Literature DB >> 31069980

Flow cytometry data analysis: Recent tools and algorithms.

Sebastiano Montante1, Ryan R Brinkman1,2.   

Abstract

Flow cytometry (FCM) allows scientists to rapidly quantify up to 50 parameters for millions of cells per sample. The bottleneck in the application of the technology is data analysis, and the high number of parameters measured by the current generation of instruments requires the use of advanced computational algorithms to make full use of their capabilities. This review summarizes the main steps of FCM data analysis, focusing on the use of the most recent bioinformatic tools developed for an R-based programming environment. In particular, for each stage of the data analysis, libraries and packages currently available are listed, and a brief description of their functioning is included.
© 2019 John Wiley & Sons Ltd.

Keywords:  automated gating; bioinformatics; clustering; data analysis; flow cytometry

Mesh:

Year:  2019        PMID: 31069980     DOI: 10.1111/ijlh.13016

Source DB:  PubMed          Journal:  Int J Lab Hematol        ISSN: 1751-5521            Impact factor:   2.877


  7 in total

Review 1.  Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence.

Authors:  Ying Xu; Guan-Hua Su; Ding Ma; Yi Xiao; Zhi-Ming Shao; Yi-Zhou Jiang
Journal:  Signal Transduct Target Ther       Date:  2021-08-20

2.  PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments.

Authors:  Yen Hoang; Stefanie Gryzik; Ines Hoppe; Alexander Rybak; Martin Schädlich; Isabelle Kadner; Dirk Walther; Julio Vera; Andreas Radbruch; Detlef Groth; Sabine Baumgart; Ria Baumgrass
Journal:  Front Immunol       Date:  2022-05-03       Impact factor: 8.786

3.  Censcyt: censored covariates in differential abundance analysis in cytometry.

Authors:  Reto Gerber; Mark D Robinson
Journal:  BMC Bioinformatics       Date:  2021-05-10       Impact factor: 3.169

Review 4.  Brain tumors: Cancer stem-like cells interact with tumor microenvironment.

Authors:  Hai-Long Liu; Ya-Nan Wang; Shi-Yu Feng
Journal:  World J Stem Cells       Date:  2020-12-26       Impact factor: 5.326

5.  Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data.

Authors:  Andrei S Rodin; Grigoriy Gogoshin; Seth Hilliard; Lei Wang; Colt Egelston; Russell C Rockne; Joseph Chao; Peter P Lee
Journal:  Int J Mol Sci       Date:  2021-02-26       Impact factor: 5.923

Review 6.  Advances in automated real-time flow cytometry for monitoring of bioreactor processes.

Authors:  Anna-Lena Heins; Manh Dat Hoang; Dirk Weuster-Botz
Journal:  Eng Life Sci       Date:  2021-11-12       Impact factor: 2.678

Review 7.  The Evolution of Single-Cell Analysis and Utility in Drug Development.

Authors:  Shibani Mitra-Kaushik; Anita Mehta-Damani; Jennifer J Stewart; Cherie Green; Virginia Litwin; Christèle Gonneau
Journal:  AAPS J       Date:  2021-08-13       Impact factor: 4.009

  7 in total

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