ArticleBioinformatics (Oxford, England)2020
Spectrum: fast density-aware spectral clustering for single and multi-omic data.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Spectral clustering identifies patterns of chiropractic care in a national longitudinal cohort.JAMIA open · 2026Article
- Navigating multi-omic integration methods for human microbiome research.Nature microbiology · 2026Review
- Characterizing spatiotemporal white matter hyperintensity pathophysiology in vivo to disentangle vascular and neurodegenerative contributions.Nature communications · 2026Article
- Explainable graph learning for multimodal single-cell data integration.BMC bioinformatics · 2026Article
- PySimi: a unified framework for similarity measure evaluation in spectral clustering with applications to omics data.Frontiers in genetics · 2026Article
- The role of MiRNA-mediated tumor microenvironment in bone metastasis from a multi-omics perspective: cross-cancer mechanisms and clinical translation.Frontiers in oncology · 2026Review
- Transcriptomic Analyses of Normal Human Pancreata Reveal the Presence of Cancer Subtypes that Correlate with Acinar Ductal Metaplasia and Donor Ancestry.Cancer research communications · 2026Article
- The Coevolution of Colour Patterns and Hindwing Shapes on a Large Phylogenetic Scale Reveals Predation-Driven Adaptive Syndromes in Swallowtail Butterflies.Ecology letters · 2026Article
- Consensus spectral clustering with weighted similarity functions for single-cell RNA sequencing data.Communications in statistics: Simulation and computation · 2025Article
- CLUEY enables knowledge-guided clustering and cell type detection from single-cell omics data.Bioinformatics (Oxford, England) · 2025Article
- Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.Genome biology · 2025Article
- stImage: a versatile framework for optimizing spatial transcriptomic analysis through customizable deep histology and location informed integration.Briefings in bioinformatics · 2025Article
- CHOIR improves significance-based detection of cell types and states from single-cell data.Nature genetics · 2025Article
- Unsupervised machine learning identifies opioid taper reversal patterns in a longitudinal cohort (2008-2018).PLOS digital health · 2025Article
- Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification.BioData mining · 2024Article
- CHAI: consensus clustering through similarity matrix integration for cell-type identification.Briefings in bioinformatics · 2024Article
- Characterization of neurofibrillary tangle immunophenotype signatures to classify tangle maturity in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification.bioRxiv : the preprint server for biology · 2024Article
- Normalizing need not be the norm: count-based math for analyzing single-cell data.Theory in biosciences = Theorie in den Biowissenschaften · 2024Article
- Multi-omics integration with weighted affinity and self-diffusion applied for cancer subtypes identification.Journal of translational medicine · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
motivationClustering patient omic data is integral to developing precision medicine because it allows the identification of disease subtypes. A current major challenge is the integration multi-omic data to identify a shared structure and reduce noise. Cluster analysis is also increasingly applied on single-omic data, for example, in single cell RNA-seq analysis for clustering the transcriptomes of individual cells. This technology has clinical implications. Our motivation was therefore to develop a flexible and effective spectral clustering tool for both single and multi-omic data.
resultsWe present Spectrum, a new spectral clustering method for complex omic data. Spectrum uses a self-tuning density-aware kernel we developed that enhances the similarity between points that share common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to reduce noise and reveal underlying structures. Spectrum contains a new method for finding the optimal number of clusters (K) involving eigenvector distribution analysis. Spectrum can automatically find K for both Gaussian and non-Gaussian structures. We demonstrate across 21 real expression datasets that Spectrum gives improved runtimes and better clustering results relative to other methods. AVAILABILITY AND IMPLEMENTATION: Spectrum is available as an R software package from CRAN https://cran.r-project.org/web/packages/Spectrum/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.