Data File Standard for Flow Cytometry, VersionFCS3.2

被引:6
|
作者
Spidlen, Josef [1 ]
Moore, Wayne [2 ]
Parks, David [3 ]
Goldberg, Michael [4 ]
Blenman, Kim [5 ]
Cavenaugh, James S.
Brinkman, Ryan [6 ,7 ,8 ]
机构
[1] BD Life Sci FlowJo, Informat, Ashland, OR USA
[2] Stanford Univ, Sch Med, Genet Dept, Stanford, CA 94305 USA
[3] Stanford Univ, Stanford Shared FACS Facil, Stanford, CA 94305 USA
[4] BD Biosci, San Jose, CA USA
[5] Yale Sch Med, New Haven, CT USA
[6] BC Canc Agcy, Terry Fox Lab, Vancouver, BC, Canada
[7] Univ British Columbia, Dept Med Genet, Vancouver, BC, Canada
[8] Cytapex Bioinformat Inc, Vancouver, BC, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
flow cytometry; FCS; 3; 2; data standard; file format; bioinformatics;
D O I
10.1002/cyto.a.24225
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
FCS 3.2 is a revision of the flow cytometry data standard based on a decade of suggested improvements from the community as well as industry needs to capture instrument conditions and measurement features more precisely. The unchanged goal of the standard is to provide a uniform file format that allows files created by one type of acquisition hardware and software to be analyzed by any other type. The standard retains the overall FCS file structure and most features of previous versions, but also contains a few changes that were required to support new types of data and use cases efficiently. These changes are incompatible with existing FCS file readers. Notably, FCS 3.2 supports mixed data types to, for example, allow FCS measurements that are intrinsically integers (e.g., indices or class assignments) or measurements that are commonly captured as integers (e.g., time ticks) to be more represented as integer values, while capturing other measurements as floating-point values in the same FCS data set. In addition, keywords explicitly specifying dyes, detectors, and analytes were added to avoid having to extract those heuristically and unreliably from measurement names. Types of measurements were formalized, several keywords added, others removed, or deprecated, and various aspects of the specification were clarified. A reference implementation of the cyclic redundancy check (CRC) calculation is provided in two programming languages since a correct CRC implementation was problematic for many vendors. (c) 2020 International Society for Advancement of Cytometry
引用
收藏
页码:100 / 102
页数:3
相关论文
共 50 条
  • [41] Standard flow cytometry as a rapid and non-destructive proxy for cell nitrogen quota
    Malerba, Martino E.
    Connolly, Sean R.
    Heimann, Kirsten
    JOURNAL OF APPLIED PHYCOLOGY, 2016, 28 (02) : 1085 - 1095
  • [42] Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data
    Weber, Lukas M.
    Robinson, Mark D.
    CYTOMETRY PART A, 2016, 89A (12) : 1084 - 1096
  • [43] Lymphocytes as internal standard in oxidative burst analysis by cytometry: A new data analysis approach
    Peluso, Ilaria
    Morabito, Giuseppa
    Riondino, Silvia
    La Farina, Francesca
    Serafini, Mauro
    JOURNAL OF IMMUNOLOGICAL METHODS, 2012, 379 (1-2) : 61 - 65
  • [44] Alternatives to current flow cytometry data analysis for clinical and research studies
    Gondhalekar, Carmen
    Rajwa, Bartek
    Patsekin, Valery
    Ragheb, Kathy
    Sturgis, Jennifer
    Robinson, J. Paul
    METHODS, 2018, 134 : 113 - 129
  • [45] flowClean: Automated Identification and Removal of Fluorescence Anomalies in Flow Cytometry Data
    Fletez-Brant, Kipper
    Spidlen, Josef
    Brinkman, Ryan R.
    Roederer, Mario
    Chattopadhyay, Pratip K.
    CYTOMETRY PART A, 2016, 89A (05) : 461 - 471
  • [46] Generation of flow cytometry data files with a potentially infinite number of dimensions
    Pedreira, Carlos E.
    Costa, Elaine S.
    Barrena, Susana
    Lecrevisse, Quentin
    Almeida, Julia
    van Dongen, Jacques J. M.
    Orfao, Alberto
    CYTOMETRY PART A, 2008, 73A (09) : 834 - 846
  • [47] Managing Multi-center Flow Cytometry Data for Immune Monitoring
    White, Scott
    Laske, Karoline
    Welters, Marij
    Bidmon, Nicole
    van der Burg, Sjoerd
    Britten, Cedrik
    Enzor, Jennifer
    Staats, Janet
    Weinhold, Kent
    Gouttefangeas, Cecile
    Chan, Cliburn
    CANCER INFORMATICS, 2014, 13 : 111 - 122
  • [48] Per-Channel Basis Normalization Methods for Flow Cytometry Data
    Hahne, Florian
    Khodabakhshi, Alireza Hadj
    Bashashati, Ali
    Wong, Chao-Jen
    Gascoyne, Randy D.
    Weng, Andrew P.
    Seyfert-Margolis, Vicky
    Bourcier, Katarzyna
    Asare, Adam
    Lumley, Thomas
    Gentleman, Robert
    Brinkman, Ryan R.
    CYTOMETRY PART A, 2010, 77A (02) : 121 - 131
  • [49] A theorem proving approach for automatically synthesizing visualizations of flow cytometry data
    Sunny Raj
    Faraz Hussain
    Zubir Husein
    Neslisah Torosdagli
    Damla Turgut
    Narsingh Deo
    Sumanta Pattanaik
    Chung-Che (Jeff) Chang
    Sumit Kumar Jha
    BMC Bioinformatics, 18
  • [50] Autogating in Flow Cytometry Data using SVM Classifiers for Bacterioplankton Identification
    Cordeiro, Elionai Moura
    Wanderley, Bruno M. S.
    Amorim de Araujo, Daniel Sabino
    Doria Neto, Adriao Duarte
    PROCEEDINGS 2017 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI), 2017, : 1265 - 1269