Optimizing weighted gene co-expression network analysis with a multi-threaded calculation of the topological overlap matrix
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Min Shuai
Abstract
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 https://github.com/do-somethings-haha/multi-threaded_calculate_unsigned_TOM_from_unsigned_or_signed_Adjacency_Matrix_of_WGCNA.
Funding source: Project of Inheritance Studio of National Famous Experts of State Administration of TCM
Award Identifier / Grant number: NO. [2019] 41
Funding source: Key project at central government level: The ability establishment of sustainable use for valuable Chinese medicine resources
Award Identifier / Grant number: 2060302
Funding source: Sichuan Science and Technology Program
Award Identifier / Grant number: 2021YJ0113
Funding source: the National Natural Science Foundation of China
Award Identifier / Grant number: 81673553
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Research funding: This study was supported by grants from the National Natural Science Foundation of China (81673553), Key project at central government level: The ability establishment of sustainable use for valuable Chinese medicine resources (2060302), Sichuan Science and Technology Program (2021YJ0113), Project of Inheritance Studio of National Famous Experts of State Administration of TCM (NO. [2019] 41).
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© 2021 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)
- Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections
- Software and Application Note
- Optimizing weighted gene co-expression network analysis with a multi-threaded calculation of the topological overlap matrix
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)
- Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections
- Software and Application Note
- Optimizing weighted gene co-expression network analysis with a multi-threaded calculation of the topological overlap matrix
