cytoflow.operations.bleedthrough_linear

The bleedthrough_linear module contains two classes:

BleedthroughLinearOp – compensates for spectral bleedthrough in a Experiment using single-color controls

BleedthroughLinearDiagnostic – a diagnostic view to make sure that BleedthroughLinearOp correctly estimated its parameters.

class cytoflow.operations.bleedthrough_linear.BleedthroughLinearOp

Bases: HasStrictTraits

Apply matrix-based bleedthrough correction to a set of fluorescence channels.

This is a traditional matrix-based compensation for bleedthrough. For each pair of channels, the user specifies the proportion of the first channel that bleeds through into the second; then, the module performs a matrix multiplication to compensate the raw data.

The module can also estimate the bleedthrough matrix using one single-color control per channel.

This works best on data that has had autofluorescence removed first; if that is the case, then the autofluorescence will be subtracted from the single-color controls too.

To use, set up the controls dict with the single color controls; call estimate to parameterize the operation; check that the bleedthrough plots look good by calling BleedthroughLinearDiagnostic.plot on the BleedthroughLinearDiagnostic instance returned by default_view; and then apply on an Experiment.

controls

The channel names to correct, and corresponding single-color control FCS files to estimate the correction splines with. Must be set to use estimate.

Type:

Dict(Str, File)

spillover

The spillover “matrix” to use to correct the data. The keys are pairs of channels, and the values are proportions of spectral overlap. If ("channel1", "channel2") is present as a key, ("channel2", "channel1") must also be present. The module does not assume that the matrix is symmetric.

Type:

Dict(Tuple(Str, Str), Float)

control_conditions

Occasionally, you’ll need to specify the experimental conditions that the bleedthrough tubes were collected under (to apply the operations in the history.) Specify them here. The key is the channel name; they value is a dictionary of the conditions (same as you would specify for a cytoflow.operations.import_op.Tube )

Type:

Dict(Str, Dict(Str, Any))

Examples

Create a small experiment:

>>> import cytoflow as flow
>>> import_op = flow.ImportOp()
>>> import_op.tubes = [flow.Tube(file = "tasbe/rby.fcs")]
>>> ex = import_op.apply()

Correct for autofluorescence

>>> af_op = flow.AutofluorescenceOp()
>>> af_op.channels = ["Pacific Blue-A", "FITC-A", "PE-Tx-Red-YG-A"]
>>> af_op.blank_file = "tasbe/blank.fcs"

>>> af_op.estimate(ex)
>>> af_op.default_view().plot(ex)

>>> ex2 = af_op.apply(ex)

Create and parameterize the operation

>>> bl_op = flow.BleedthroughLinearOp()
>>> bl_op.controls = {'Pacific Blue-A' : 'tasbe/ebfp.fcs',
...                   'FITC-A' : 'tasbe/eyfp.fcs',
...                   'PE-Tx-Red-YG-A' : 'tasbe/mkate.fcs'}

Estimate the model parameters

>>> bl_op.estimate(ex2)

Plot the diagnostic plot

Note

The diagnostic plots look really bad in the online documentation. They’re better in a real-world example, I promise!

>>> bl_op.default_view().plot(ex2)

Apply the operation to the experiment

>>> ex2 = bl_op.apply(ex2)

estimate(experiment, subset=None)

Estimate the bleedthrough from simgle-channel controls in controls

set_from_fcs(filename)

apply(experiment)

Applies the bleedthrough correction to an experiment.

Parameters:

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

Returns:

A new Experiment with the bleedthrough subtracted out. The corrected channels have the following metadata added:

• linear_bleedthrough : Dict(Str : Float) The values for spillover from other channels into this channel.

• bleedthrough_channels : List(Str) The channels that were used to correct this one.

• bleedthrough_fn : Callable (Tuple(Float) –> Float) The function that will correct one event in this channel. Pass it the values specified in controls and it will return the corrected value for this channel.

Return type:

Experiment

default_view(**kwargs)

Returns a diagnostic plot to make sure spillover estimation is working.

Returns:

An IView, call BleedthroughLinearDiagnostic.plot to see the diagnostic plots

Return type:

IView

class cytoflow.operations.bleedthrough_linear.BleedthroughLinearDiagnostic

Bases: HasStrictTraits

Plots a scatterplot of each channel vs every other channel and the bleedthrough line

op

The operation whose parameters we’re viewing. If you made the instance with BleedthroughLinearOp.default_view, this is set for you already.

Type:

Instance(BleedthroughPiecewiseOp)

subset

If set, only plot this subset of the underlying data.

Type:

str

plot(experiment=None, **kwargs)

Plot a diagnostic of the bleedthrough model computation.