These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

675 related articles for article (PubMed ID: 27992111)

  • 1. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.
    Weber LM; Robinson MD
    Cytometry A; 2016 Dec; 89(12):1084-1096. PubMed ID: 27992111
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A computational approach for phenotypic comparisons of cell populations in high-dimensional cytometry data.
    Platon L; Pejoski D; Gautreau G; Targat B; Le Grand R; Beignon AS; Tchitchek N
    Methods; 2018 Jan; 132():66-75. PubMed ID: 28917725
    [TBL] [Abstract][Full Text] [Related]  

  • 3. immunoClust--An automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets.
    Sörensen T; Baumgart S; Durek P; Grützkau A; Häupl T
    Cytometry A; 2015 Jul; 87(7):603-15. PubMed ID: 25850678
    [TBL] [Abstract][Full Text] [Related]  

  • 4. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.
    Van Gassen S; Callebaut B; Van Helden MJ; Lambrecht BN; Demeester P; Dhaene T; Saeys Y
    Cytometry A; 2015 Jul; 87(7):636-45. PubMed ID: 25573116
    [TBL] [Abstract][Full Text] [Related]  

  • 5. The end of gating? An introduction to automated analysis of high dimensional cytometry data.
    Mair F; Hartmann FJ; Mrdjen D; Tosevski V; Krieg C; Becher B
    Eur J Immunol; 2016 Jan; 46(1):34-43. PubMed ID: 26548301
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Cluster stability in the analysis of mass cytometry data.
    Melchiotti R; Gracio F; Kordasti S; Todd AK; de Rinaldis E
    Cytometry A; 2017 Jan; 91(1):73-84. PubMed ID: 27754590
    [TBL] [Abstract][Full Text] [Related]  

  • 7. DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data.
    Lee AJ; Chang I; Burel JG; Lindestam Arlehamn CS; Mandava A; Weiskopf D; Peters B; Sette A; Scheuermann RH; Qian Y
    Cytometry A; 2018 Jun; 93(6):597-610. PubMed ID: 29665244
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Analysis of Mass Cytometry Data.
    Pedersen CB; Olsen LR
    Methods Mol Biol; 2019; 1989():267-279. PubMed ID: 31077111
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predicting Cell Populations in Single Cell Mass Cytometry Data.
    Abdelaal T; van Unen V; Höllt T; Koning F; Reinders MJT; Mahfouz A
    Cytometry A; 2019 Jul; 95(7):769-781. PubMed ID: 30861637
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Analyzing high-dimensional cytometry data using FlowSOM.
    Quintelier K; Couckuyt A; Emmaneel A; Aerts J; Saeys Y; Van Gassen S
    Nat Protoc; 2021 Aug; 16(8):3775-3801. PubMed ID: 34172973
    [TBL] [Abstract][Full Text] [Related]  

  • 11. diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering.
    Weber LM; Nowicka M; Soneson C; Robinson MD
    Commun Biol; 2019; 2():183. PubMed ID: 31098416
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automatically generate two-dimensional gating hierarchy from clustered cytometry data.
    Yang X; Qiu P
    Cytometry A; 2018 Oct; 93(10):1039-1050. PubMed ID: 30176185
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Scalable clustering algorithms for continuous environmental flow cytometry.
    Hyrkas J; Clayton S; Ribalet F; Halperin D; Armbrust EV; Howe B
    Bioinformatics; 2016 Feb; 32(3):417-23. PubMed ID: 26476780
    [TBL] [Abstract][Full Text] [Related]  

  • 14. flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification.
    Malek M; Taghiyar MJ; Chong L; Finak G; Gottardo R; Brinkman RR
    Bioinformatics; 2015 Feb; 31(4):606-7. PubMed ID: 25378466
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automated gating of flow cytometry data via robust model-based clustering.
    Lo K; Brinkman RR; Gottardo R
    Cytometry A; 2008 Apr; 73(4):321-32. PubMed ID: 18307272
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Analysis of High-Dimensional Phenotype Data Generated by Mass Cytometry or High-Dimensional Flow Cytometry.
    Cirovic B; Katzmarski N; Schlitzer A
    Methods Mol Biol; 2019; 1989():281-294. PubMed ID: 31077112
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A comparison framework and guideline of clustering methods for mass cytometry data.
    Liu X; Song W; Wong BY; Zhang T; Yu S; Lin GN; Ding X
    Genome Biol; 2019 Dec; 20(1):297. PubMed ID: 31870419
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Rapid cell population identification in flow cytometry data.
    Aghaeepour N; Nikolic R; Hoos HH; Brinkman RR
    Cytometry A; 2011 Jan; 79(1):6-13. PubMed ID: 21182178
    [TBL] [Abstract][Full Text] [Related]  

  • 19. CyCadas: accelerating interactive annotation and analysis of clustered cytometry data.
    Hunewald O; Demczuk A; Longworth J; Ollert M
    Bioinformatics; 2024 Oct; 40(10):. PubMed ID: 39374546
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools.
    Couckuyt A; Rombaut B; Saeys Y; Van Gassen S
    Bioinformatics; 2024 Mar; 40(4):. PubMed ID: 38632080
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 34.