ArticlePloS one2013
Massive-scale gene co-expression network construction and robustness testing using random matrix theory.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Network aggregation improves gene function prediction of grapevine gene co-expression networks.Plant molecular biology · 2020Pooled it
- A Comparative Study of Gene Co-Expression Thresholding Algorithms.Journal of computational biology : a journal of computational molecular cell biology · 2024Article
- A method for mining condition-specific co-expressed genes in Camellia sinensis based on k-means clustering.BMC plant biology · 2024Article
- Addressing noise in co-expression network construction.Briefings in bioinformatics · 2022Review
- TheGenome research · 2021Article
- Exploration into biomarker potential of region-specific brain gene co-expression networks.Scientific reports · 2020Article
- NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles.G3 (Bethesda, Md.) · 2020Article
- Computational Analysis of the Global Effects ofGenes · 2020Article
- Gene Coexpression Network and Module Analysis across 52 Human Tissues.BioMed research international · 2020Article
- Improved gene co-expression network quality through expression dataset down-sampling and network aggregation.Scientific reports · 2019Article
- Linking Binary Gene Relationships to Drivers of Renal Cell Carcinoma Reveals Convergent Function in Alternate Tumor Progression Paths.Scientific reports · 2019Article
- EdgeScaping: Mapping the spatial distribution of pairwise gene expression intensities.PloS one · 2019Article
- Identifying Temporally Regulated Root Nodulation Biomarkers Using Time Series Gene Co-Expression Network Analysis.Frontiers in plant science · 2019Article
- Network spectra for drug-target identification in complex diseases: new guns against old foes.Applied network science · 2018Review
- Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study.Scientific reports · 2017Article
- Understanding cancer complexome using networks, spectral graph theory and multilayer framework.Scientific reports · 2017Article
- Analysis of bHLH coding genes using gene co-expression network approach.Molecular biology reports · 2016Article
- Stability of similarity measurements for bipartite networks.Scientific reports · 2016Article
- Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality.Life (Basel, Switzerland) · 2015Article
- Construction of citrus gene coexpression networks from microarray data using random matrix theory.Horticulture research · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.