spatialfeatureexperiment package

Submodules

spatialfeatureexperiment.aligned_spatialimage module

class spatialfeatureexperiment.aligned_spatialimage.AlignedSpatialImage(metadata=None)[source]

Bases: VirtualSpatialImage

Base class for spatial images with extent.

All images in SpatialFeatureExperiment have an extent in spatial coordinates.

__abstractmethods__ = frozenset({'img_raster', 'img_source'})
__init__(metadata=None)[source]

Initializes the AlignedSpatialImage.

Parameters:

metadata (Optional[dict]) – Optional dictionary of metadata.

property extent: Dict[str, float]

Alias for get_extent().

get_extent()[source]

Get the spatial extent of the image.

Subclasses must implement this to return their specific extent.

Return type:

Dict[str, float]

Returns:

A dictionary with keys ‘xmin’, ‘xmax’, ‘ymin’, ‘ymax’.

set_extent(extent, in_place=False)[source]

Set the spatial extent of the image.

Subclasses must implement this.

Parameters:

  • extent (Dict[str, float]) – A dictionary with keys ‘xmin’, ‘xmax’, ‘ymin’, ‘ymax’.

  • in_place (bool) – If True, modifies the object in place. Otherwise, returns a new instance.

Return type:

AlignedSpatialImage

Returns:

The modified AlignedSpatialImage.

class spatialfeatureexperiment.aligned_spatialimage.BioFormatsImage(path, extent=None, is_full=True, origin=None, transformation=None, metadata=None, validate=True)[source]

Bases: AlignedSpatialImage

On-disk representation of BioFormats images (e.g., OME-TIFF) in SFE objects.

This class uses aicsimageio for reading, aligning with R’s BioFormatsImage.

Transformations are stored and applied lazily.

__abstractmethods__ = frozenset({})
__annotations__ = {'_combined_affine_matrix': 'Optional[np.ndarray]', '_transformation_list': 'List[Dict[str, Any]]'}
__init__(path, extent=None, is_full=True, origin=None, transformation=None, metadata=None, validate=True)[source]

Initialize the BioFormatsImage.

Parameters:

  • path (Union[str, Path]) – Path to the image file (e.g., OME-TIFF).

  • extent (Optional[Dict[str, float]]) – The spatial extent of the image IF KNOWN and potentially different from full. If None, it’s inferred from metadata (full extent).

  • is_full (bool) – Whether the provided/inferred extent is the full image.

  • origin (Optional[List[float]]) – Spatial coordinates [x, y] of the image’s own origin (often [0,0] in its coordinate system).

  • transformation (Union[List[Dict[str, Any]], ndarray, None]) – Stored transformation(s) to be applied. Can be a list of dicts (e.g. [{‘type’: ‘rotate’, ‘degrees’: 90}]) or a single 2x3 or 3x3 affine numpy matrix.

  • metadata (Optional[dict]) – Additional image metadata.

  • validate (bool) – Internal use only.

__repr__()[source]

Return repr(self).

copy()[source]

Creates a copy of the BioFormatsImage.

Return type:

BioFormatsImage

get_dimensions()[source]

Get the dimensions of the image (X, Y, C, Z, T) from metadata.

This refers to the dimensions of the source image file, not affected by transformations.

Return type:

Tuple[int, int, int, int, int]

get_extent()[source]

Get the spatial extent of the image, applying stored transformations to the base extent.

Return type:

Dict[str, float]

img_raster(resolution=None, scene=0, channel=None, **kwargs)[source]

Load the image data as a PIL Image, applying transformations.

This calls to_ext_image and then extracts the PIL image. resolution here might map to scene in aicsimageio if multi-resolution is stored as scenes.

Defaulting to scene 0.

Return type:

Image

img_source(as_path=False)[source]

Get the source file path.

Return type:

str

property is_full: bool

Is the current base_extent considered the full extent of the image?

property origin: List[float]

Spatial coordinates [x, y] of the image’s own origin.

property path: Path

Get the path to the image file.

set_extent(extent, in_place=False)[source]

Set the base spatial extent of the image (pre-transformation).

To change transformations, use the transformation property or specific methods.

Return type:

BioFormatsImage

property shape: Tuple[int, int, int, int, int]

Alias for get_dimensions, returning (X,Y,C,Z,T).

to_ext_image()[source]
Return type:

ExtImage

property transformation: List[Dict[str, Any]] | ndarray | None

Stored transformation(s) to be applied.

Returns:

The combined affine matrix if available, else the list of operations.

class spatialfeatureexperiment.aligned_spatialimage.ExtImage(image, extent=None, metadata=None)[source]

Bases: AlignedSpatialImage

In-memory image using PIL/numpy arrays with spatial extent information.

__abstractmethods__ = frozenset({})
__annotations__ = {'_array': 'np.ndarray', '_pil_image_cache': 'Optional[Image.Image]'}
__init__(image, extent=None, metadata=None)[source]

Initialize an ExtImage.

Parameters:

  • image (Union[Image, ndarray]) – The image data (PIL Image or numpy array).

  • extent (Optional[Dict[str, float]]) – The spatial extent of the image. Must have keys: ‘xmin’, ‘xmax’, ‘ymin’, ‘ymax’. If None, and image is numpy array, defaults to pixel extent.

  • metadata (Optional[dict]) – Additional image metadata. Defaults to None.

__repr__()[source]

String representation.

property array: ndarray

Get the image as a numpy array (height, width, channels) or (height, width).

copy()[source]

Creates a copy of the ExtImage.

Return type:

ExtImage

get_dimensions()[source]

Get the dimensions of the image array (height, width, channels) or (height, width).

Return type:

Tuple[int, ...]

get_extent()[source]

Get the extent of the image.

Return type:

Dict[str, float]

img_raster()[source]

Get the image as a PIL Image object.

Return type:

Image

img_source(as_path=False)[source]

Get the source file path (always None for in-memory ExtImage).

Return type:

None

set_extent(extent, in_place=False)[source]

Set the extent of the image.

Return type:

ExtImage

property shape: Tuple[int, ...]

Get the shape of the image array (height, width, channels) or (height, width).

to_pil()[source]

Convert to PIL Image.

Return type:

Image

class spatialfeatureexperiment.aligned_spatialimage.SpatRasterImage(image, extent=None, metadata=None)[source]

Bases: AlignedSpatialImage

SpatRaster representation of images in SpatialFeatureExperiment objects.

This class is a wrapper around rasterio for handling GeoTIFF images, aligning with R’s SpatRasterImage which uses terra::SpatRaster.

__abstractmethods__ = frozenset({})
__annotations__ = {'_img_source': 'Optional[str]', '_in_memory': 'bool', '_src': 'Optional[rasterio.DatasetReader]'}
__del__()[source]

Clean up resources.

__init__(image, extent=None, metadata=None)[source]

Initialize a SpatRasterImage.

Parameters:

  • image (Union[DatasetReader, ndarray]) – The image data, either as a rasterio dataset or a numpy array.

  • extent (Optional[Dict[str, float]]) –

    The spatial extent of the image. Required if image is a numpy array.

    If image is a rasterio.DatasetReader, extent is inferred if not provided.

    Must have keys: ‘xmin’, ‘xmax’, ‘ymin’, ‘ymax’.

  • metadata (Optional[dict]) – Additional image metadata. Defaults to None.

__repr__()[source]

String representation.

property array: ndarray

Get the image as a numpy array (loads into memory if not already).

Returns:

NumPy array in (height, width, channels) or (height, width) format.

copy()[source]

Creates a copy of the SpatRasterImage.

Return type:

SpatRasterImage

get_dimensions()[source]

Get the dimensions of the image (height, width, channels/count).

Return type:

Tuple[int, int, int]

Returns:

This method returns (height, width, channels).

get_extent()[source]

Get the extent of the image.

Return type:

Dict[str, float]

img_raster(window=None, out_shape=None, resampling_method_str='nearest')[source]

Load the image data as a numpy array.

Parameters:

  • window (Optional[Window]) – A rasterio.windows.Window object to read a subset.

  • out_shape (Optional[Tuple[int, int, int]]) – Tuple (bands, height, width) for the output array. If None, native shape.

  • resampling_method_str (str) – Resampling method string (e.g., “nearest”, “bilinear”).

Return type:

ndarray

Returns:

Image data as a numpy array (bands, height, width) or (height, width) if single band.

img_source(as_path=False)[source]

Get the source file path if available.

Return type:

Optional[str]

property in_memory: bool

Check if the image is primarily in memory.

set_extent(extent, in_place=False)[source]

Set the extent of the image.

Return type:

SpatRasterImage

property shape: Tuple[int, int, int]

Get the shape of the image (height, width, channels/bands).

This matches common numpy/PIL dimension order after loading.

to_ext_image(maxcell=None, channel=None)[source]

Convert this SpatRasterImage to an ExtImage (in-memory PIL/numpy based).

Parameters:

  • maxcell (Optional[int]) – Maximum number of pixels for the output ExtImage. If the original image is larger, it will be downsampled.

  • channel (Union[int, List[int], None]) – Specific channel index or list of indices to select. If None, all channels are used.

Return type:

ExtImage

Returns:

An ExtImage instance.

spatialfeatureexperiment.coercions module

spatialfeatureexperiment.coercions.dataframe_to_geopandas(coords_df, spatial_coordinates_names=None, spot_diameter=None, buffer_radius=1.0, vertices_col=None, geometry_type='POINT', end_cap_style='ROUND')[source]

Convert DataFrame coordinates to a Geopandas DataFrame.

Parameters:

  • coords_df (DataFrame) – DataFrame with coordinates.

  • spatial_coordinates_names (list) – Names of the coordinate columns.

  • spot_diameter (float) – Diameter of spots for creating buffers.

  • buffer_radius (float) – Radius for buffering, used if geometry_type is ‘POLYGON’ and ‘vertices_col’ is not provided, or for ‘POINT’ if spot_diameter is given.

  • vertices_col (Optional[str]) – Name of a column in coords_df that contains lists of coordinate tuples for creating POLYGON or MULTIPOINT geometries. For POLYGON: [(x1,y1), (x2,y2), …, (xn,yn)] For MULTIPOINT: [(x1,y1), (x2,y2)] or list of Point objects.

  • geometry_type (Literal['POINT', 'POLYGON', 'MULTIPOINT']) – Type of geometry to create (‘POINT’, ‘POLYGON’, ‘MULTIPOINT’).

  • end_cap_style (Literal['ROUND', 'FLAT', 'SQUARE']) – Style of the end cap for buffered geometries (“ROUND”, “FLAT”, “SQUARE”).

Return type:

GeoDataFrame

Returns:

GeoDataFrame with geometries.

spatialfeatureexperiment.coercions.df_dict_to_gdf_dict(df_dict, spatial_coordinates_names=None, geometry_type='POINT', spot_diameter=None, buffer_radius=1.0, vertices_col=None, end_cap_style='ROUND')[source]

Convert a list of DataFrames to a list of GeoPandas DataFrames.

Parameters:

  • df_dict – Dictionary of DataFrames with coordinates.

  • spatial_coordinates_names (list) – Names of the coordinate columns.

  • spot_diameter (float) – Diameter of spots for creating buffers.

  • geometry_type (Literal['POINT', 'POLYGON', 'MULTIPOINT']) – Type of geometry to create (‘POINT’, ‘POLYGON’, ‘MULTIPOINT’).

  • buffer_radius (float) – Radius for buffering, used if geometry_type is ‘POLYGON’ and ‘vertices_col’ is not provided, or for ‘POINT’ if spot_diameter is given.

  • vertices_col (Optional[str]) – Name of a column in coords_df that contains lists of coordinate tuples for creating POLYGON or MULTIPOINT geometries. For POLYGON: [(x1,y1), (x2,y2), …, (xn,yn)] For MULTIPOINT: [(x1,y1), (x2,y2)] or list of Point objects.

  • end_cap_style (Literal['ROUND', 'FLAT', 'SQUARE']) – Style of the end cap for buffered geometries.

Return type:

Dict[str, GeoDataFrame]

Returns:

Dictionary of GeoDataFrames.

spatialfeatureexperiment.coercions.spatial_coords_to_col_geometries(coords, spot_diameter=None, end_cap_style='ROUND')[source]

Convert spatial coordinates to column geometries.

Parameters:

  • coords (BiocFrame) – Spatial coordinates DataFrame.

  • spot_diameter (float) – Diameter of spots.

  • end_cap_style (Literal['ROUND', 'FLAT', 'SQUARE']) – Style for buffered spot polygons.

Returns:

GeoDataFrame with geometries.

spatialfeatureexperiment.coercions.spe_to_sfe(spe, row_geometries=None, column_geometries=None, annotation_geometries=None, spatial_coordinates_names=None, row_geometry_type='POINT', annotation_geometry_type='POLYGON', vertices_col_row=None, vertices_col_annot=None, buffer_radius_row=1.0, buffer_radius_annot=1.0, spatial_graphs=None, spot_diameter=None, unit=None, end_cap_style='ROUND', add_centroids_if_spots_are_polygons=True)[source]

Convert a SpatialExperiment to a SpatialFeatureExperiment.

Parameters:

  • spe (SpatialExperiment) – SpatialExperiment object.

  • column_geometries (Optional[Dict[str, GeoDataFrame]]) – Column geometries.

  • row_geometries (Optional[Dict[str, GeoDataFrame]]) – Row geometries.

  • annotation_geometries (Optional[Dict[str, GeoDataFrame]]) – Annotation geometries.

  • spatial_coordinates_names (list) – Names of spatial coordinates.

  • row_geometry_type (Literal['POINT', 'POLYGON', 'MULTIPOINT']) – Default geometry type for row_geometries if converting from DataFrame.

  • annotation_geometry_type (Literal['POINT', 'POLYGON', 'MULTIPOINT']) – Default geometry type for annot_geometries if converting from DataFrame.

  • vertices_col_row (Optional[str]) – Column name with vertex lists for complex row geometries.

  • vertices_col_annot (Optional[str]) – Column name with vertex lists for complex annotation geometries.

  • buffer_radius_row (float) – Buffer radius for simple row polygons.

  • buffer_radius_annot (float) – Buffer radius for simple annotation polygons.

  • spatial_graphs (Optional[Dict[str, Union[Graph, Any]]]) – Spatial graphs.

  • spot_diameter (float) – Diameter of spots.

  • unit (str) – Unit of measurement.

  • end_cap_style (Literal['ROUND', 'FLAT', 'SQUARE']) – Style of end cap for buffered geometries.

  • add_centroids – Whether to add centroids to column geometries.

  • add_centroids_if_spots_are_polygons (bool) – If spot diameter is set, also add ‘centroids’.

Returns:

SpatialFeatureExperiment object.

spatialfeatureexperiment.sfe module

class spatialfeatureexperiment.sfe.SpatialFeatureExperiment(assays=None, row_ranges=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, reduced_dims=None, main_experiment_name=None, alternative_experiments=None, alternative_experiment_check_dim_names=True, row_pairs=None, column_pairs=None, spatial_coords=None, img_data=None, col_geometries=None, row_geometries=None, annot_geometries=None, spatial_graphs=None, unit='full_res_image_pixel', validate=True, **kwargs)[source]

Bases: SpatialExperiment

Container class for storing data from spatial-omics experiments with feature geometries.

This class extends SpatialExperiment to provide slots for geometries of spots/cells, tissue boundaries, pathologist annotations and other spatial features.

__copy__()[source]
Returns:

A shallow copy of the current SpatialExperiment.

__deepcopy__(memo=None, _nil=[])[source]
Returns:

A deep copy of the current SpatialExperiment.

__init__(assays=None, row_ranges=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, reduced_dims=None, main_experiment_name=None, alternative_experiments=None, alternative_experiment_check_dim_names=True, row_pairs=None, column_pairs=None, spatial_coords=None, img_data=None, col_geometries=None, row_geometries=None, annot_geometries=None, spatial_graphs=None, unit='full_res_image_pixel', validate=True, **kwargs)[source]

Initialize a spatial feature class.

Parameters:

  • assays (Dict[str, Any]) –

    A dictionary containing matrices, with assay names as keys and 2-dimensional matrices represented as either ndarray or spmatrix.

    Alternatively, you may use any 2-dimensional matrix that has the shape property and implements the slice operation using the __getitem__ dunder method.

    All matrices in assays must be 2-dimensional and have the same shape (number of rows, number of columns).

  • row_ranges (Union[GenomicRanges, GenomicRangesList, None]) – Genomic features, must be the same length as the number of rows of the matrices in assays.

  • row_data (Optional[BiocFrame]) –

    Features, must be the same length as the number of rows of the matrices in assays.

    Feature information is coerced to a BiocFrame. Defaults to None.

  • column_data (Optional[BiocFrame]) –

    Sample data, must be the same length as the number of columns of the matrices in assays. For instances of the SpatialExperiment class, the sample data must include a column named sample_id. If any ‘sample_id’ in the sample data is not present in the ‘sample_id’s of ‘img_data’, a warning will be issued.

    If sample_id is not present, a column with this name will be created and filled with the default value sample01.

    Sample information is coerced to a BiocFrame. Defaults to None.

  • row_names (Optional[List[str]]) – A list of strings, same as the number of rows.Defaults to None.

  • column_names (Optional[List[str]]) – A list of strings, same as the number of columns. Defaults to None.

  • metadata (Optional[dict]) – Additional experimental metadata describing the methods. Defaults to None.

  • reduced_dims (Optional[Dict[str, Any]]) –

    Slot for low-dimensionality embeddings.

    Usually a dictionary with the embedding method as keys (e.g., t-SNE, UMAP) and the dimensions as values.

    Embeddings may be represented as a matrix or a data frame, must contain a shape.

  • main_experiment_name (Optional[str]) – A string, specifying the main experiment name.

  • alternative_experiments (Optional[Dict[str, Any]]) –

    Used to manage multi-modal experiments performed on the same sample/cells.

    Alternative experiments must contain the same cells (rows) as the primary experiment. It’s a dictionary with keys as the names of the alternative experiments (e.g., sc-atac, crispr) and values as subclasses of SummarizedExperiment.

  • alternative_experiment_check_dim_names (bool) –

    Whether to check if the column names of the alternative experiment match the column names of the main experiment. This is the equivalent to the withDimnames parameter in the R implementation.

    Defaults to True.

  • row_pairs (Optional[Any]) –

    Row pairings/relationships between features.

    Defaults to None.

  • column_pairs (Optional[Any]) –

    Column pairings/relationships between cells.

    Defaults to None.

  • spatial_coords (Union[BiocFrame, ndarray, None]) –

    Optional ndarray or BiocFrame containing columns of spatial coordinates. Must be the same length as column_data.

    If spatial_coords is a BiocFrame, typical column names might include:

    • [‘x’, ‘y’]: For simple 2D coordinates.

    • [‘pxl_col_in_fullres’, ‘pxl_row_in_fullres’]: For pixel-based

    coordinates in full-resolution images.

    If spatial coordinates is a DataFrame or None, it is coerced to a BiocFrame. Defaults to None.

  • img_data (Optional[BiocFrame]) –

    Optional BiocFrame containing the image data, structured with the following columns:

    • sample_id (str): A string identifier for the sample to which an image corresponds.

    • image_id (str): A unique string identifier for each image within each sample.

    • data (VirtualSpatialImage): The image itself, represented as a VirtualSpatialImage object or one of its subclasses.

    • scale_factor (float): A numerical value that indicates the scaling factor applied to the image.

    All ‘sample_id’s in ‘img_data’ must be present in the ‘sample_id’s of ‘column_data’.

    Image data are coerced to a BiocFrame. Defaults to None.

  • col_geometries (Optional[Dict[str, GeoDataFrame]]) – Dictionary of GeoDataFrames containing geometries for columns (e.g. cells, spots).

  • row_geometries (Optional[Dict[str, GeoDataFrame]]) – Dictionary of GeoDataFrames containing geometries for rows (e.g. genes).

  • annot_geometries (Optional[Dict[str, GeoDataFrame]]) – Dictionary of GeoDataFrames containing annotation geometries (e.g. tissue boundaries).

  • spatial_graphs (Optional[Dict[str, Union[Graph, Any]]]) – A Dictionary containing spatial neighborhood graphs represented as Graph.

  • unit (str) – Unit for spatial coordinates (‘full_res_image_pixel’ or ‘micron’).

  • validate (bool) – Internal use only.

__repr__()[source]

Get string representation.

Return type:

str

property annot_geometries: Dict[str, GeoDataFrame]

Get annotation geometries.

property col_geometries: Dict[str, GeoDataFrame]

Get column geometries.

copy()[source]

Alias for __copy__().

classmethod from_spatial_experiment(input, row_geometries=None, column_geometries=None, annotation_geometries=None, spatial_coordinates_names=None, annotation_geometry_type='POLYGON', spatial_graphs=None, spot_diameter=None, unit=None)[source]

Coerce a :py:class:~`spatialexperiment.SpatialExperiment` to a SpatialFeatureExperiment.

Parameters:

  • input (SpatialExperiment) – SpatialExperiment object.

  • row_geometries (Optional[Dict[str, GeoDataFrame]]) – Row geometries. Defaults to None.

  • column_geometries (Optional[Dict[str, GeoDataFrame]]) – Column geometries. Defaults to None.

  • annotation_geometries (Optional[Dict[str, GeoDataFrame]]) – Annotation geometries. Defaults to None.

  • spatial_coordinates_names (list) – Names of spatial coordinates. Defaults to None.

  • annotation_geometry_type (str) – Type og annotation geometry. Defaults to “POLYGON”.

  • spatial_graphs (BiocFrame) – Spatial graphs. Defaults to None.

  • spot_diameter (float) – Diameter of spots. Defaults to None.

  • unit (str) – Unit of measurement. Defaults to None.

Return type:

SpatialFeatureExperiment

get_annot_geometries()[source]

Get annotation geometries.

Return type:

Dict[str, GeoDataFrame]

get_col_geometries()[source]

Get column geometries.

Return type:

Dict[str, GeoDataFrame]

get_row_geometries()[source]

Get row geometries.

Return type:

Dict[str, GeoDataFrame]

get_slice(rows=None, columns=None)[source]

Get a slice of the experiment.

Parameters:
Return type:

SpatialFeatureExperiment

Returns:

Sliced SpatialFeatureExperiment.

get_spatial_graphs()[source]

Get spatial neighborhood graphs.

Return type:

Optional[BiocFrame]

get_unit()[source]

Get the coordinate unit.

Return type:

str

property row_geometries: Dict[str, GeoDataFrame]

Get row geometries.

set_annot_geometries(geometries, in_place=False)[source]

Set annotation geometries.

Parameters:

  • geometries (Dict[str, GeoDataFrame]) – New annotation geometries.

  • in_place (bool) – Whether to modify the SpatialFeatureExperiment in place. Defaults to False.

Return type:

SpatialFeatureExperiment

Returns:

A modified SpatialFeatureExperiment object, either as a copy of the original or as a reference to the (in-place-modified) original.

set_col_geometries(geometries, in_place=False)[source]

Set column geometries.

Parameters:

  • geometries (Dict[str, GeoDataFrame]) – New column geometries.

  • in_place (bool) – Whether to modify the SpatialFeatureExperiment in place. Defaults to False.

Return type:

SpatialFeatureExperiment

Returns:

A modified SpatialFeatureExperiment object, either as a copy of the original or as a reference to the (in-place-modified) original.

set_column_data(cols, replace_column_names=False, in_place=False)[source]

Override: Set sample data.

Parameters:

  • cols (Optional[BiocFrame]) –

    New sample data. If ‘cols’ contains a column named ‘sample_id’s, a check is performed to ensure that all ‘sample_id’s in the ‘img_data’ are present. If any ‘sample_id’ in the ‘cols’ is not present in the ‘sample_id’s of ‘img_data’, a warning will be issued.

    If ‘sample_id’ is not present or ‘cols’ is None, the original ‘sample_id’s are retained.

  • replace_column_names (bool) – Whether to replace experiment’s column_names with the names from the new object. Defaults to False.

  • in_place (bool) – Whether to modify the SpatialFeatureExperiment in place. Defaults to False.

Return type:

SpatialFeatureExperiment

Returns:

A modified SpatialFeatureExperiment object, either as a copy of the original or as a reference to the (in-place-modified) original.

set_row_geometries(geometries, in_place=False)[source]

Set row geometries.

Parameters:

  • geometries (Dict[str, GeoDataFrame]) – New row geometries.

  • in_place (bool) – Whether to modify the SpatialFeatureExperiment in place. Defaults to False.

Return type:

SpatialFeatureExperiment

Returns:

A modified SpatialFeatureExperiment object, either as a copy of the original or as a reference to the (in-place-modified) original.

set_spatial_graphs(graphs, in_place=False)[source]

Set spatial neighborhood graphs.

Parameters:

  • graphs (Optional[BiocFrame]) – New spatial graphs as BiocFrame.

  • in_place (bool) – Whether to modify the SpatialFeatureExperiment in place. Defaults to False.

Return type:

SpatialFeatureExperiment

Returns:

A modified SpatialFeatureExperiment object, either as a copy of the original or as a reference to the (in-place-modified) original.

set_unit(unit, in_place=False)[source]

Set the coordinate unit.

Parameters:

  • unit (str) – New unit (‘full_res_image_pixel’ or ‘micron’).

  • in_place (bool) – Whether to modify the SpatialFeatureExperiment in place. Defaults to False.

Return type:

SpatialFeatureExperiment

Returns:

A modified SpatialFeatureExperiment object, either as a copy of the original or as a reference to the (in-place-modified) original.

property spatial_graphs: BiocFrame | None

Get spatial graphs.

property unit: str

Get coordinate unit.

Module contents