API

Import Squidpy as:

import squidpy as sq

Graph

gr.spatial_neighbors(adata[, spatial_key, …])	Create a graph from spatial coordinates.
gr.nhood_enrichment(adata, cluster_key[, …])	Compute neighborhood enrichment by permutation test.
gr.centrality_scores(adata, cluster_key[, …])	Compute centrality scores per cluster or cell type.
gr.interaction_matrix(adata, cluster_key[, …])	Compute interaction matrix for clusters.
gr.ligrec(adata, cluster_key[, …])	Perform the permutation test as described in [Efremova et al., 2020].
gr.spatial_autocorr(adata[, …])	Calculate Global Autocorrelation Statistic (Moran’s I or Geary’s C).
gr.ripley_k(adata, cluster_key[, …])	Calculate Ripley’s K statistics for each cluster in the tissue coordinates.
gr.co_occurrence(adata, cluster_key[, …])	Compute co-occurrence probability of clusters.

Image

im.process(img[, layer, method, size, …])	Process an image by applying a transformation.
im.segment(img[, layer, method, channel, …])	Segment an image.
im.calculate_image_features(adata, img[, …])	Calculate image features for all observations in adata.

Plotting

pl.nhood_enrichment(adata, cluster_key[, …])	Plot neighborhood enrichment.
pl.centrality_scores(adata, cluster_key[, …])	Plot centrality scores.
pl.interaction_matrix(adata, cluster_key[, …])	Plot cluster interaction matrix.
pl.ligrec(adata[, cluster_key, …])	Plot the result of a receptor-ligand permutation test.
pl.ripley_k(adata, cluster_key[, palette, …])	Plot Ripley’s K estimate for each cluster.
pl.co_occurrence(adata, cluster_key[, …])	Plot co-occurrence probability ratio for each cluster.
pl.extract(adata[, obsm_key, prefix])	Create a temporary anndata.AnnData object for plotting.

Datasets

datasets.four_i([path])	Pre-processed subset 4i dataset from Gut et al.
datasets.imc([path])	Pre-processed subset IMC dataset from Jackson et al.
datasets.seqfish([path])	Pre-processed subset seqFISH dataset from Lohoff et al.
datasets.visium_hne_adata([path])	Pre-processed 10x Genomics Visium H&E dataset.
datasets.visium_hne_adata_crop([path])	Pre-processed subset 10x Genomics Visium H&E dataset.
datasets.visium_fluo_adata([path])	Pre-processed 10x Genomics Visium Fluorecent dataset.
datasets.visium_fluo_adata_crop([path])	Pre-processed subset 10x Genomics Visium Fluorescent dataset.
datasets.visium_hne_image([path])	H&E image from 10x Genomics Visium dataset.
datasets.visium_hne_image_crop([path])	Cropped H&E image from 10x Genomics Visium dataset.
datasets.visium_fluo_image_crop([path])	Cropped Fluorescent image from 10x Genomics Visium dataset.