`siibra.features.tabular`

Multimodal data features in tabular formats.

Submodules

Package Contents

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.

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 initialiation.

property location

DESCRIPTION = 'Cortical profiles of BigBrain staining intensities computed by Konrad Wagstyl, as described in...'

class siibra.features.tabular.CellDensityProfile(section: int, patch: int, url: str, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], id: str = None)

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 initialiation.

property boundary_positions

property cells

property density_image

property depth_image: Cortical depth image from layer boundary polygons by equidistant sampling.

property key

property layer_mask: Generates a layer mask from boundary annotations.

property layers

property location

property shape

BIGBRAIN_VOLUMETRIC_SHRINKAGE_FACTOR = 1.931

DESCRIPTION = 'Cortical profile of estimated densities of detected cell bodies (in detected cells per 0.1 cube...'

classmethod CELL_READER(b)

classmethod LAYER_READER(b)

boundary_annotation(boundary): Returns the annotation of a specific layer boundary.

layer_annotation(layer)

static poly_rev(poly)

static poly_srt(poly)

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.

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.
    """

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.

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)

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.

property data

property key

DESCRIPTION = 'Layerwise estimated densities of detected cell bodies (in detected cells per 0.1 cube...'

classmethod CELL_READER(b)

classmethod LAYER_READER(b)

class siibra.features.tabular.ReceptorDensityFingerprint(tsvfile: str, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], id: str = None)

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.

property data

property neurotransmitters: List[str]

property receptors: List[str]

property unit: str

DESCRIPTION = 'Fingerprint of densities (in fmol/mg protein) of receptors for classical neurotransmitters...'

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”

class siibra.features.tabular.ReceptorDensityProfile(receptor: str, tsvfile: str, anchor: siibra.features.anchor.AnatomicalAnchor, datasets: list = [], id: str = None)

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 initialiation.

property key

property neurotransmitter

property receptor_fullname

property unit: Optionally overridden in derived classes.

DESCRIPTION = 'Cortical profile of densities (in fmol/mg protein) of receptors for classical neurotransmitters...'

classmethod parse_tsv_data(data)

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.