ArticleBioinformation2012
Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of methods using microarray data.
Article in Bioinformation, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- GSNCASCR: An R Package to Identify Differentially Co-Expressed Curated Gene Sets with Single-Cell RNA-Seq Data.International journal of molecular sciences · 2025Article
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- Whole transcriptome sequencing reveals HOXD11-AGAP3, a novel fusion transcript in the Indian acute leukemia cohort.Frontiers in genetics · 2023Article
- Co-expression analysis to identify key modules and hub genes associated with COVID-19 in platelets.BMC medical genomics · 2022Article
- Article
- Gene Co-expression Network Analysis and Linking Modules to Phenotyping Response in Plants.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Transcriptomic profiling of whole blood in 22q11.2 reciprocal copy number variants reveals that cell proportion highly impacts gene expression.Brain, behavior, & immunity - health · 2021Article
- Deep Transcriptomic Analysis Reveals the Dynamic Developmental Progression during Early Development of Channel Catfish (International journal of molecular sciences · 2020Article
- A prognostic nomogram integrating novel biomarkers identified by machine learning for cervical squamous cell carcinoma.Journal of translational medicine · 2020Article
- Genome-Wide Analysis of Basic Helix-Loop-Helix Transcription Factors to Elucidate Candidate Genes Related to Fruit Ripening and Stress in Banana (Frontiers in plant science · 2020Article
- TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework.Journal of applied statistics · 2020Article
- Network-based feature selection reveals substructures of gene modules responding to salt stress in rice.Plant direct · 2019Article
- Four lncRNAs associated with breast cancer prognosis identified by coexpression network analysis.Journal of cellular physiology · 2019Article
- Weighted Gene Co-expression Network Analysis for RNA-Sequencing Data of the Varicose Veins Transcriptome.Frontiers in physiology · 2019Article
- Identification of an oncogenic network with prognostic and therapeutic value in prostate cancer.Molecular systems biology · 2018Article
- Analysis of weighted co-regulatory networks in maize provides insights into new genes and regulatory mechanisms related to inositol phosphate metabolism.BMC genomics · 2016Article
- Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model.BMC medical genomics · 2014Article
- A predictor for predicting Escherichia coli transcriptome and the effects of gene perturbations.BMC bioinformatics · 2014Article
- Hypermethylation in the ZBTB20 gene is associated with major depressive disorder.Genome biology · 2014Article
- Co-expression analysis of fetal weight-related genes in ovine skeletal muscle during mid and late fetal development stages.International journal of biological sciences · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four different treatments with Metyrapone, an inhibitor of cortisol biosynthesis. We conducted several microarray quality control checks before applying GCN methods to filtered datasets. Then we compared the outputs of two methods using connectivity as a criterion, as it measures how well a node (gene) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT and node ranks in two methods were compared to identify those nodes which are highly differentially ranked (HDR). A total of 1,017 HDR nodes were identified across one or more of four networks. We investigated HDR nodes by gene enrichment analyses in relation to their biological relevance to phenotypes. We observed that, in contrast to WGCNA method, PCIT algorithm removes many of the edges of the most highly interconnected nodes. Removal of edges of most highly connected nodes or hub genes will have consequences for downstream analyses and biological interpretations. In general, for large GCN construction (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended.
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Registered trials
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