Tools

Quantification

tl.colocalization_proportion(sdata, outpath)	Calculate the proportion of colocalized transcripts for each gene.
tl.calculate_heuristic_radius_by_cells(sdata)	Estimate a neighbor-distance radius from the local geometry of a sample of cells.
tl.identify_density_k_neighbors(sdata[, ...])	Find and visualize the smallest neighbor count k that separates cell from extracellular transcript density.
tl.density_similarity(sdata[, radius, ...])	Classify transcripts by local-density enrichment relative to a Bayesian cell/background model.
tl.segment_protrusions(sdata[, layer, ...])	Identify extracellular RNA structures, quantify their morphology, and assign them to parent cells.
tl.spatial_variability(sdata[, coord_keys, ...])	Compute spatial variability of extracellular RNA using Moran's I (or another autocorrelation statistic).
tl.create_urna_metadata(sdata[, layer, ...])	Create the "xrna_metadata" table holding the unique genes found in a transcripts layer.
tl.quantify_overexpression(sdata, ...[, ...])	Quantify gene overexpression relative to a Poisson noise model derived from control codewords.
tl.extracellular_enrichment(sdata[, ...])	Compute the proportion of extracellular vs.
tl.spatial_colocalization(sdata[, ...])	Compute the proportion of spatially colocalized extracellular transcripts per gene.
tl.in_out_correlation(sdata[, ...])	Compute the correlation between intracellular and extracellular gene expression.
tl.compare_intra_extra_distribution(sdata[, ...])	Compare the spatial distribution of intracellular and extracellular transcripts per gene.
tl.segmentation_free_clustering(sdata[, ...])	Cluster transcripts without relying on pre-defined cell or tissue segmentations.
tl.get_proportion_expressed_per_cell_type(adata)	Calculate the mean expression of each feature (gene) per cell type.

Source, target and communication

tl.adaptative_source_score(sdata[, ...])	Score extracellular transcripts against nearby cells using an adaptive neighborhood size.
tl.adaptative_source_score_optimized(sdata)	Score extracellular transcripts against nearby cells using a chunked, numba-accelerated kernel.
tl.calculate_target_cells(sdata[, layer, ...])	Find the nearest cell to each transcript and annotate it with that cell's ID, type, and distance.
tl.compute_target_score(sdata[, layer, ...])	Compute, for every extracellular transcript, a per-cell-type target score plus its closest cell.
tl.define_target_by_celltype(sdata[, layer, ...])	Compute the per-gene proportion of extracellular transcripts assigned to each cell type.
tl.cluster_distribution_from_source(sdata[, ...])	Cluster genes by the distribution of their transcripts' distances to source cells.
tl.cell_contacts_with_urna_sources(sdata[, ...])	Compute cell-type x cell-type contact-count matrices based on spatial and uRNA-mediated neighborhoods.
tl.celltype_contact_matrix(sdata[, ...])	Compute a cell-type x cell-type contact matrix from the spatial proximity of "owned" transcripts.
tl.get_gene_interaction_strength(...[, ...])	Compute and plot the interaction strength between source and target cell types for one gene.
tl.communication_strength(sdata[, ...])	Compute a 3D interaction strength matrix by multiplying per-transcript source and target scores.
tl.gene_specific_interactions(sdata[, copy, ...])	Aggregate per-transcript interaction scores into gene-level mean interaction matrices.

Cell scores

tl.compute_contribution_score(sdata)

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

Factor analysis

tl.factors_to_cells(sdata[, ...])	Project extracellular-RNA factor loadings onto the cellular table for shared genes.
tl.latent_factor(sdata[, method, layer, ...])	Apply latent factor identification (NMF, LDA, or DRVI) to reduce the dimensionality of gene expression data.

Diffusion-related

tl.assess_diffusion(sdata[, gene_key, ...])	Fit a 2D Rayleigh diffusion model to transcript distances from their source cell.
tl.compute_js_divergence(P, Q[, eps])	Compute the Jensen-Shannon divergence between two probability distributions.

Multimodal quantication

tl.image_intensities_per_transcript(sdata, ...)

Extract image intensities at transcript locations and store them as a new table.