cytoflow.operations.kmeans¶
Use k-means clustering to cluster events in any number of dimensions.
kmeans
has three classes:
KMeansOp
– the IOperation
to perform the clustering.
KMeans1DView
– a diagnostic view of the clustering (1D, using a histogram)
KMeans2DView
– a diagnostic view of the clustering (2D, using a scatterplot)
- class cytoflow.operations.kmeans.KMeansOp[source]¶
Bases:
traits.has_traits.HasStrictTraits
Use a K-means clustering algorithm to cluster events.
Call
estimate
to compute the cluster centroids.Calling
apply
creates a new categorical metadata variable namedname
, with possible values{name}_1
….name_n
wheren
is the number of clusters, specified withnum_clusters
.The same model may not be appropriate for different subsets of the data set. 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 model. The number of clusters is 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 clustering algorithm to.
- Type
List(Str)
- scale¶
Re-scale the data in the specified channels before fitting. If a channel is in
channels
but not inscale
, the current package-wide default (set withset_default_scale
) is used.Note
Sometimes you may see events labeled
{name}_None
– this results from events for which the selected scale is invalid. For example, if an event has a negative measurement in a channel and that channel’s scale is set to “log”, this event will be set to{name}_None
.- Type
Dict(Str : {“linear”, “logicle”, “log”})
- num_clusters¶
How many components to fit to the data? Must be a positive integer.
- Type
Int (default = 2)
- 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
andDox
, settingby
to["Time", "Dox"]
will fit the model separately to each subset of the data with a unique combination ofTime
andDox
.- Type
List(Str)
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.
>>> km_op = flow.KMeansOp(name = 'KMeans', ... channels = ['V2-A', 'Y2-A'], ... scale = {'V2-A' : 'log', ... 'Y2-A' : 'log'}, ... num_clusters = 2)
Estimate the clusters
>>> km_op.estimate(ex)
Plot a diagnostic view
>>> km_op.default_view().plot(ex)
Apply the gate
>>> ex2 = km_op.apply(ex)
Plot a diagnostic view with the event assignments
>>> km_op.default_view().plot(ex2)
- estimate(experiment, subset=None)[source]¶
Estimate the k-means clusters
- Parameters
experiment (Experiment) – The
Experiment
to use to estimate the k-means clusterssubset (str (default = None)) – A Python expression that specifies a subset of the data in
experiment
to use to parameterize the operation.
- apply(experiment)[source]¶
Apply the KMeans clustering to the data.
- Returns
a new Experiment with one additional entry in
Experiment.conditions
namedname
, of typecategory
. The new category has valuesname_1
,name_2
, etc to indicate which k-means cluster an event is a member of.The new
Experiment
also has one new statistic calledcenters
, which is a list of tuples encoding the centroids of each k-means cluster.- Return type
- default_view(**kwargs)[source]¶
Returns a diagnostic plot of the k-means clustering.
- Returns
IView
- Return type
an IView, call
KMeans1DView.plot
to see the diagnostic plot.
- class cytoflow.operations.kmeans.KMeans1DView[source]¶
Bases:
cytoflow.operations.base_op_views.By1DView
,cytoflow.operations.base_op_views.AnnotatingView
,cytoflow.views.histogram.HistogramView
A diagnostic view for
KMeansOp
(1D, using a histogram)- 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’}
- subset¶
An expression that specifies the subset of the statistic to plot. Passed unmodified to
pandas.DataFrame.query
.- Type
- xfacet¶
Set to one of the
Experiment.conditions
in theExperiment
, 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 theExperiment
, 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 theExperiment
, and a new color will be added to the plot for every unique value of that condition.- Type
String
- 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 andyfacet
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 iswhitegrid
.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 istalk
.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 fromenum_plots
.
- class cytoflow.operations.kmeans.KMeans2DView[source]¶
Bases:
cytoflow.operations.base_op_views.By2DView
,cytoflow.operations.base_op_views.AnnotatingView
,cytoflow.views.scatterplot.ScatterplotView
A diagnostic view for
KMeansOp
(2D, using a scatterplot).- 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’}
- subset¶
An expression that specifies the subset of the statistic to plot. Passed unmodified to
pandas.DataFrame.query
.- Type
- xfacet¶
Set to one of the
Experiment.conditions
in theExperiment
, 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 theExperiment
, 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 theExperiment
, and a new color will be added to the plot for every unique value of that condition.- Type
String
- 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 andyfacet
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 iswhitegrid
.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 istalk
.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 fromenum_plots
.