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FlowRepository uses a typical spillover matrix. However, there is a difference in how custom compensation is described. These choices correspond to compensation options used in FlowRepository internally. Provide a custom compensation description within the Gating-ML 2.0 file.Compensate data as prescribed by the data file (e.g., $SPILLOVER keyword in the FCS file).Gating-ML 2.0 supports the following choices for compensation description: In addition, minor adjustments due to a different scale transformation may need to be performed (see Scale transformations below). Therefore, for the Gating-ML 2.0 export, the gate coordinates have to be “unbinned” in order to bring them to the visualization space. Consequently, gates in FlowRepository are described in the “bin space”. In FlowRepository, there is an additional step of “binning” performed after the visualization transformation.
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Apply the gates as “data filters” to produce sub-populations of events.Ĭonsequently, gates in Gating-ML 2.0 are described in the “visualization space”.Perform a “visualization” transformation (e.g., ASinH, Logicle, etc.).Read and linearize data from a list mode data file (i.e., read data from an FCS file and perform the “channel to scale” conversion based on FCS keywords, such as $PnE, $PnG etc.).Gating-ML 2.0 expects the following steps to be followed when gating: As detailed in the Gating-ML import section, we are trying to approximate these with internally supported gate and transformation types where possible. Gating-ML 2.0 documents may include certain gates or data transformations that are impossible to represent internally in FlowRepository. However, we are using certain proprietary mechanisms in order to be able to reconstruct some of this mapping if the Gating-ML file has been exported from FlowRepository. Consequently, this mapping is not formally included in the exported Gating-ML. This sort of information is not formally describable in Gating-ML (the International Society for Analytical Cytology - ISAC - is developing a separate standard for the description of this sort of relations). In addition, FlowRepository’s internal gating information includes mapping of tailored gates to FCS files that the gates are applicable to. Details about these are described in the Gating-ML export section. These differences include supported gate types, scale definitions, and other details.Īll gates and population definitions saved in FlowRepository can be exported to Gating-ML however, this export involves certain conversions that are necessary to adapt to the gate and transformation types supported by Gating-ML. There are certain differences between what can be described by Gating-ML version 2.0 and what is internally supported by FlowRepository.
#ADDITIONAL COMPENSATION WITH FLOWJO 10 SOFTWARE#
FlowRepository supports Gating-ML 2.0 import and export, which may be used for the interchange of gating with other software tools supporting the Gating-ML 2.0 specification. Transformations, including compensation description are included as part of the Gating-ML specification. Gates can be uniquely identified and may be ordered into a hierarchical structure to describe a gating strategy. The specification supports rectangular gates in n dimensions (i.e., from one-dimensional range gates up to n-dimensional hyper-rectangular regions), quadrant gates in n dimensions, polygon gates, ellipsoid gates in n dimensions, and Boolean collections of any of the types of gates. Gating-ML 2.0 represents a standard developed by the International Society for Advancement of Cytometry (ISAC) for computer interchangable and unambiguous XML-based gate definitions. Gating-ML 2.0 support in FlowRepository Summary