ArticlePloS one2019
Determination of essential phenotypic elements of clusters in high-dimensional entities-DEPECHE.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Application and characterization of the multiple instance learning framework in flow cytometry.Scientific reports · 2026Article
- Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.Genome biology · 2025Article
- High-Dimensional Analysis of Type 2 Lymphocyte Dynamics During Mepolizumab or Dupilumab Treatment in Severe Asthma.Allergy · 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
- ImmCellTyper facilitates systematic mass cytometry data analysis for deep immune profiling.eLife · 2024Article
- The single-cell transcriptional landscape of innate and adaptive lymphocytes in pediatric-onset colitis.Cell reports. Medicine · 2023Article
- Cervical lymph nodes and ovarian teratomas as germinal centres in NMDA receptor-antibody encephalitis.Brain : a journal of neurology · 2022Article
- New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy.Patterns (New York, N.Y.) · 2021Article
- Article
- Progress and applications of mass cytometry in sketching immune landscapes.Clinical and translational medicine · 2020Review
- Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data.Frontiers in cell and developmental biology · 2020Review
- A comparison framework and guideline of clustering methods for mass cytometry data.Genome biology · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Technological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make such highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi- and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better than the currently available best performing clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. DEPECHE is implemented in the open source R package DepecheR currently available at github.com/Theorell/DepecheR.
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Registered trials
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