tl

Spatial domain

tl.domain_from_STAGATE(adata[, ...])	Using the STAGATE method generate the spatial domain.
tl.cal_aucell(adata, signatures[, ...])	AUCell scores were calculated for given gene lists.
tl.domain_from_local_moran(adata, score_key)	Using the local moran method generate the spatial domain.
tl.global_moran(adata[, score_labels, k, ...])	The global Moran index is calculated based on selected genes or indicators such as AUCell score.

Mask

tl.adata_from_mask(adata, mask[, location, ...])	Generate a new adata object through the selected mask region.
tl.get_mask_from_domain(adata, clusters, KSize)	A mask image is generated according to the selected category, and the mask can be processed using morphological methods such as dilation and erosion, removal of holes, and removal of small connected components

Spatial tendency

tl.ANOVA(adata, score_label[, cluster_label])	Anova of genes or other metrics
tl.wilcoxon_test(adata, mask, radius[, ...])	Wilcoxon tests were performed using the distance from each spot(cell) to the mask boundary and the expression of a gene(protein) at each spot.
tl.spearman_correlation(adata, mask, radius)	Spearman test was performed on the expression levels of genes in each class after the samples were classified equally by distance
tl.spatial_tendency(adata, mask, radius[, ...])	We used two regression methods, Loess regression, and Polynomial regression, to study the variation of the expression with the min distance from its location to the pixel of boundary.
tl.gene_cluster(adata, k[, norm, method, ...])	The regression curves of multiple genes are clustered, and the curves can be screened by adjusting a variety of indicators
tl.cal_spatialDE(adata[, gene_name, ...])	Calculate SpatialDE analysis on spatial omics data.
tl.cal_sparkX(adata[, gene_name, num_core, ...])	Calculate SPARK-X analysis on spatial omics data.
tl.cal_spark(adata[, gene_name, method, ...])	Perform SPARK analysis on spatial omics data.

Cell type proximity

tl.neighborhood_analysis(adata[, method, ...])	Compute neighborhood enrichment Z-score by permutation test.
tl.infiltration_analysis(adata[, ...])	The infiltration score was calculated by the infiltration of non-parenchymal cells into parenchymal cells.

Niche composition

tl.get_c_niche(adata, k_max[, niche_key, ...])

Parameters adata anndata.AnnData An AnnData object containing spatial omics data and spatial information. k_max: If sdbw is true, k_max is the maximum number of c-niche; If sdbw is false, it represents the number of c-niche niche_key str, optional Add the keyword of niche in adata.obs.columns. celltype_key str, optional The keyword of spot cluster in adata.obs.columns. sample_key str, optional The keyword of sample id in adata.obs.columns. sample: Union[str, list, None], The samples involved in calculating the niche. sdbw bool, optional Automated cluster number screening using sdbw. inplace bool, optional Whether to change the original adata.

Spatial communication

tl.lr_pairs(lr_data[, Annotation_key, ...])	Class for ligand and receptor pairs
tl.cell_level_communications(adata, lr_pairs)	A permutation test of ligand-receptor expression across every spot.
tl.cell_type_level_communication(adata, lr_pairs)	Cell type ligand-receptor algorithm composed of two indexes: affinity and strength.

Tensor decomposition

tl.TensorDecomposition()

Function: