wTO: an R package for computing weighted topological overlap and consensus networks with an integrated visualization tool

TL;DR

The paper introduces an R package for calculating weighted topological overlap that explicitly considers the sign of correlations, includes p-value calculations, supports time series data, and offers a consensus network method with visualization tools.

Contribution

It presents a novel R package that improves network analysis by explicitly handling positive and negative correlations, and introduces a new consensus network method for biological data.

Findings

The package effectively analyzes gene co-expression networks.

It accurately calculates p-values for gene pair scores.

Demonstrates utility with human brain and metagenomics datasets.

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. Here, we present an R package for calculating the wTO, that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set. In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL-2 Open Source License (https://cran.r-project.org/web/packages/wTO/).