siibra.features.tabular

Multimodal data features in tabular formats.

Submodules

Classes

BigBrainIntensityProfile

Represents a 1-dimensional profile of measurements along cortical depth,

CellDensityProfile

Represents a 1-dimensional profile of measurements along cortical depth,

GeneExpressions

A set gene expressions for different candidate genes

LayerwiseBigBrainIntensities

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

LayerwiseCellDensity

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

ReceptorDensityFingerprint

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

ReceptorDensityProfile

Represents a 1-dimensional profile of measurements along cortical depth,

RegionalBOLD

Blood-oxygen-level-dependent (BOLD) signals per region.

Package Contents

class siibra.features.tabular.BigBrainIntensityProfile(anchor: siibra.features.anchor.AnatomicalAnchor, depths: list, values: list, boundaries: list)
Inheritance diagram of siibra.features.tabular.BigBrainIntensityProfile

Represents a 1-dimensional profile of measurements along cortical depth, measured at relative depths between 0 representing the pial surface, and 1 corresponding to the gray/white matter boundary.

Mandatory attributes are the list of depth coordinates and the list of corresponding measurement values, which have to be of equal length, as well as a unit and description of the measurements.

Optionally, the depth coordinates of layer boundaries can be specified.

Most attributes are modelled as properties, so dervide classes are able to implement lazy loading instead of direct initialization.

DESCRIPTION = "Cortical profiles of BigBrain staining intensities computed by Konrad Wagstyl, as described in...
property location
class siibra.features.tabular.CellDensityProfile(section: int, patch: int, url: str, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], id: str = None, prerelease: bool = False)
Inheritance diagram of siibra.features.tabular.CellDensityProfile

Represents a 1-dimensional profile of measurements along cortical depth, measured at relative depths between 0 representing the pial surface, and 1 corresponding to the gray/white matter boundary.

Mandatory attributes are the list of depth coordinates and the list of corresponding measurement values, which have to be of equal length, as well as a unit and description of the measurements.

Optionally, the depth coordinates of layer boundaries can be specified.

Most attributes are modelled as properties, so dervide classes are able to implement lazy loading instead of direct initialization.

boundary_annotation(boundary: Tuple[int, int]) numpy.ndarray

Returns the annotation of a specific layer boundary.

layer_annotation(layer: int) numpy.ndarray
BIGBRAIN_VOLUMETRIC_SHRINKAGE_FACTOR = 1.931
DESCRIPTION = 'Cortical profile of estimated densities of detected cell bodies (in detected cells per 0.1 cube...
property boundary_positions
property cells: pandas.DataFrame
property density_image: numpy.ndarray
property depth_image: numpy.ndarray

Cortical depth image from layer boundary polygons by equidistant sampling.

property layer_mask: numpy.ndarray

Generates a layer mask from boundary annotations.

property layers: pandas.DataFrame
property location
patch
section
property shape

(y,x)

class siibra.features.tabular.GeneExpressions(levels: List[float], z_scores: List[float], genes: List[str], additional_columns: dict, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: List = [DATASET])
Inheritance diagram of siibra.features.tabular.GeneExpressions

A set gene expressions for different candidate genes measured inside a brain structure.

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

Create a box plot per gene.

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

  • **kwargs – Keyword arguments are passed on to the plot command.

ALLEN_ATLAS_NOTIFICATION = Multiline-String
Show Value
"""
    For retrieving microarray data, siibra connects to the web API of
    the Allen Brain Atlas (© 2015 Allen Institute for Brain Science),
    available from https://brain-map.org/api/index.html. Any use of the
    microarray data needs to be in accordance with their terms of use,
    as specified at https://alleninstitute.org/legal/terms-use/.
    """
DATASET
DESCRIPTION = Multiline-String
Show Value
"""
    Gene expressions extracted from microarray data in the Allen Atlas.
    """
unit = 'expression level'
class siibra.features.tabular.LayerwiseBigBrainIntensities(anchor: siibra.features.anchor.AnatomicalAnchor, means: list, stds: list)
Inheritance diagram of siibra.features.tabular.LayerwiseBigBrainIntensities

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.

DESCRIPTION = "Layerwise averages and standard deviations of of BigBrain staining intensities computed by...
class siibra.features.tabular.LayerwiseCellDensity(segmentfiles: list, layerfiles: list, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], id: str = None, prerelease: bool = False)
Inheritance diagram of siibra.features.tabular.LayerwiseCellDensity

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.

DESCRIPTION = 'Layerwise estimated densities of detected cell bodies  (in detected cells per 0.1 cube...
property data
unit = '# detected cells/0.1mm3'
class siibra.features.tabular.ReceptorDensityFingerprint(tsvfile: str, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], id: str = None, prerelease: bool = False)
Inheritance diagram of siibra.features.tabular.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, **kwargs)

Create a bar plot of a columns of the data. :param backend: “matplotlib”, “plotly”, or others supported by pandas DataFrame

plotting backend.

Parameters:

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

Represents a 1-dimensional profile of measurements along cortical depth, measured at relative depths between 0 representing the pial surface, and 1 corresponding to the gray/white matter boundary.

Mandatory attributes are the list of depth coordinates and the list of corresponding measurement values, which have to be of equal length, as well as a unit and description of the measurements.

Optionally, the depth coordinates of layer boundaries can be specified.

Most attributes are modelled as properties, so dervide classes are able to implement lazy loading instead of direct initialization.

classmethod parse_tsv_data(data)
DESCRIPTION = 'Cortical profile of densities (in fmol/mg protein) of receptors for classical neurotransmitters...
property key
property neurotransmitter
receptor
property receptor_fullname
property unit

Optionally overridden in derived classes.

class siibra.features.tabular.RegionalBOLD(paradigm: str, **kwargs)
Inheritance diagram of siibra.features.tabular.RegionalBOLD

Blood-oxygen-level-dependent (BOLD) signals per region.

property name

Returns a short human-readable name of this feature.

paradigm