Tools

Quantification

tl.colocalization_proportion(sdata, outpath)	Calculate the proportion of colocalized transcripts for each gene in the provided AnnData object.
tl.spatial_variability(sdata[, coord_keys, ...])	Computes spatial variability of extracellular RNA using Moran's I.
tl.create_urna_metadata(sdata[, layer, ...])	Creates a new table within the SpatialData object that contains a 'gene' column with the unique gene names extracted from the specified points layer.
tl.quantify_overexpression(sdata, ...[, ...])	Compare counts per gene with counts per non-gene feature.
tl.extracellular_enrichment(sdata[, ...])	Computes the proportion of transcripts classified as extracellular or intracellular for each gene and calculates additional metrics, including log fold change of extracellular to intracellular proportions.
tl.spatial_colocalization(sdata[, ...])	Computes spatial variability of extracellular RNA using Moran's I.
tl.in_out_correlation(sdata[, ...])	Computes the correlation between intracellular and extracellular gene expressionusing k-nearest extracellular bins.
tl.compare_intra_extra_distribution(sdata[, ...])	Compare the spatial distribution of intracellular and extracellular transcripts for each gene.
tl.segmentation_free_clustering(sdata[, ...])	Clusters transcriptomic data without relying on pre-defined cell or tissue segmentations.It supports multiple clustering methods, with Points2Regions being the default.
tl.get_proportion_expressed_per_cell_type(adata)	Calculate the proportion of expression for each feature (gene) per cell type.

Source, target and communication

tl.compute_distant_cells_proportion(sdata[, ...])	Compute the proportion of transcripts for each gene that are located beyond a specified distance (in um) from their closest source cell, and add the result to the metadata of the SpatialData object.
tl.compute_source_cells(sdata[, ...])	Compute the source of extracellular RNA by linking detected extracellular transcripts to specific cell types in the spatial data.
tl.compute_source_score(sdata[, layer, ...])	Compute a source score for extracellular transcripts based on nearby cell types and gene expression profiles, using exponential distance decay.
tl.distance_to_source_cell(sdata[, layer, ...])	It computes the distance from each extracellular RNA transcript to the nearest source cell based on their spatial coordinates.
tl.calculate_target_cells(sdata[, layer, ...])	Identifies the nearest cell to each transcript based on spatial coordinates and annotates the transcript data with the ID, cell type, and distance to the closest cell.
tl.compute_target_score(sdata[, layer, ...])	Computes scores for each extracellular transcript targeting specific cell types using spatial proximity.
tl.define_target_by_celltype(sdata[, layer, ...])	It calculates a cross-tabulation between features (e.g., extracellular transcripts) and cell types,and then normalizes the result to provide the proportion of each feature associated with each cell type.
tl.cluster_distribution_from_source(sdata[, ...])	Clusters genes based on the distribution of distances of extracellular transcripts from their source cell.
tl.get_gene_interaction_strength(...[, ...])	Calculates the interaction strength between source and target cell types for a specified gene by multiplying the proportions of the gene in the source and target cell types.
tl.communication_strength(sdata[, ...])	Compute a 3D interaction strength matrix from the source table in SpatialData.
tl.gene_specific_interactions(sdata[, copy, ...])	Group the read-specific interaction scores into gene-specific scores

Cell scores

tl.compute_contribution_score(sdata)

Compute a normalized extracellular RNA (uRNA) contribution score for each cell.

Factor analysis

tl.factors_to_cells(sdata[, ...])	Extracts extracellular RNA data and associated NMF factor loadings, intersects the gene annotations between the extracellular data and the cellular data, and applies the NMF factors to annotate the cellular data with exRNA-related factors.
tl.latent_factor(sdata[, method, layer, ...])	Applies latent factor identification (NMF, LDA, or DRVI) to reduce dimensionality of gene expression data.

Diffusion-related

tl.assess_diffusion(sdata[, gene_key, ...])	Computes goodness-of-fit metrics for the diffusion pattern of unassigned RNA by testing against a Rayleigh distribution.
tl.compute_js_divergence(P, Q[, eps])	Compute the Jensen-Shannon divergence between two probability distributions.

Multimodal quantication

tl.image_intensities_per_transcript(sdata, ...)

Extracts image intensities at transcript locations and adds them as a new layer in the SpatialData object.