ArticleScientific reports2019
Improved gene co-expression network quality through expression dataset down-sampling and network aggregation.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 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
- Constructing gene co-functional and co-regulatory networks from public transcriptomes using condition-specific ensemble co-expression.Nature communications · 2026Article
- Inferring Gene Networks from High-Throughput Transcriptomes.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Integrative gene co-expression network analysis reveals protein-coding and LncRNA genes associated with Alzheimer's disease pathology.Scientific reports · 2025Article
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- DRaCOon: a novel algorithm for pathway-level differential co-expression analysis in transcriptomics.BMC bioinformatics · 2025Article
- A method for mining condition-specific co-expressed genes in Camellia sinensis based on k-means clustering.BMC plant biology · 2024Article
- Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance.Plants (Basel, Switzerland) · 2023Article
- siVAE: interpretable deep generative models for single-cell transcriptomes.Genome biology · 2023Article
- GeneFriends: gene co-expression databases and tools for humans and model organisms.Nucleic acids research · 2023Article
- COXPRESdb v8: an animal gene coexpression database navigating from a global view to detailed investigations.Nucleic acids research · 2023Article
- Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective.Frontiers in genetics · 2023Article
- Comparative transcriptomic analysis revealed dynamic changes of distinct classes of genes during development of the Manila clam (Ruditapes philippinarum).BMC genomics · 2022Article
- A computational approach to generate highly conserved gene co-expression networks with RNA-seq data.STAR protocols · 2022Article
- GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation.Current issues in molecular biology · 2022Article
- Addressing noise in co-expression network construction.Briefings in bioinformatics · 2022Review
- Conditional transcriptional relationships may serve as cancer prognostic markers.BMC medical genomics · 2021Article
- Layer reconstruction and missing link prediction of a multilayer network with maximum a posteriori estimation.Physical review. E · 2021Article
- GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package.BMC bioinformatics · 2021Article
- Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study.NPJ systems biology and applications · 2021Article
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11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Large-scale gene co-expression networks are an effective methodology to analyze sets of co-expressed genes and discover new gene functions or associations. Distances between genes are estimated according to their expression profiles and are visualized in networks that may be further partitioned to reveal communities of co-expressed genes. Creating expression profiles is now eased by the large amounts of publicly available expression data (microarrays and RNA-seq). Although many distance calculation methods have been intensively compared and reviewed in the past, it is unclear how to proceed when many samples reflecting a wide range of different conditions are available. Should as many samples as possible be integrated into network construction or be partitioned into smaller sets of more related samples? Previous studies have indicated a saturation in network performances to capture known associations once a certain number of samples is included in distance calculations. Here, we examined the influence of sample size on co-expression network construction using microarray and RNA-seq expression data from three plant species. We tested different down-sampling methods and compared network performances in recovering known gene associations to networks obtained from full datasets. We further examined how aggregating networks may help increase this performance by testing six aggregation methods.
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