cytoflow.operations.quad

class cytoflow.operations.quad.QuadOp

Bases: traits.has_traits.HasStrictTraits

Apply a quadrant gate to a cytometry experiment.

Creates a new metadata column named name, with values name_1, name_2, name_3, name_4 ordered clockwise from upper-left.

name

The operation name. Used to name the new metadata field in the experiment that’s created by apply()

Type:Str

xchannel

The name of the first channel to apply the range gate.

Type:Str

xthreshold

The threshold in the xchannel to gate with.

Type:Float

ychannel

The name of the secon channel to apply the range gate.

Type:Str

ythreshold

The threshold in ychannel to gate with.

Type:Float

Examples

Make a little data set.

>>> import cytoflow as flow
>>> import_op = flow.ImportOp()
>>> import_op.tubes = [flow.Tube(file = "Plate01/RFP_Well_A3.fcs",
...                              conditions = {'Dox' : 10.0}),
...                    flow.Tube(file = "Plate01/CFP_Well_A4.fcs",
...                              conditions = {'Dox' : 1.0})]
>>> import_op.conditions = {'Dox' : 'float'}
>>> ex = import_op.apply()

Create and parameterize the operation.

>>> quad = flow.QuadOp(name = "Quad",
...                    xchannel = "V2-A",
...                    xthreshold = 100,
...                    ychannel = "Y2-A",
...                    ythreshold = 1000)

Show the default view

>>> qv = quad.default_view(huefacet = "Dox",
...                        xscale = 'log',
...                        yscale = 'log')
...

>>> qv.plot(ex)

Note

If you want to use the interactive default view in a Jupyter notebook, make sure you say %matplotlib notebook in the first cell (instead of %matplotlib inline or similar). Then call default_view() with interactive = True:

qv = quad.default_view(huefacet = "Dox",
                       xscale = 'log',
                       yscale = 'log',
                       interactive = True)
qv.plot(ex)

Apply the gate and show the result

>>> ex2 = quad.apply(ex)
>>> ex2.data.groupby('Quad').size()
Quad
Quad_1    1783
Quad_2    2584
Quad_3    8236
Quad_4    7397
dtype: int64

apply(experiment)

Applies the quad gate to an experiment.

Parameters:experiment (Experiment) – the old experiment to which this op is applied Returns:a new Experiment, the same as the old Experiment but with a new column the same as the operation name. The new column is of type Category, with values name_1, name_2, name_3, and name_4, applied to events CLOCKWISE from upper-left. Return type:Experiment

default_view(**kwargs)

class cytoflow.operations.quad.QuadSelection

Bases: cytoflow.operations.base_op_views.Op2DView, cytoflow.views.scatterplot.ScatterplotView

Plots, and lets the user interact with, a quadrant gate.

interactive

is this view interactive? Ie, can the user set the threshold with a mouse click?

Type:Bool

xchannel, ychannel

The channels to use for this view’s X and Y axes. If you created the view using default_view(), this is already set.

Type:String

xscale, yscale

The way to scale the x axes. If you created the view using default_view(), this may be already set.

Type:{‘linear’, ‘log’, ‘logicle’}

op

The IOperation that this view is associated with. If you created the view using default_view(), this is already set.

Type:Instance(IOperation)

xlim, ylim

Set the min and max limits of the plots’ x and y axes.

Type:(float, float)

xfacet, yfacet

Set to one of the conditions in the Experiment, and a new row or column of subplots will be added for every unique value of that condition.

Type:String

huefacet

Set to one of the conditions in the in the Experiment, and a new color will be added to the plot for every unique value of that condition.

Type:String

huescale

How should the color scale for huefacet be scaled?

Type:{‘linear’, ‘log’, ‘logicle’}

Notes

We inherit xfacet and yfacet from cytoflow.views.ScatterplotView, but they must both be unset!

Examples

In an Jupyter notebook with %matplotlib notebook

>>> q = flow.QuadOp(name = "Quad",
...                 xchannel = "V2-A",
...                 ychannel = "Y2-A"))
>>> qv = q.default_view()
>>> qv.interactive = True
>>> qv.plot(ex2)

plot(experiment, **kwargs)

Plot the underlying scatterplot and then plot the selection on top of it.

Parameters:

• experiment (Experiment) – The Experiment to plot using this view.
• title (str) – Set the plot title
• xlabel, ylabel (str) – Set the X and Y axis labels
• huelabel (str) – Set the label for the hue facet (in the legend)
• legend (bool) – Plot a legend for the color or hue facet? Defaults to True.
• sharex, sharey (bool) – If there are multiple subplots, should they share axes? Defaults to True.
• height (float) – The height of each row in inches. Default = 3.0
• aspect (float) – The aspect ratio of each subplot. Default = 1.5
• col_wrap (int) – If xfacet is set and yfacet is not set, you can “wrap” the subplots around so that they form a multi-row grid by setting col_wrap to the number of columns you want.
• sns_style ({“darkgrid”, “whitegrid”, “dark”, “white”, “ticks”}) – Which seaborn style to apply to the plot? Default is whitegrid.
• sns_context ({“paper”, “notebook”, “talk”, “poster”}) – Which seaborn context to use? Controls the scaling of plot elements such as tick labels and the legend. Default is talk.
• despine (Bool) – Remove the top and right axes from the plot? Default is True.
• min_quantile (float (>0.0 and <1.0, default = 0.001)) – Clip data that is less than this quantile.
• max_quantile (float (>0.0 and <1.0, default = 1.00)) – Clip data that is greater than this quantile.
• xlim, ylim ((float, float)) – Set the range of the plot’s axis.
• alpha (float (default = 0.25)) – The alpha blending value, between 0 (transparent) and 1 (opaque).
• s (int (default = 2)) – The size in points^2.
• marker (a matplotlib marker style, usually a string) – Specfies the glyph to draw for each point on the scatterplot. See matplotlib.markers for examples. Default: ‘o’