siibra.features.tabular
Multimodal data features in tabular formats.
Submodules
- siibra.features.tabular.bigbrain_intensity_profile
- siibra.features.tabular.cell_density_profile
- siibra.features.tabular.cortical_profile
- siibra.features.tabular.gene_expression
- siibra.features.tabular.layerwise_bigbrain_intensities
- siibra.features.tabular.layerwise_cell_density
- siibra.features.tabular.receptor_density_fingerprint
- siibra.features.tabular.receptor_density_profile
- siibra.features.tabular.regional_timeseries_activity
- siibra.features.tabular.tabular
Classes
Represents a 1-dimensional profile of measurements along cortical depth, |
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Represents a 1-dimensional profile of measurements along cortical depth, |
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A set gene expressions for different candidate genes |
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Represents a table of different measures anchored to a brain location. |
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Represents a table of different measures anchored to a brain location. |
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Represents a table of different measures anchored to a brain location. |
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Represents a 1-dimensional profile of measurements along cortical depth, |
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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)
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)
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])
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)
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)
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)
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)
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)
Blood-oxygen-level-dependent (BOLD) signals per region.
- property name
Returns a short human-readable name of this feature.
- paradigm