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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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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6783424 | PMC |
http://dx.doi.org/10.1038/s41598-019-50885-8 | DOI Listing |
Plant Physiol Biochem
December 2024
Key Laboratory of Herbage Improvement and Grassland Agroecosystems, Lanzhou University, Lanzhou, 730020, China; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou, 730020, China; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, China; Engineering Research Center of Grassland Industry, Ministry of Education, Gansu Tech Innovation Centre of Western China Grassland Industry, China. Electronic address:
Arbuscular mycorrhizal (AM) fungi are widely existing soil microorganisms that form symbiotic relationships with most terrestrial plants. They are important for enhancing adversity resistance, including resistance to disease and water stresses. Nevertheless, it is not clear whether the benefits can be maintained in regulating the occurrence of plant diseases under drought, flooding stress and during water restoration.
View Article and Find Full Text PDFPlant Cell Environ
December 2024
Department of Plant and Soil Sciences and Kentucky Tobacco Research and Development Center, University of Kentucky, Lexington, Kentucky, USA.
The evolutionary arms race between plants and insects has led to key adaptive innovations that drive diversification. Alkaloids are well-documented anti-herbivory compounds in plant chemical defences, but how these specialized metabolites are allocated to cope with both biotic and abiotic stresses concomitantly is largely unknown. To examine how plants prioritize their metabolic resources responding to herbivory and cold, we integrated dietary toxicity bioassay in insects with co-expression analysis, hierarchical clustering, promoter assay, and protein-protein interaction in plants.
View Article and Find Full Text PDFCurr Res Food Sci
November 2024
Shandong Provincial Key Laboratory of Test Technology on Food Quality and Safety, Institute of Quality Standard and Testing Technology for Agro-Products, Shandong Academy of Agricultural Sciences, Jinan, 250100, China.
Radish is an important vegetable worldwide, with wide medicinal functions and health benefits. The quality of radish, strongly affected by phytochemicals like flavonoids and glucosinolates, are quite different depending on the radish varieties. However, the comprehensive accumulation profiles of secondary metabolites and their molecular regulatory mechanisms in different radish cultivars remain unclear thus far.
View Article and Find Full Text PDFMediators Inflamm
December 2024
Department of Stomatology, School of Stomatology, The Third Affiliated Hospital, Xi'an Medical University, Xi'an, China.
This study aimed to investigate the molecular mechanisms of periodontitis and identify key immune-related biomarkers using machine learning and Mendelian randomization (MR). Differentially expressed gene (DEG) analysis was performed on periodontitis datasets GSE16134 and GSE10334 from the Gene Expression Omnibus (GEO) database, followed by weighted gene co-expression network analysis (WGCNA) to identify relevant gene modules. Various machine learning algorithms were utilized to construct predictive models, highlighting core genes, while MR assessed the causal relationships between these genes and periodontitis.
View Article and Find Full Text PDFPharmgenomics Pers Med
December 2024
Department of General Surgery, The Second Affiliated Hospital of Air Force Medical University, Xi'an, Shaanxi Province, People's Republic of China.
Objective: To identify key genes and potential molecular mechanisms associated with Hashimoto's thyroiditis (HT) to provide new insights for the development of diagnostic and therapeutic targets for this disease.
Methods: Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify the differentially expressed genes (DEGs) associated with HT. A protein-protein interaction (PPI) network was used to obtain hub genes, with CD27 emerging as the key gene in HT.
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