cytoflow.views.radviz#

A “Radviz” plot projects multivariate plots into two dimensions.

RadvizView – the IView class that makes the plot.

class cytoflow.views.radviz.RadvizView[source]#

Bases: BaseNDView

Plots a Radviz plot. Radviz plots project multivariate plots into two dimensions. Good for looking for clusters.

channels#

The channels to view

Type:

List(Str)

scale#

Re-scale the data in the specified channels before plotting. If a channel isn’t specified, assume that the scale is linear.

Type:

Dict(Str : {“linear”, “logicle”, “log”})

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’}

Notes

The Radviz plot is based on a method of “dimensional anchors” [1]. The variables are conceived as points equidistant around a unit circle, and each data point connected to each anchor by a spring whose stiffness corresponds to the value of that data point. The location of the data point is the location where springs’ tensions are minimized. Fortunately, there is fast matrix math to do this.

As per [2], the order of the anchors can make a huge difference. I’ve adapted the code from the R radviz package [3] to compute the cosine similarity of all possible circular permutations (“necklaces”). For a moderate number of anchors such as is likely to be encountered here, computing them all is completely feasible.

References

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()

Plot the radviz.

>>> flow.RadvizView(channels = ['B1-A', 'V2-A', 'Y2-A'],
...                 scale = {'Y2-A' : 'log',
...                          'V2-A' : 'log',
...                          'B1-A' : 'log'},
...                 huefacet = 'Dox').plot(ex)
../../_images/cytoflow-views-radviz-2.png
plot(experiment, **kwargs)[source]#

Plot a faceted Radviz plot

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 (Dict(Str : (float, float))) – Set the range of each channel’s axis. If unspecified, assume that the limits are the minimum and maximum of the clipped data

  • 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’

Notes

Other kwargs are passed to matplotlib.pyplot.scatter