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MIFlowCyt: the minimum information about a Flow Cytometry Experiment.

ABSTRACT: A fundamental tenet of scientific research is that published results are open to independent validation and refutation. Minimum data standards aid data providers, users, and publishers by providing a specification of what is required to unambiguously interpret experimental findings. Here, we present the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard, stating the minimum information required to report flow cytometry (FCM) experiments. We brought together a cross-disciplinary international collaborative group of bioinformaticians, computational statisticians, software developers, instrument manufacturers, and clinical and basic research scientists to develop the standard. The standard was subsequently vetted by the International Society for Advancement of Cytometry (ISAC) Data Standards Task Force, Standards Committee, membership, and Council. The MIFlowCyt standard includes recommendations about descriptions of the specimens and reagents included in the FCM experiment, the configuration of the instrument used to perform the assays, and the data processing approaches used to interpret the primary output data. MIFlowCyt has been adopted as a standard by ISAC, representing the FCM scientific community including scientists as well as software and hardware manufacturers. Adoptionof MIFlowCyt by the scientific and publishing communities will facilitate third-party understanding and reuse of FCM data.

SUBMITTER: Lee JA

PROVIDER: S-EPMC2773297 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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MIFlowCyt: the minimum information about a Flow Cytometry Experiment.

Lee Jamie A JA Spidlen Josef J Boyce Keith K Cai Jennifer J Crosbie Nicholas N Dalphin Mark M Furlong Jeff J Gasparetto Maura M Goldberg Michael M Goralczyk Elizabeth M EM Hyun Bill B Jansen Kirstin K Kollmann Tobias T Kong Megan M Leif Robert R McWeeney Shannon S Moloshok Thomas D TD Moore Wayne W Nolan Garry G Nolan John J Nikolich-Zugich Janko J Parrish David D Purcell Barclay B Qian Yu Y Selvaraj Biruntha B Smith Clayton C Tchuvatkina Olga O Wertheimer Anne A Wilkinson Peter P Wilson Christopher C Wood James J Zigon Robert R Scheuermann Richard H RH Brinkman Ryan R RR

Cytometry. Part A : the journal of the International Society for Analytical Cytology 20081001 10

A fundamental tenet of scientific research is that published results are open to independent validation and refutation. Minimum data standards aid data providers, users, and publishers by providing a specification of what is required to unambiguously interpret experimental findings. Here, we present the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard, stating the minimum information required to report flow cytometry (FCM) experiments. We brought together a cross-discip ...[more]

PMID: 18752282

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Project description: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.

Dataset Information

MIFlowCyt: the minimum information about a Flow Cytometry Experiment.

Publications

MIFlowCyt: the minimum information about a Flow Cytometry Experiment.

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