ArticleBMC genomics2022
oCEM: Automatic detection and analysis of overlapping co-expressed gene modules.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Metabolite coupling analysis and metabolite-flux coupling analysis of genome-scale metabolic models.Frontiers in bioinformatics · 2026Article
- Reconciling multiple connectivity-based systems biology methods for drug repurposing.Briefings in bioinformatics · 2025Review
- A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology.BMC genomics · 2023Article
- DrGA: cancer driver gene analysis in a simpler manner.BMC bioinformatics · 2022Article
- Identification and Validation of a Novel Three Hub Long Noncoding RNAs With m6A Modification Signature in Low-Grade Gliomas.Frontiers in molecular biosciences · 2022Article
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2 authors.
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Abstract
backgroundWhen it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated.
resultsThis study presented an R-package, Overlapping CoExpressed gene Module (oCEM), armed with the decomposition methods to solve the challenges above. We also developed a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. We showed that oCEM outperformed state-of-the-art techniques in the ability to detect biologically relevant modules additionally.
conclusionsoCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/oCEM .
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