BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

173 related articles for article (PubMed ID: 35328645)

  • 1. Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing.
    Cheung M; Campbell JJ; Thomas RJ; Braybrook J; Petzing J
    Int J Mol Sci; 2022 Mar; 23(6):. PubMed ID: 35328645
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Current trends in flow cytometry automated data analysis software.
    Cheung M; Campbell JJ; Whitby L; Thomas RJ; Braybrook J; Petzing J
    Cytometry A; 2021 Oct; 99(10):1007-1021. PubMed ID: 33606354
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Systematic Design, Generation, and Application of Synthetic Datasets for Flow Cytometry.
    Cheung M; Campbell JJ; Thomas RJ; Braybrook J; Petzing J
    PDA J Pharm Sci Technol; 2022; 76(3):200-215. PubMed ID: 35031542
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation.
    Mosmann TR; Naim I; Rebhahn J; Datta S; Cavenaugh JS; Weaver JM; Sharma G
    Cytometry A; 2014 May; 85(5):422-33. PubMed ID: 24532172
    [TBL] [Abstract][Full Text] [Related]  

  • 12. SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: algorithm design.
    Naim I; Datta S; Rebhahn J; Cavenaugh JS; Mosmann TR; Sharma G
    Cytometry A; 2014 May; 85(5):408-21. PubMed ID: 24677621
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Competitive SWIFT cluster templates enhance detection of aging changes.
    Rebhahn JA; Roumanes DR; Qi Y; Khan A; Thakar J; Rosenberg A; Lee FE; Quataert SA; Sharma G; Mosmann TR
    Cytometry A; 2016 Jan; 89(1):59-70. PubMed ID: 26441030
    [TBL] [Abstract][Full Text] [Related]  

  • 14. OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration.
    Hunter B; Nicorescu I; Foster E; McDonald D; Hulme G; Fuller A; Thomson A; Goldsborough T; Hilkens CMU; Majo J; Milross L; Fisher A; Bankhead P; Wills J; Rees P; Filby A; Merces G
    Cytometry A; 2024 Jan; 105(1):36-53. PubMed ID: 37750225
    [TBL] [Abstract][Full Text] [Related]  

  • 15. flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding.
    Ge Y; Sealfon SC
    Bioinformatics; 2012 Aug; 28(15):2052-8. PubMed ID: 22595209
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. An R-Derived FlowSOM Process to Analyze Unsupervised Clustering of Normal and Malignant Human Bone Marrow Classical Flow Cytometry Data.
    Lacombe F; Lechevalier N; Vial JP; Béné MC
    Cytometry A; 2019 Nov; 95(11):1191-1197. PubMed ID: 31577391
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Critical assessment of automated flow cytometry data analysis techniques.
    Aghaeepour N; Finak G; ; ; Hoos H; Mosmann TR; Brinkman R; Gottardo R; Scheuermann RH
    Nat Methods; 2013 Mar; 10(3):228-38. PubMed ID: 23396282
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.