ArticleBMC bioinformatics2010
Data reduction for spectral clustering to analyze high throughput flow cytometry data.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed, 166 citations in OpenAlex.
- Identifying sex-specific sub-phenotypes of Alzheimer's disease progression using longitudinal electronic health records.EBioMedicine · 2026Article
- Identifying Sex-Specific Sub-phenotypes of Alzheimer's Disease Progression Using Longitudinal Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.Briefings in bioinformatics · 2024Article
- Benchmarking digital PCR partition classification methods with empirical and simulated duplex data.Briefings in bioinformatics · 2024Article
- A cell-level discriminative neural network model for diagnosis of blood cancers.Bioinformatics (Oxford, England) · 2023Article
- The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions.Immunoinformatics (Amsterdam, Netherlands) · 2023Review
- Animal models and animal-free innovations for cardiovascular research: current status and routes to be explored. Consensus document of the ESC Working Group on Myocardial Function and the ESC Working Group on Cellular Biology of the Heart.Cardiovascular research · 2022Article
- Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease.Cell reports · 2022Article
- On clustering for cell-phenotyping in multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) data.BMC research notes · 2022Article
- Automated identification of maximal differential cell populations in flow cytometry data.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022Article
- Automatic, fast, hierarchical, and non-overlapping gating of flow cytometric data with flowEMMi v2.Computational and structural biotechnology journal · 2022Article
- Data-driven identification of SARS-CoV-2 subpopulations using PhenoGraph and binary-coded genomic data.Briefings in bioinformatics · 2021Article
- scIGANs: single-cell RNA-seq imputation using generative adversarial networks.Nucleic acids research · 2020Article
- Spectrum: fast density-aware spectral clustering for single and multi-omic data.Bioinformatics (Oxford, England) · 2020Article
- Spectral Clustering Algorithm for Cognitive Diagnostic Assessment.Frontiers in psychology · 2020Article
- Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data.Frontiers in cell and developmental biology · 2020Review
- flowEMMi: an automated model-based clustering tool for microbial cytometric data.BMC bioinformatics · 2019Article
- Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction.Nature communications · 2019Article
- Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline: Data architecture, artificial intelligence, automated processing, containerization, and clusters orchestration ease the transition from data acquisition to insights in medium-to-large datasets.BioEssays : news and reviews in molecular, cellular and developmental biology · 2019Article
- Determination of essential phenotypic elements of clusters in high-dimensional entities-DEPECHE.PloS one · 2019Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundRecent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address this issue, we have modified spectral clustering by adding an information preserving sampling procedure and applying a post-processing stage. We call this entire algorithm SamSPECTRAL.
resultsWe tested our algorithm on flow cytometry data as an example of large, multidimensional data containing potentially hundreds of thousands of data points (i.e., "events" in flow cytometry, typically corresponding to cells). Compared to two state of the art model-based flow cytometry clustering methods, SamSPECTRAL demonstrates significant advantages in proper identification of populations with non-elliptical shapes, low density populations close to dense ones, minor subpopulations of a major population and rare populations.
conclusionsThis work is the first successful attempt to apply spectral methodology on flow cytometry data. An implementation of our algorithm as an R package is freely available through BioConductor.
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Registered trials
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.