BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

292 related articles for article (PubMed ID: 31098416)

  • 1. 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]  

  • 2. 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]  

  • 3. Computational approaches for high-throughput single-cell data analysis.
    Todorov H; Saeys Y
    FEBS J; 2019 Apr; 286(8):1451-1467. PubMed ID: 30058136
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks.
    Li YH; Li D; Samusik N; Wang X; Guan L; Nolan GP; Wong WH
    PLoS Comput Biol; 2017 Dec; 13(12):e1005875. PubMed ID: 29281633
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Determination of essential phenotypic elements of clusters in high-dimensional entities-DEPECHE.
    Theorell A; Bryceson YT; Theorell J
    PLoS One; 2019; 14(3):e0203247. PubMed ID: 30845234
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre.
    Ashhurst TM; Marsh-Wakefield F; Putri GH; Spiteri AG; Shinko D; Read MN; Smith AL; King NJC
    Cytometry A; 2022 Mar; 101(3):237-253. PubMed ID: 33840138
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Unsupervised flow cytometry analysis in hematological malignancies: A new paradigm.
    Béné MC; Lacombe F; Porwit A
    Int J Lab Hematol; 2021 Jul; 43 Suppl 1():54-64. PubMed ID: 34288436
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9.
    Opzoomer JW; Timms JA; Blighe K; Mourikis TP; Chapuis N; Bekoe R; Kareemaghay S; Nocerino P; Apollonio B; Ramsay AG; Tavassoli M; Harrison C; Ciccarelli F; Parker P; Fontenay M; Barber PR; Arnold JN; Kordasti S
    Elife; 2021 Apr; 10():. PubMed ID: 33929322
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Challenges in unsupervised clustering of single-cell RNA-seq data.
    Kiselev VY; Andrews TS; Hemberg M
    Nat Rev Genet; 2019 May; 20(5):273-282. PubMed ID: 30617341
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Misty Mountain clustering: application to fast unsupervised flow cytometry gating.
    Sugár IP; Sealfon SC
    BMC Bioinformatics; 2010 Oct; 11():502. PubMed ID: 20932336
    [TBL] [Abstract][Full Text] [Related]  

  • 13. JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters.
    Lim HS; Qiu P
    PLoS Comput Biol; 2021 Mar; 17(3):e1008804. PubMed ID: 33724985
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Clustering Single-Cell Expression Data Using Random Forest Graphs.
    Pouyan MB; Nourani M
    IEEE J Biomed Health Inform; 2017 Jul; 21(4):1172-1181. PubMed ID: 28113735
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. 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]  

  • 19. K-means quantization for a web-based open-source flow cytometry analysis platform.
    Wong N; Kim D; Robinson Z; Huang C; Conboy IM
    Sci Rep; 2021 Mar; 11(1):6735. PubMed ID: 33762594
    [TBL] [Abstract][Full Text] [Related]  

  • 20. OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis.
    Finak G; Frelinger J; Jiang W; Newell EW; Ramey J; Davis MM; Kalams SA; De Rosa SC; Gottardo R
    PLoS Comput Biol; 2014 Aug; 10(8):e1003806. PubMed ID: 25167361
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.