cytoflowgui.op_plugins.flowpeaks

This module uses the flowPeaks algorithm to assign events to clusters in an unsupervized manner.

Name

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.

tol

How readily should clusters be merged? Must be between 0 and 1.

Merge Distance

How far apart can clusters be before they are merged?

By

A list of metadata attributes to aggregate the data before estimating the model. For example, if the experiment has two pieces of metadata, Time and Dox, setting By to ["Time", "Dox"] will fit the model separately to each subset of the data with a unique combination of Time and Dox.

class cytoflowgui.op_plugins.flowpeaks.FlowPeaksHandler(*args: Any, **kwargs: Any)

Bases: traitsui.api.

class cytoflowgui.op_plugins.flowpeaks.FlowPeaksViewHandler(*args: Any, **kwargs: Any)

Bases: traitsui.api.

class cytoflowgui.op_plugins.flowpeaks.FlowPeaksPlugin

Bases: envisage.plugin.Plugin, cytoflowgui.op_plugins.op_plugin_base.PluginHelpMixin

operation_id = 'edu.mit.synbio.cytoflow.operations.flowpeaks'

view_id = 'edu.mit.synbio.cytoflowgui.op_plugins.flowpeaks'

short_name = 'Flow Peaks'

menu_group = 'Gates'

get_operation()

get_handler(model, context)

get_icon()