API#

Preprocessing#

pp.pre_clustering(adata[, n_rings, ...])

Create a basic leiden-pre-clustering and spatial graph.

pp._r_squared_linreg(y_true, y_pred)

Compute the R-squared value via linear regression.

Tools#

tl._get_quality_metric(adata, raw_cluster, ...)

tl._metagene_detection(adata, cluster, tresh)

tl._neighbour_detection(adata[, groupby, ...])

tl._svg_detection(adata, cluster, tresh[, ...])

tl.getComGenes(adata, raw_cluster[, tresh, ...])

Extract communication metagenes.

tl.learn_model(adata, gene_list, model_type, ...)

Learn a model with the given parameters to rank input genes list by importance.

Plotting#

pl.roc_auc_classification(adata, all_metas, ...)

Calculate and plot the ROC-AUC of spot clustering using metagenes.

pl.attention_matrix(model, data[, ...])

Create Attention Matrix plot.

pl.attention_pca(model, data[, font_size, ...])

Create Attention PCA plot.

pl.attention_umap(model, data, adata[, ...])

Create Attention UMAP plot.

pl.compute_saliency(model, data)

Compute the saliency map of the model.

pl.get_top_k_genes(model, gene_list, k[, ...])

Create Top k sender and receiver genes plot.

pl.get_top_k_genes_saliency(model, data, ...)

Create Top k genes plot (sorted by saliency scores).

pl.model_pca(model, data[, n_components, ...])

Create Model PCA plot.

pl.model_umap(model, data, adata[, ...])

Create Model UMAP plot.