cytoflow.operations.threshold#

Applies a threshold gate to an Experiment. threshold has two classes:

ThresholdOp – Applies the gate, given a threshold

ThresholdSelection – an IView that allows you to view and/or interactively set the threshold.

class cytoflow.operations.threshold.ThresholdOp[source]#

Bases: HasStrictTraits

Apply a threshold gate to a cytometry experiment.

name#

The operation name. Used to name the new column in the experiment that’s created by apply

Type:

Str

channel#

The name of the channel to apply the threshold on.

Type:

Str

threshold#

The value at which to threshold this channel.

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.

>>> thresh_op = flow.ThresholdOp(name = 'Threshold',
...                              channel = 'Y2-A',
...                              threshold = 2000)

Plot a diagnostic view

>>> tv = thresh_op.default_view(scale = 'log')
>>> tv.plot(ex)
../../_images/cytoflow-operations-threshold-3.png

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:

tv = thresh_op.default_view(scale = 'log',
                            interactive = True)
tv.plot(ex)

Apply the gate, and show the result

>>> ex2 = thresh_op.apply(ex)
>>> ex2.data.groupby('Threshold', observed = True).size()
Threshold
False    15786
True      4214
dtype: int64
apply(experiment)[source]#

Applies the threshold to an experiment.

Parameters:

experiment (Experiment) – the Experiment to which this operation is applied

Returns:

a new Experiment, the same as the old experiment but with a new column of type bool with the same name as the operation name. The new condition is True if the event’s measurement in channel is greater than threshold; it is False otherwise.

Return type:

Experiment

default_view(**kwargs)[source]#
class cytoflow.operations.threshold.ThresholdSelection[source]#

Bases: Op1DView, HistogramView

Plots, and lets the user interact with, a threshold on the X axis.

interactive#

is this view interactive?

Type:

Bool

channel#

The channel this view is viewing. If you created the view using default_view, this is already set.

Type:

Str

scale#

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)

subset#

An expression that specifies the subset of the statistic to plot. Passed unmodified to pandas.DataFrame.query.

Type:

str

xfacet#

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

Type:

String

yfacet#

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

Type:

String

huefacet#

Set to one of the Experiment.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’}

Examples

In an Jupyter notebook with %matplotlib notebook

>>> t = flow.ThresholdOp(name = "Threshold",
...                      channel = "Y2-A")
>>> tv = t.default_view()
>>> tv.plot(ex2)
>>> tv.interactive = True
>>> # .... draw a threshold on the plot
>>> ex3 = thresh.apply(ex2)
plot(experiment, **kwargs)[source]#

Plot the histogram and then plot the threshold on top of it.

Parameters:

  • experiment (Experiment) – The Experiment to plot using this view.

  • title (str) – Set the plot title

  • xlabel (str) – Set the X axis label

  • ylabel (str) – Set the Y axis label

  • 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.

  • legend_out (bool) – Plot the legend outside of the plot or grid? Defaults to True.

  • sharex (bool) – If there are multiple subplots, should they share X axes? Defaults to True.

  • sharey (bool) – If there are multiple subplots, should they share Y axes? Defaults to True.

  • row_order (list) – Override the row facet value order with the given list. If a value is not given in the ordering, it is not plotted. Defaults to a “natural ordering” of all the values.

  • col_order (list) – Override the column facet value order with the given list. If a value is not given in the ordering, it is not plotted. Defaults to a “natural ordering” of all the values.

  • hue_order (list) – Override the hue facet value order with the given list. If a value is not given in the ordering, it is not plotted. Defaults to a “natural ordering” of all the values.

  • 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 this 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 ({“notebook”, “paper”, “talk”, “poster”}) – Which seaborn context to use? Controls the scaling of plot elements such as tick labels and the legend. Default is notebook.

  • palette (palette name, list, or dict) – Colors to use for the different levels of the hue variable. Should be something that can be interpreted by seaborn.color_palette, or a dictionary mapping hue levels to matplotlib colors. See https://seaborn.pydata.org/tutorial/color_palettes.html for a good overview.

  • 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.

  • lim ((float, float)) – Set the range of the plot’s data axis.

  • orientation ({‘vertical’, ‘horizontal’})

  • num_bins (int) – The number of bins to plot in the histogram. Clipped to [100, 1000]

  • histtype ({‘stepfilled’, ‘step’, ‘bar’}) – The type of histogram to draw. stepfilled is the default, which is a line plot with a color filled under the curve.

  • density (bool) – If True, re-scale the histogram to form a probability density function, so the area under the histogram is 1.

  • linewidth (float) – The width of the histogram line (in points)

  • linestyle ([‘-’ | ‘–’ | ‘-.’ | ‘:’ | “None”]) – The style of the line to plot

  • alpha (float (default = 0.5)) – The alpha blending value, between 0 (transparent) and 1 (opaque).

  • line_props (Dict) – The properties of the matplotlib.lines.Line2D that are drawn on top of the histogram. They’re passed directly to the matplotlib.lines.Line2D constructor. Default: {color : 'black', linewidth : 2}