High throughput automated analysis of big flow cytometry data
作者: Albina RahimJustin MeskasSibyl DrisslerAlice YueAnna LorencAdam LaingNamita SaranJacqui WhiteLucie Abeler-DörnerAdrian HaydayRyan R. Brinkman
作者单位: 1Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada
2Department of Immunobiology, King’s College London, United Kingdom
3Wellcome Trust Sanger Institute, Hinxton, United Kingdom
4The Francis Crick Institute, London, United Kingdom
5School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
6Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
7Department of Bioinformatics, University of British Columbia, Vancouver, BC, Canada
刊名: Methods, 2018, Vol.134-135 , pp.164-176
来源数据库: Elsevier Journal
DOI: 10.1016/j.ymeth.2017.12.015
关键词: Flow cytometryAutomated analysisBioinformatics
英文摘要: Abstract(#br)The rapid expansion of flow cytometry applications has outpaced the functionality of traditional manual analysis tools used to interpret flow cytometry data. Scientists are faced with the daunting prospect of manually identifying interesting cell populations in 50-dimensional datasets, equalling the complexity previously only reached in mass cytometry. Data can no longer be analyzed or interpreted fully by manual approaches. While automated gating has been the focus of intense efforts, there are many significant additional steps to the analytical pipeline ( e.g. , cleaning the raw files, event outlier detection, extracting immunophenotypes). We review the components of a customized automated analysis pipeline that can be generally applied to large scale flow cytometry data....
全文获取路径: Elsevier  (合作)
影响因子:3.641 (2012)

  • analysis 分析
  • throughput 吞吐量
  • cytometry 血细胞计数
  • automated 自动