locan.analysis.cbc.CoordinateBasedColocalization#

class locan.analysis.cbc.CoordinateBasedColocalization(meta=None, radius=100, n_steps=10)[source]#

Bases: _Analysis

Compute a colocalization index for each localization by coordinate-based colocalization (CBC).

The colocalization index is calculated for each localization in locdata by finding nearest neighbors in locdata or other_locdata within radius. A normalized number of nearest neighbors at a certain radius is computed for n_steps equally-sized steps of increasing radii ranging from 0 to radius. The Spearman rank correlation coefficent is computed for these values and weighted by Exp[-nearestNeighborDistance/distanceMax].

Parameters:

  • meta (locan.analysis.metadata_analysis_pb2.AMetadata) – Metadata about the current analysis routine.

  • radius (int | float) – The maximum radius up to which nearest neighbors are determined

  • n_steps (int) – The number of bins from which Spearman correlation is computed.

Variables:

  • count (int) – A counter for counting instantiations.

  • parameter (dict) – A dictionary with all settings for the current computation.

  • meta (locan.analysis.metadata_analysis_pb2.AMetadata) – Metadata about the current analysis routine.

  • results (pandas.DataFrame) – Coordinate-based colocalization coefficients for each input point.

Methods

__init__([meta, radius, n_steps])

compute(locdata[, other_locdata])

Run the computation.

hist([ax, bins, density])

Provide histogram as matplotlib.axes.Axes object showing hist(results).

report(*args, **kwargs)

Show a report about analysis results.

Attributes

count

A counter for counting Analysis class instantiations (class attribute).
compute(locdata, other_locdata=None)[source]#

Run the computation.

Parameters:

  • locdata (LocData) – Localization data for which CBC values are computed.

  • other_locdata (Optional[LocData]) – Localization data to be colocalized. If None other_locdata is set to locdata.

Return type:

Self

count: int = 0#

A counter for counting Analysis class instantiations (class attribute).

hist(ax=None, bins=(-1, -0.3333, 0.3333, 1), density=True, **kwargs)[source]#

Provide histogram as matplotlib.axes.Axes object showing hist(results).

Parameters:

  • ax (Optional[Axes]) – The axes on which to show the image

  • bins (Union[int, Sequence[int | float], Literal['auto']]) – Bin specification as used in matplotlib.hist().

  • density (bool) – Flag for normalization as used in matplotlib.hist(). True returns probability density function; None returns counts.

  • kwargs (Any) – Other parameters passed to matplotlib.hist().

Returns:

Axes object with the plot.

Return type:

matplotlib.axes.Axes