Literature DB >> 23746659

Overview of clinical flow cytometry data analysis: recent advances and future challenges.

Carlos E Pedreira1, Elaine S Costa, Quentin Lecrevisse, Jacques J M van Dongen, Alberto Orfao.   

Abstract

Major technological advances in flow cytometry (FC), both for instrumentation and reagents, have emerged over the past few decades. These advances facilitate simultaneous evaluation of more parameters in single cells analyzed at higher speed. Consequently, larger and more complex data files that contain information about tens of parameters for millions of cells are generated. This increasing complexity has challenged pre-existing data analysis tools and promoted the development of new algorithms and tools for data analysis and visualization. Here, we review the currently available (conventional and newly developed) data analysis and visualization strategies that aim for easier, more objective, and robust interpretation of FC data both in biomedical research and clinical diagnostic laboratories.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2013        PMID: 23746659     DOI: 10.1016/j.tibtech.2013.04.008

Source DB:  PubMed          Journal:  Trends Biotechnol        ISSN: 0167-7799            Impact factor:   19.536


  31 in total

1.  Persistent polyclonal B-cell lymphocytosis: extensively proliferated CD27+IgM+IgD+ memory B cells with a distinctive immunophenotype.

Authors:  M A Berkowska; C Grosserichter-Wagener; H J Adriaansen; D de Ridder; K P Mirani-Oostdijk; H J Agteresch; S Böttcher; A Orfao; J J M van Dongen; M C van Zelm
Journal:  Leukemia       Date:  2014-02-19       Impact factor: 11.528

Review 2.  Minimal residual disease diagnostics in acute lymphoblastic leukemia: need for sensitive, fast, and standardized technologies.

Authors:  Jacques J M van Dongen; Vincent H J van der Velden; Monika Brüggemann; Alberto Orfao
Journal:  Blood       Date:  2015-05-21       Impact factor: 22.113

3.  Large population cell characterization using quantitative phase cytometer.

Authors:  Di Jin; Yongjin Sung; Niyom Lue; Yang-Hyo Kim; Peter T C So; Zahid Yaqoob
Journal:  Cytometry A       Date:  2017-04-26       Impact factor: 4.355

4.  PhenoGraph and viSNE facilitate the identification of abnormal T-cell populations in routine clinical flow cytometric data.

Authors:  Joseph A DiGiuseppe; Jolene L Cardinali; William N Rezuke; Dana Pe'er
Journal:  Cytometry B Clin Cytom       Date:  2017-09-26       Impact factor: 3.058

5.  CytoML for cross-platform cytometry data sharing.

Authors:  Greg Finak; Wenxin Jiang; Raphael Gottardo
Journal:  Cytometry A       Date:  2018-12       Impact factor: 4.355

Review 6.  Minimal/Measurable Residual Disease Detection in Acute Leukemias by Multiparameter Flow Cytometry.

Authors:  Franklin Fuda; Weina Chen
Journal:  Curr Hematol Malig Rep       Date:  2018-12       Impact factor: 3.952

7.  Phenotypic identification of subclones in multiple myeloma with different chemoresistant, cytogenetic and clonogenic potential.

Authors:  T Paíno; B Paiva; J M Sayagués; I Mota; T Carvalheiro; L A Corchete; I Aires-Mejía; J J Pérez; M L Sanchez; P Barcena; E M Ocio; L San-Segundo; M E Sarasquete; R García-Sanz; M-B Vidriales; A Oriol; M-T Hernández; M-A Echeveste; A Paiva; J Blade; J-J Lahuerta; A Orfao; M-V Mateos; N C Gutiérrez; J F San-Miguel
Journal:  Leukemia       Date:  2014-11-12       Impact factor: 11.528

Review 8.  Review: imaging technologies for flow cytometry.

Authors:  Yuanyuan Han; Yi Gu; Alex Ce Zhang; Yu-Hwa Lo
Journal:  Lab Chip       Date:  2016-11-29       Impact factor: 6.799

9.  Three-dimensional image cytometer based on widefield structured light microscopy and high-speed remote depth scanning.

Authors:  Heejin Choi; Dushan N Wadduwage; Ting Yuan Tu; Paul Matsudaira; Peter T C So
Journal:  Cytometry A       Date:  2014-10-28       Impact factor: 4.355

Review 10.  Flow Cytometric Minimal Residual Disease Analysis in Acute Leukemia: Current Status.

Authors:  Pulkit Rastogi; Man Updesh Singh Sachdeva
Journal:  Indian J Hematol Blood Transfus       Date:  2019-04-02       Impact factor: 0.900

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