This module uses the flowPeaks algorithm to assign events to clusters in an unsupervized manner.
The operation name; determines the name of the new metadata
- X Channel, Y Channel
The channels to apply the mixture model to.
- X Scale, Y Scale
Re-scale the data in Channel before fitting.
- h, h0
Scalar values that control the smoothness of the estimated distribution. Increasing h makes it “rougher,” while increasing h0 makes it smoother.
How readily should clusters be merged? Must be between 0 and 1.
- Merge Distance
How far apart can clusters be before they are merged?
A list of metadata attributes to aggregate the data before estimating the model. For example, if the experiment has two pieces of metadata,
Dox, setting By to
["Time", "Dox"]will fit the model separately to each subset of the data with a unique combination of
- class cytoflowgui.op_plugins.flowpeaks.FlowPeaksHandler(*args: Any, **kwargs: Any)¶
- class cytoflowgui.op_plugins.flowpeaks.FlowPeaksViewHandler(*args: Any, **kwargs: Any)¶
- class cytoflowgui.op_plugins.flowpeaks.FlowPeaksPlugin¶
- operation_id = 'edu.mit.synbio.cytoflow.operations.flowpeaks'¶
- view_id = 'edu.mit.synbio.cytoflowgui.op_plugins.flowpeaks'¶
- short_name = 'Flow Peaks'¶
- get_handler(model, context)¶