A standard for outlining the minimum information required to unambiguously record the experimental details of flow cytometry experiments
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- Flow cytometry protocol package. Flow cytometry specific classes shall be extended from FuGE to support flow cytometry specific actions including computational and data processing actions.
- Data package. Event though FCS is supposed to
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- Material package. Physical characteristics of material used as flow cytometry sample are describable using general FuGE concepts. FCM extension mainly captures the role of various subcomponents in conjugated samples (i.e., reporter and detector), which is crucial to associate detected fluorescence values with properties of biological interests. Several flow cytometry use cases will be modeled within the flow cytometry extended FuGE in order to provide immediate feedback and validation.
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In flow cytometry, the emission spectral overlap of fluorescent labels makes it necessary to correct detected signals before using the values as a basis for any other analyses. Fluorescence compensation is the process by which the amounts of spectral overlap are subtracted from the total detected signals to yield an estimate of the actual amount of each dye. Compensation-ML provides a detailed XML-based specification on how to unambiguously describe compensations based on spillover matrices and how to compensate flow cytometry data based on these compensation descriptions.
While analyzing flow cytometry data, various parameter transformations are performed to provide user-friendly visualization and/or to perform statistical analyses and interpret the data. Transformation-ML provides a detailed XML-based specification on how to describe these transformations.
FlowRDF is a proposal on how to provide metadata information in a standardized and extensible way using the Resource Description Framework (RDF).
The ontology for flow cytometry is now being developed as part of the Ontology for Biomedical Investigations (OBI). Please see the OBI site for more information.
Model driven architecture technologies (e.g., AndroMDA) will be applied to the object model to assist deriving the database schema. This will be evaluated, adjusted and optimized for performance reasons.