Optimizing weighted gene co-expression network analysis with a multi-threaded calculation of the topological overlap matrix

• Published in: Statistical applications in genetics and molecular biology Vol. 20; no. 4; pp. 145 - 153
• Main Authors: Shuai, Min, He, Dongmei, Chen, Xin
• Format: Journal Article
• Language: English
• Published: Germany De Gruyter 09.11.2021; Walter de Gruyter GmbH
• Subjects: adjacency matrix; Algorithms; C++ (programming language); Gene expression; Gene Regulatory Networks; graph theory; Mathematical analysis; Modules; Multiprocessing; Network analysis; parallel computing; Software; topological overlap matrix; Topology; weighted gene co-expression network; adjacency matrix; graph theory; parallel computing; weighted gene co-expression network; topological overlap matrix
• ISSN: 1544-6115; 2194-6302; 1544-6115
• DOI: 10.1515/sagmb-2021-0025

Biomolecular networks are often assumed to be scale-free hierarchical networks. The weighted gene co-expression network analysis (WGCNA) treats gene co-expression networks as undirected scale-free hierarchical weighted networks. The WGCNA R software package uses an Adjacency Matrix to store a network, next calculates the topological overlap matrix (TOM), and then identifies the modules (sub-networks), where each module is assumed to be associated with a certain biological function. The most time-consuming step of WGCNA is to calculate TOM from the Adjacency Matrix in a single thread. In this paper, the single-threaded algorithm of the TOM has been changed into a multi-threaded algorithm (the parameters are the default values of WGCNA). In the multi-threaded algorithm, Rcpp was used to make R call a C++ function, and then C++ used OpenMP to start multiple threads to calculate TOM from the Adjacency Matrix. On shared-memory MultiProcessor systems, the calculation time decreases as the number of CPU cores increases. The algorithm of this paper can promote the application of WGCNA on large data sets, and help other research fields to identify sub-networks in undirected scale-free hierarchical weighted networks. The source codes and usage are available at