanding complex systems, in all research fields. Examples of complex biological or medical systems include gene regulatory, ecological, and neuropsychology networks.

2nd Workshop of Advanced Bioinformatics: Network Medicine

Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts: Co-expression Differential Network Analysis (CoDiNA).

Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at https://doi.org/10.1371/journal.pone.0240523

Computes the Weighted Topological Overlap with positive and negative signs (wTO) networks given a data frame containing the mRNA count/ expression/ abundance per sample, and a vector containing the interested nodes of interaction (a subset of the elements of the full data frame). It also computes the cut-off threshold or p-value based on the individuals bootstrap or the values reshuffle per individual. It also allows the construction of a consensus network, based on multiple wTO networks. The package includes a visualization tool for the networks.

Abstract Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network.