ArticleBioinformatics (Oxford, England)2020
PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells.
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 69 papers, 2 of them syntheses that pooled it.
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Who cites it
69 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated and reproducible cell identification in mass cytometry using neural networks.Briefings in bioinformatics · 2023Pooled it
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
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- Generalizable morphological profiling of cells by interpretable unsupervised learning.Nature communications · 2025Article
- Zika virus induces persistent phenotypic changes in natural killer cells distinct from dengue virus infection.bioRxiv : the preprint server for biology · 2025Article
- Intraperitoneal infusion of NKG2D CAR-NK cells induces endogenous CD8Molecular therapy : the journal of the American Society of Gene Therapy · 2025Article
- Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.Genome biology · 2025Article
- A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering.BMC genomics · 2025Article
- Minimally invasive diagnosis of precancerous cervical lesions using single-cell peripheral immune atlas.Cell reports. Medicine · 2025Article
- Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity.Cell · 2025Article
- Deconvolution of cell types and states in spatial multiomics utilizing TACIT.Nature communications · 2025Article
- Single-cell transcriptome analyses of PBMCs reveal the immunological characteristics of individuals with phlegm-dampness constitution.Frontiers of medicine · 2025Article
- Prior vaccination prevents overactivation of innate immune responses during COVID-19 breakthrough infection.Science translational medicine · 2025Article
- Multi-dimensional analysis of B cells reveals the expansion of memory and regulatory B-cell clusters in humans living in rural tropical areas.Clinical and experimental immunology · 2025Article
- The gut-brain-axis one year after treatment with cladribine tablets in patients with relapsing remitting multiple sclerosis: a pilot study.Frontiers in immunology · 2025Observational
- Distinct immune profiles in children living with HIV based on timing and duration of suppressive antiretroviral treatment.Virology · 2025Article
9 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationNew single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity.
resultsWe introduce a highly scalable graph-based clustering algorithm PARC-Phenotyping by Accelerated Refined Community-partitioning-for large-scale, high-dimensional single-cell data (>1 million cells). Using large single-cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without subsampling of cells, including Phenograph, FlowSOM and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single-cell dataset of 1.1 million cells within 13 min, compared with >2 h for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis. AVAILABILITY AND IMPLEMENTATION: https://github.com/ShobiStassen/PARC. 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.