# cytoflow.operations.gaussian¶

gaussian contains three classes:

GaussianMixtureOp – an operation that fits a Gaussian mixture model to one or more channels.

GaussianMixture1DView – a diagnostic view that shows how the GaussianMixtureOp estimated its model (on a 1D data set, using a histogram).

GaussianMixture2DView – a diagnostic view that shows how the GaussianMixtureOp estimated its model (on a 2D data set, using a scatter plot).

class cytoflow.operations.gaussian.GaussianMixtureOp[source]

This module fits a Gaussian mixture model with a specified number of components to one or more channels.

If num_components > 1, apply creates a new categorical metadata variable named name, with possible values {name}_1 …. name_n where n is the number of components. An event is assigned to name_i category if it has the highest posterior probability of having been produced by component i. If an event has a value that is outside the range of one of the channels’ scales, then it is assigned to {name}_None.

Optionally, if sigma is greater than 0, apply creates new boolean metadata variables named {name}_1{name}_n where n is the number of components. The column {name}_i is True if the event is less than sigma standard deviations from the mean of component i. If num_components is 1, sigma must be greater than 0.

Note

The sigma attribute does NOT affect how events are assigned to components in the new name variable. That is to say, if an event is more than sigma standard deviations from ALL of the components, you might expect it would be labeled as {name}_None. It is not. An event is only labeled {name}_None if it has a value that is outside of the channels’ scales.

Optionally, if posteriors is True, apply creates a new double metadata variables named {name}_1_posterior{name}_n_posterior where n is the number of components. The column {name}_i_posterior contains the posterior probability that this event is a member of component i.

Finally, the same mixture model (mean and standard deviation) may not be appropriate for every subset of the data. If this is the case, you can use the by attribute to specify metadata by which to aggregate the data before estimating (and applying) a mixture model. The number of components must be the same across each subset, though.

name

The operation name; determines the name of the new metadata column

Type

Str

channels

The channels to apply the mixture model to.

Type

List(Str)

scale

Re-scale the data in the specified channels before fitting. If a channel is in channels but not in scale, the current package-wide default (set with set_default_scale) is used.

Type

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

num_components

How many components to fit to the data? Must be a positive integer.

Type

Int (default = 1)

sigma

If not None, use this operation as a “gate”: for each component, create a new boolean variable {name}_i and if the event is within sigma standard deviations, set that variable to True. If num_components is 1, must be > 0.

Type

Float

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.

Type

List(Str)

posteriors

If True, add columns named {name}_{i}_posterior giving the posterior probability that the event is in component i. Useful for filtering out low-probability events.

Type

Bool (default = False)

Notes

We use the Mahalnobis distance as a multivariate generalization of the number of standard deviations an event is from the mean of the multivariate gaussian. If $$\vec{x}$$ is an observation from a distribution with mean $$\vec{\mu}$$ and $$S$$ is the covariance matrix, then the Mahalanobis distance is $$\sqrt{(x - \mu)^T \cdot S^{-1} \cdot (x - \mu)}$$.

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.

>>> gm_op = flow.GaussianMixtureOp(name = 'Gauss',
...                                channels = ['Y2-A'],
...                                scale = {'Y2-A' : 'log'},
...                                num_components = 2)


Estimate the clusters

>>> gm_op.estimate(ex)


Plot a diagnostic view

>>> gm_op.default_view().plot(ex)


Apply the gate

>>> ex2 = gm_op.apply(ex)


Plot a diagnostic view with the event assignments

>>> gm_op.default_view().plot(ex2)


And with two channels:

>>> gm_op = flow.GaussianMixtureOp(name = 'Gauss',
...                                channels = ['V2-A', 'Y2-A'],
...                                scale = {'V2-A' : 'log',
...                                         'Y2-A' : 'log'},
...                                num_components = 2)
>>> gm_op.estimate(ex)
>>> ex2 = gm_op.apply(ex)
>>> gm_op.default_view().plot(ex2)

estimate(experiment, subset=None)[source]

Estimate the Gaussian mixture model parameters

Parameters
• experiment (Experiment) – The data to use to estimate the mixture parameters

• subset (str (default = None)) – If set, a Python expression to determine the subset of the data to use to in the estimation.

apply(experiment)[source]

Assigns new metadata to events using the mixture model estimated in estimate.

Returns

A new Experiment with the new condition variables as described in the class documentation. Also adds the following new statistics:

• meanFloat

the mean of the fitted gaussian in each channel for each component.

• sigma(Float, Float)

the locations the mean +/- one standard deviation in each channel for each component.

• correlationFloat

the correlation coefficient between each pair of channels for each component.

• proportionFloat

the proportion of events in each component of the mixture model. only added if num_components > 1.

Return type

Experiment

default_view(**kwargs)[source]

Returns a diagnostic plot of the Gaussian mixture model.

Returns

An IView, call plot to see the diagnostic plot.

Return type

IView

class cytoflow.operations.gaussian.GaussianMixture1DView[source]

A default view for GaussianMixtureOp that plots the histogram of a single channel, then the estimated Gaussian distributions on top of it.

facets

A read-only list of the conditions used to facet this view.

Type

List(Str)

by

A read-only list of the conditions used to group this view’s data before plotting.

Type

List(Str)

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’}

plot(experiment, **kwargs)[source]

Plot the plots.

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.

• 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 ({“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.

• 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.

• 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).

• color (matplotlib color) – The color to plot the annotations. Overrides the default color cycle.

• plot_name (Str) – If this IView can make multiple plots, plot_name is the name of the plot to make. Must be one of the values retrieved from enum_plots.

cytoflow.operations.gaussian.poly_area(x, y)[source]
class cytoflow.operations.gaussian.GaussianMixture2DView[source]

A default view for GaussianMixtureOp that plots the scatter plot of a two channels, then the estimated 2D Gaussian distributions on top of it.

facets

A read-only list of the conditions used to facet this view.

Type

List(Str)

by

A read-only list of the conditions used to group this view’s data before plotting.

Type

List(Str)

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)

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’}

plot(experiment, **kwargs)[source]

Plot the plots.

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.

• 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 ({“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.

• 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.

• 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’

• color (matplotlib color) – The color to plot the annotations. Overrides the default color cycle.

• plot_name (Str) – If this IView can make multiple plots, plot_name is the name of the plot to make. Must be one of the values retrieved from enum_plots.