siibra.features.tabular.receptor_density_fingerprint

Classes

ReceptorDensityFingerprint

Represents a table of different measures anchored to a brain location.

Module Contents

class siibra.features.tabular.receptor_density_fingerprint.ReceptorDensityFingerprint(tsvfile: str, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], id: str = None, prerelease: bool = False)
Inheritance diagram of siibra.features.tabular.receptor_density_fingerprint.ReceptorDensityFingerprint

Represents a table of different measures anchored to a brain location.

Columns represent different types of values, while rows represent different samples. The number of columns might thus be interpreted as the feature dimension.

As an example, receptor fingerprints use rows to represent different neurotransmitter receptors, and separate columns for the mean and standard deviations measure across multiple tissue samples.

classmethod parse_tsv_data(data: dict)
plot(*args, receptors: List[str] = None, backend: str = 'matplotlib', **kwargs)

Create a bar plot of receptor density fingerprint.

Parameters:

  • receptors (List[str], optional) – Plot a subset of receptors.

  • backend (str) – “matplotlib”, “plotly”, or others supported by pandas DataFrame plotting backend.

  • **kwargs – takes Matplotlib.pyplot keyword arguments

polar_plot(*args, backend='matplotlib', **kwargs)

Create a polar plot of the fingerprint. backend: str

“matplotlib” or “plotly”

DESCRIPTION = 'Fingerprint of densities (in fmol/mg protein) of receptors for classical neurotransmitters...
property data
property neurotransmitters: List[str]
property receptors: List[str]
property unit: str