cytoflow.operations.quad#

Applies a (2D) quad gate to an Experiment. quad has two classes:

QuadOp – Applies the gate, given a pair of thresholds

ScatterplotQuadSelectionView – an IView that allows you to view the quadrants and/or interactively set the thresholds on a scatterplot.

ScatterplotQuadSelectionView – an IView that allows you to view the quadrants and/or interactively set the thresholds on a density plot.

class cytoflow.operations.quad.QuadOp[source]#

Bases: HasStrictTraits

Apply a quadrant gate to a cytometry experiment.

Creates a new metadata column named name, with values name_1 (upper-left quadrant), name_2 (upper-right), name_3 (lower-left), and name_4 (lower-right). This ordering is arbitrary, and was chosen to match the FACSDiva order.

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)
../../_images/cytoflow-operations-quad-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:

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', observed = True).size()
Quad
Quad_1    1783
Quad_2    2584
Quad_3    8236
Quad_4    7397
dtype: int64
apply(experiment)[source]#

Applies the quad gate to an experiment.

Parameters:

experiment (Experiment) – the 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

Raises:

CytoflowOpError – if for some reason the operation can’t be applied to this experiment. The reason is in the args attribute of CytoflowOpError.

default_view(**kwargs)[source]#

Returns an IView that allows a user to view the quad selector or interactively draw it.

Parameters:

density (bool, default = False) – If True, return a density plot instead of a scatterplot.

class cytoflow.operations.quad.ScatterplotQuadSelectionView[source]#

Bases: _QuadSelection, 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#

The channels to use for this view’s X axis. If you created the view using default_view, this is already set.

Type:

Str

ychannel#

The channels to use for this view’s Y axis. If you created the view using default_view, this is already set.

Type:

Str

xscale#

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

Type:

{‘linear’, ‘log’, ‘logicle’}

yscale#

The way to scale the y axis. 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)

huechannel#

If set, color the points using a normed color scale. The norm function is set by huescale, and the color palette can be changed by passing the palette parameter to plot.

Type:

Str

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

>>> q = flow.QuadOp(name = "Quad",
...                 xchannel = "V2-A",
...                 ychannel = "Y2-A"))
>>> qv = q.default_view()
>>> qv.interactive = True
>>> qv.plot(ex2)
plot(experiment, **kwargs)[source]#

Plot the default view, and then draw the quad selection 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.

  • xlim ((float, float)) – Set the range of the plot’s X axis.

  • ylim ((float, float)) – Set the range of the plot’s Y 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’

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

class cytoflow.operations.quad.DensityQuadSelectionView[source]#

Bases: _QuadSelection, DensityView

Plots, and lets the user interact with, a quadrant gate on a density view

interactive#

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

Type:

Bool

xchannel#

The channels to use for this view’s X axis. If you created the view using default_view, this is already set.

Type:

Str

ychannel#

The channels to use for this view’s Y axis. If you created the view using default_view, this is already set.

Type:

Str

xscale#

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

Type:

{‘linear’, ‘log’, ‘logicle’}

yscale#

The way to scale the y axis. 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)

huefacet#

You must leave the hue facet unset!

Type:

None

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

huescale#

How should the color scale for huefacet be scaled?

Type:

{‘linear’, ‘log’, ‘logicle’}

Examples

In an Jupyter notebook with %matplotlib notebook

>>> q = flow.QuadOp(name = "Quad",
...                 xchannel = "V2-A",
...                 ychannel = "Y2-A"))
>>> qv = q.default_view(density = True)
>>> qv.interactive = True
>>> qv.plot(ex2)
plot(experiment, **kwargs)[source]#

Plot the default view, and then draw the quad selection 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.

  • xlim ((float, float)) – Set the range of the plot’s X axis.

  • ylim ((float, float)) – Set the range of the plot’s Y axis.

  • gridsize (int) – The size of the grid on each axis. Default = 50

  • smoothed (bool) – Should the resulting mesh be smoothed?

  • smoothed_sigma (int) – The standard deviation of the smoothing kernel. default = 1.

  • cmap (cmap) – An instance of matplotlib.colors.Colormap. By default, the viridis colormap is used

  • under_color (matplotlib color) – Sets the color to be used for low out-of-range values.

  • bad_color (matplotlib color) – Set the color to be used for masked values.

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