Literature DB >> 25407887

ISAC's classification results file format.

Josef Spidlen1, Chris Bray, Ryan R Brinkman.   

Abstract

Identifying homogenous sets of cell populations in flow cytometry is an important process for sorting and selecting populations of interests for further data acquisition and analysis. Many computational methods are now available to automate this process, with several algorithms partitioning cells based on high-dimensional separation versus the traditional pairwise two-dimensional visualization approach of manual gating. ISAC's classification results file format was developed to exchange the results of both manual gating and algorithmic classification approaches in a standardized way based on per event based classifications, including the potential for soft classifications expressed as the probability of an event being a member of a class.
© 2014 International Society for Advancement of Cytometry. © 2014 International Society for Advancement of Cytometry.

Entities:  

Keywords:  analysis interchange; classification; clustering; file format; flow cytometry; software interoperability; standard

Mesh:

Year:  2014        PMID: 25407887      PMCID: PMC4874736          DOI: 10.1002/cyto.a.22586

Source DB:  PubMed          Journal:  Cytometry A        ISSN: 1552-4922            Impact factor:   4.355


  5 in total

1.  Data File Standard for Flow Cytometry, version FCS 3.1.

Authors:  Josef Spidlen; Wayne Moore; David Parks; Michael Goldberg; Chris Bray; Pierre Bierre; Peter Gorombey; Bill Hyun; Mark Hubbard; Simon Lange; Ray Lefebvre; Robert Leif; David Novo; Leo Ostruszka; Adam Treister; James Wood; Robert F Murphy; Mario Roederer; Damir Sudar; Robert Zigon; Ryan R Brinkman
Journal:  Cytometry A       Date:  2010-01       Impact factor: 4.355

Review 2.  Single-cell technologies for monitoring immune systems.

Authors:  Pratip K Chattopadhyay; Todd M Gierahn; Mario Roederer; J Christopher Love
Journal:  Nat Immunol       Date:  2014-02       Impact factor: 25.606

Review 3.  Computational analysis of high-throughput flow cytometry data.

Authors:  J Paul Robinson; Bartek Rajwa; Valery Patsekin; Vincent Jo Davisson
Journal:  Expert Opin Drug Discov       Date:  2012-06-18       Impact factor: 6.098

4.  Gating-ML: XML-based gating descriptions in flow cytometry.

Authors:  Josef Spidlen; Robert C Leif; Wayne Moore; Mario Roederer; Ryan R Brinkman
Journal:  Cytometry A       Date:  2008-12       Impact factor: 4.355

5.  Flow cytometry bioinformatics.

Authors:  Kieran O'Neill; Nima Aghaeepour; Josef Spidlen; Ryan Brinkman
Journal:  PLoS Comput Biol       Date:  2013-12-05       Impact factor: 4.475

  5 in total
  4 in total

1.  ISAC's Gating-ML 2.0 data exchange standard for gating description.

Authors:  Josef Spidlen; Wayne Moore; Ryan R Brinkman
Journal:  Cytometry A       Date:  2015-05-14       Impact factor: 4.355

2.  Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data.

Authors:  Kirsten E Diggins; P Brent Ferrell; Jonathan M Irish
Journal:  Methods       Date:  2015-05-13       Impact factor: 3.608

Review 3.  Computational flow cytometry: helping to make sense of high-dimensional immunology data.

Authors:  Yvan Saeys; Sofie Van Gassen; Bart N Lambrecht
Journal:  Nat Rev Immunol       Date:  2016-06-20       Impact factor: 53.106

4.  CytoML for cross-platform cytometry data sharing.

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

  4 in total

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