ArticleBMC bioinformatics2021
Consensus clustering applied to multi-omics disease subtyping.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Ensemble clustering: A practical tutorial.Behavior research methods · 2026Article
- Multi-modal learning with incomplete data.Nature communications · 2026Article
- Multiple Clustering Algorithms for Ovarian Cancer Subtyping (MCAOCS): identifying clinical phenotypes and treatment response in critical care.Journal of ovarian research · 2026Article
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- CSIE: cancer subtyping via inference and ensemble.Briefings in bioinformatics · 2026Article
- Identification of PI3K/AKT/mTOR-related genes as diagnostic biomarkers for cutaneous squamous cell carcinoma.Biochemistry and biophysics reports · 2026Article
- PySimi: a unified framework for similarity measure evaluation in spectral clustering with applications to omics data.Frontiers in genetics · 2026Article
- Identification and Experimental Validation of Tumor Antigens and Hypoxia Subtypes of Osteosarcoma for Potential mRNA Vaccine Development.Current medicinal chemistry · 2026Article
- Multi-omics analysis identifies stemness-driven molecular subtypes, prognostic signature, epigenetic target APCDD1, and drug candidate Leflunomide in Wilms tumor.Frontiers in oncology · 2026Article
- Uncovering distinct clinical phenotypes in disseminated intravascular coagulation through machine learning-enabled cluster analysis.Frontiers in molecular biosciences · 2026Article
- Comprehensive Analysis of Immunomodulatory-Related Genes Reveals MAPK14-Associated Myeloid-Derived Suppressor Cells Infiltration in Pediatric Sepsis.Journal of inflammation research · 2026Article
- Predictive value of immunomodulatory-related genes for the prognosis of lung adenocarcinoma based on bioinformatics analysis.Discover oncology · 2025Article
- Shared M0 macrophage infiltration-related gene signatures in osteoarthritis and osteomyelitis by integrating bulk and single-cell RNA transcriptome.European journal of medical research · 2025Article
- DSCC: disease subtyping using spectral clustering and community detection from consensus networks.Briefings in bioinformatics · 2025Article
- Ferroptosis and radiotherapy-related genes in head and neck squamous cell carcinoma: diagnostic and prognostic significance.Discover oncology · 2025Article
- The role of four ERS-related key genes in sepsis induced organ dysfunction and the forewarning model construction.BMC medical informatics and decision making · 2025Article
- A Novel Neutrophil Extracellular Trap Signature Predicts Patient Chemotherapy Resistance and Prognosis in Lung Adenocarcinoma.Molecular biotechnology · 2025Article
- Characteristics of Oxidative Phosphorylation-Related Subtypes and Construction of a Prognostic Signature in Ovarian Cancer.Current gene therapy · 2025Article
- A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer.Open medicine (Warsaw, Poland) · 2025Article
- NK cell-related genes-driven novel molecular subtyping and prognostic signatures for Wilms tumor: uncovering the therapeutic potential of TGX-221 and biomarker role of HS2ST1.Frontiers in oncology · 2025Article
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4 authors.
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Abstract
backgroundFacing the diversity of omics data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results.
resultsHere, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omics data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering. ClustOmics computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganize data into consensus clusters.
conclusionWe applied ClustOmics to multi-omics disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high-quality consensus clusters, both from a computational and a biological point of view. The comparison to a state-of-the-art consensus-based integration tool, COCA, further corroborated this statement. However, the main interest of ClustOmics is not to compete with other tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance. AVAILABILITY: The ClustOmics source code, released under MIT license, and the results obtained on TCGA cancer data are available on GitHub: https://github.com/galadrielbriere/ClustOmics .
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