API

Import SPArrOW as:

import sparrow as sp

IO

I/O.

io.merscope(path[, to_coordinate_system, ...])	Read MERSCOPE data from Vizgen.
io.xenium(path[, to_coordinate_system, ...])	Read a 10X Genomics Xenium dataset into a SpatialData object.
io.visium_hd(path[, dataset_id, ...])	Read 10x Genomics Visium HD formatted dataset.
io.read_transcripts(sdata, path_count_matrix)	Reads transcript information from a file with each row listing the x and y coordinates, along with the gene name.
io.read_resolve_transcripts(sdata, ...[, ...])	Reads and adds Resolve transcript information to a SpatialData object.
io.read_merscope_transcripts(sdata, ...[, ...])	Reads and adds merscope transcript information to a SpatialData object.
io.read_stereoseq_transcripts(sdata, ...[, ...])	Reads and adds Stereoseq transcript information to a SpatialData object.
io.create_sdata(input[, output_path, ...])	Convert input images or arrays into a SpatialData object with the image added as an image layer with name img_layer.

Image

Operations on image and labels layers.

im.add_image_layer(sdata, arr, output_layer)	Add an image layer to a SpatialData object.
im.add_labels_layer(sdata, arr, output_layer)	Add a labels layer to a SpatialData object.
im.tiling_correction(sdata[, img_layer, ...])	Function corrects for the tiling effect that occurs in some image data (e.g. resolve data).
im.enhance_contrast(sdata[, img_layer, ...])	Enhance the contrast of an image in a SpatialData object.
im.normalize(sdata, img_layer, output_layer)	Normalize the intensity of an image layer in a SpatialData object using specified percentiles.
im.min_max_filtering(sdata[, img_layer, ...])	Apply min max filtering to an image in a SpatialData object using dask (using dask_image.ndfilters.maximum_filter and dask_image.ndfilters.minimum_filter).
im.gaussian_filtering(sdata[, img_layer, ...])	Apply Gaussian filtering to an image in a SpatialData object using dask.
im.transcript_density(sdata[, img_layer, ...])	Calculate the transcript density using gaussian filter and add it to the provided spatial data.
im.combine(sdata, img_layer, output_layer[, ...])	Combines specific channels within an image layer of a SpatialData object.
im.segment(sdata, img_layer[, model, ...])	Segment images using a provided model and add segmentation results (labels layer and shapes layer) to the SpatialData object.
im.segment_points(sdata, labels_layer, ...)	Segment images using a points_layer and a prior (labels_layer) and add segmentation results (labels layer and shapes layer) to the SpatialData object.
im.cellpose_callable(img[, batch_size, ...])	Perform cell segmentation using the Cellpose model.
im.baysor_callable(img, df, name_x, name_y, ...)	Perform cell segmentation using the Baysor algorithm.
im.add_grid_labels_layer(sdata, shape, size, ...)	Adds a grid-based labels layer to the SpatialData object using either a hexagonal or square grid.
im.expand_labels_layer(sdata, labels_layer)	Expand cells in labels layer labels_layer of Spatialdata object with distance, using skimage.segmentation.expand_labels.
im.align_labels_layers(sdata, ...[, ...])	Align two labels layers.
im.filter_labels_layer(sdata, labels_layer)	Filter labels in labels layer labels_layer of Spatialdata object that have a size less than min_size or size greater than max_size.
im.merge_labels_layers(sdata, ...[, ...])	Merges two labels layers within a SpatialData object based on a specified threshold.
im.merge_labels_layers_nuclei(sdata, ...[, ...])	Merge labels layers using nuclei segmentation.
im.rasterize(sdata, shapes_layer, output_layer)	Given a shapes layer in a SpatialData object, corresponding masks are created, and added as a labels layer to the SpatialData object.
im.mask_to_original(sdata, labels_layer, ...)	Map to original.

Shape

Operations on shapes (polygons) layers.

sh.vectorize(sdata, labels_layer, output_layer)	Vectorize a labels layer.
sh.add_shapes_layer(sdata, input, output_layer)	Add a shapes layer to a SpatialData object.
sh.filter_shapes_layer(sdata, table_layer, ...)	Filter shapes in a SpatialData object.
sh.create_voronoi_boundaries(sdata[, ...])	Create Voronoi boundaries from the shapes layer of the provided SpatialData object.

Table

Operations on table (AnnData object) layers.

tb.add_table_layer(sdata, adata, ...[, ...])	Add a table layer to a SpatialData object.
tb.allocate(sdata, labels_layer[, ...])	Allocates transcripts to cells via provided labels_layer and points_layer and returns updated SpatialData object with a table layer (sdata.tables[output_layer]) holding the AnnData object with cell counts.
tb.allocate_intensity(sdata[, img_layer, ...])	Allocates intensity values from a specified image layer to corresponding cells in a SpatialData object and returns an updated SpatialData object augmented with a table layer (sdata.tables[output_layer]) AnnData object with intensity values for each cell and each (specified) channel.
tb.preprocess_transcriptomics(sdata, ...[, ...])	Preprocess a table (AnnData) attribute of a SpatialData object for transcriptomics data.
tb.preprocess_proteomics(sdata, ...[, ...])	Preprocess a table (AnnData) attribute of a SpatialData object for proteomics data.
tb.filter_on_size(sdata, labels_layer, ...)	Returns the updated SpatialData object.
tb.leiden(sdata, labels_layer, table_layer, ...)	Applies leiden clustering on the table_layer of the SpatialData object with optional UMAP calculation and gene ranking.
tb.kmeans(sdata, labels_layer, table_layer, ...)	Applies KMeans clustering on the table_layer of the SpatialData object with optional UMAP calculation and gene ranking.
tb.score_genes(sdata, labels_layer, ...[, ...])	The function loads marker genes from a CSV file and scores cells for each cell type using those markers using scanpy's sc.tl.score_genes function.
tb.score_genes_iter(sdata, labels_layer, ...)	Iterative annotation algorithm.
tb.correct_marker_genes(sdata, labels_layer, ...)	Correct celltype expression in sdata.tables[table_layer] using celltype_correction_dict.
tb.cluster_cleanliness(sdata, labels_layer, ...)	Re-calculates annotations, potentially following corrections to the list of celltypes, or after a manual update of the assigned scores per cell type via e.g. correct_marker_genes.
tb.nhood_enrichment(sdata, labels_layer, ...)	Calculate the nhood enrichment using squidpy via sq.gr.spatial_neighbors and sq.gr.nhood_enrichment.
tb.add_regionprop_features(sdata[, ...])	Enhances a SpatialData object with region property features calculated from the specified labels layer, updating its table attribute (sdata.tables[table_layer]) with these computed cellular properties.

Points

Operations on points (Dask DataFrame object) layers.

pt.add_points_layer(sdata, ddf, ...[, ...])

Add a points layer to a SpatialData object.

Plotting

Plotting functions.

General plots

pl.plot_image(sdata[, img_layer, channel, ...])	Plot an image based on given parameters.
pl.plot_shapes(sdata[, img_layer, ...])	Plots a SpatialData object.
pl.plot_labels(sdata[, labels_layer, ...])	Plot a labels layer (masks) based on given parameters.
pl.tiling_correction(sdata[, img_layer, ...])	Visualizes the effect of tiling correction.
pl.flatfield(flatfield[, output])	Visualize the correction performed per tile using a flatfield image.
pl.segment(sdata[, img_layer, shapes_layer, ...])	Visualize obtained shapes layer (i.e. segmentation mask boundaries) from a SpatialData object.

Proteomics plots

pl.snr_ratio(sdata[, ax, loglog, color])	Plot the signal to noise ratio.
pl.group_snr_ratio(sdata, groupby[, ax, ...])	Plot the signal to noise ratio.
pl.snr_clustermap(sdata[, signal_threshold, ...])
pl.signal_clustermap(sdata[, ...])
pl.clustermap(*args, **kwargs)
pl.segmentation_coverage(sdata[, ax])
pl.segmentation_size_boxplot(sdata[, ...])
pl.segments_per_area(sdata[, ax])

Transcriptomics plots

pl.sanity(sdata[, img_layer, labels_layer, ...])	Produce a sanity plot to visualize segmentation masks, and/or transcripts on top of an image.
pl.analyse_genes_left_out(sdata, ...[, ...])	Analyse and visualize the proportion of genes that could not be assigned to a cell during allocation step.
pl.transcript_density(sdata[, img_layer, ...])	Visualize the transcript density layer.
pl.preprocess_transcriptomics(sdata[, ...])	Function plots the size of the nucleus/cell related to the counts.
pl.cluster(sdata, table_layer[, key_added, ...])	Visualize clusters.
pl.score_genes(sdata, table_layer, celltypes)	Function generates following plots:
pl.cluster_cleanliness(sdata, table_layer[, ...])	Generate plots that allow assessing the "cleanliness" or accuracy of the cell clustering:
pl.nhood_enrichment(sdata, table_layer[, ...])	Plot the neighborhood enrichment across cell-type annotations.

Utils

Utility functions.

utils.bounding_box_query(sdata, ...[, ...])	Query the labels layers of a SpatialData object and the corresponding instances it annotates in sdata.tables via a bounding box query.
utils.RasterAggregator(mask_dask_array, ...)	Helper class to calulate aggregated 'sum', 'mean', 'var', 'kurtosis', 'skew', 'area', 'min', 'max' 'quantiles', 'center of mass', 'radii' or 'principal_axes' of image and labels using Dask.

Datasets

Dataset loaders.

datasets.resolve_example()	Example transcriptomics dataset
datasets.visium_hd_example([bin_size, output])	Example transcriptomics dataset
datasets.get_registry([path])	Get the Pooch registry
datasets.get_spatialdata_registry([path])	Get the Pooch SpatialData registry