Data File Standard for Flow Cytometry, version FCS 3.1

Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada.
Cytometry Part A (Impact Factor: 2.93). 01/2009; 77(1):97-100. DOI: 10.1002/cyto.a.20825
Source: PubMed


The flow cytometry data file standard provides the specifications needed to completely describe flow cytometry data sets within the confines of the file containing the experimental data. In 1984, the first Flow Cytometry Standard format for data files was adopted as FCS 1.0. This standard was modified in 1990 as FCS 2.0 and again in 1997 as FCS 3.0. We report here on the next generation flow cytometry standard data file format. FCS 3.1 is a minor revision based on suggested improvements from the community. 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 FCS 3.1 standard retains the basic FCS file structure and most features of previous versions of the standard. Changes included in FCS 3.1 address potential ambiguities in the previous versions and provide a more robust standard. The major changes include simplified support for international characters and improved support for storing compensation. The major additions are support for preferred display scale, a standardized way of capturing the sample volume, information about originality of the data file, and support for plate and well identification in high throughput, plate based experiments. Please see the normative version of the FCS 3.1 specification in Supporting Information for this manuscript (or at in the Current standards section) for a complete list of changes.

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    • "The latest version, FCS 3.1, was introduced in 2010 [59]. It retains the basic FCS file structure and most features of previous versions of the standard. "
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    ABSTRACT: Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.
    PLoS Computational Biology 12/2013; 9(12):e1003365. DOI:10.1371/journal.pcbi.1003365 · 4.62 Impact Factor
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    • "The flowPhyto package is compatible with conventional Flow Cytometry Standard (FCS) files (Spidlen et al., 2010), as well as those from the SeaFlow repository. SeaFlow data are stored in a custom binary file (EVT file) created every 3 min and consists of eight 16-bit integer channels (see Ribalet et al., 2010 for more details). "
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    ABSTRACT: Flow cytometry is a widely used technique among biologists to study the abundances of populations of microscopic algae living in aquatic environments. A new generation of high-frequency flow cytometers collects up to several hundred samples per day and can run continuously for several weeks. Automated computational methods are needed to analyze the different phytoplankton populations present in each sample. Software packages in the programming environment R provide powerful tools for conducting such analyses. We introduce flowPhyto, an R package that performs aggregate statistics on virtually unlimited collections of raw flow cytometry files and provides a memory efficient, parallelized solution for analyzing high-throughput flow cytometric data. Freely accessible at
    Bioinformatics 03/2011; 27(5):732-3. DOI:10.1093/bioinformatics/btr003 · 4.98 Impact Factor
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    • "These values are stored in the Flow Cytometry Standard (FCS) data file format developed by ISAC in 1984 and it is still the common representation of FCM data supported by all instruments and FCM data analysis tools [1]. FCS was recently extended to version 3.1 [2], correcting some ambiguities, improving support for international characters and storing compensation, and adding support for preferred display scale, sample volume, tracking originality of data files and plate and well identification. "
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    ABSTRACT: Flow cytometry is a widely used analytical technique for examining microscopic particles, such as cells. The Flow Cytometry Standard (FCS) was developed in 1984 for storing flow data and it is supported by all instrument and third party software vendors. However, FCS does not capture the full scope of flow cytometry (FCM)-related data and metadata, and data standards have recently been developed to address this shortcoming. The Data Standards Task Force (DSTF) of the International Society for the Advancement of Cytometry (ISAC) has developed several data standards to complement the raw data encoded in FCS files. Efforts started with the Minimum Information about a Flow Cytometry Experiment, a minimal data reporting standard of details necessary to include when publishing FCM experiments to facilitate third party understanding. MIFlowCyt is now being recommended to authors by publishers as part of manuscript submission, and manuscripts are being checked by reviewers and editors for compliance. Gating-ML was then introduced to capture gating descriptions - an essential part of FCM data analysis describing the selection of cell populations of interest. The Classification Results File Format was developed to accommodate results of the gating process, mostly within the context of automated clustering. Additionally, the Archival Cytometry Standard bundles data with all the other components describing experiments. Here, we introduce these recent standards and provide the very first example of how they can be used to report FCM data including analysis and results in a standardized, computationally exchangeable form. Reporting standards and open file formats are essential for scientific collaboration and independent validation. The recently developed FCM data standards are now being incorporated into third party software tools and data repositories, which will ultimately facilitate understanding and data reuse.
    BMC Research Notes 03/2011; 4:50. DOI:10.1186/1756-0500-4-50
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