Algorithm optimization for weighted gene co-expression network analysis: accelerating the calculation of Topology Overlap Matrices with OpenMP and SQLite

Author:

Shuai Min,Chen Xin

Abstract

AbstractMotivationWeighted gene co-expression network analysis (WGCNA) is an R package that can search highly related gene modules. The most time-consuming step of the whole analysis is to calculate the Topological Overlap Matrix (TOM) from the Adjacency Matrix in a single thread. This study changes it to multithreading.ResultsThis paper uses SQLite for multi-threaded data transfer between R and C++, uses OpenMP to enable multi-threading and calculates the TOM via an adjacency matrix on a Shared-memory MultiProcessor (SMP) system, where the calculation time decreases as the number of physical CPU cores increases.Availability and implementationThe source code is available at https://github.com/do-somethings-haha/fast_calculate_TOM_of_WGCNAContactchenxin@cdutcm.edu.cn

Publisher

Cold Spring Harbor Laboratory

Reference14 articles.

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2. David, T. et al. (2019) A Genetic Algorithm to Optimize Weighted Gene Co-Expression Network Analysis., Journal of computational biology: a journal of computational molecular cell biology, 26.

3. EWipf, D. et al. (2014) Transcriptional responses of Medicago truncatula upon sulfur deficiency stress and arbuscular mycorrhizal symbios, FRONT PLANT SCI, 5.

4. Langfelder, P. and Horvath, S. (2008) WGCNA: an R package for weighted correlation network analysis, BMC BIOINFORMATICS, 9.

5. Lu, C. et al. (2019) Comparative transcriptomics and weighted gene co-expression correlation network analysis (WGCNA) reveal potential regulation mechanism of carotenoid accumulation in Chrysanthemum × morifolium, PLANT PHYSIOL BIOCH, 142.

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