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.co_occurrence(adata, cluster_key[, ...])	Compute co-occurrence probability of clusters.
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.ripley(adata, cluster_key[, mode, ...])	Calculate various Ripley's statistics for point processes.
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.sepal(adata, max_neighs[, genes, n_iter, ...])	Identify spatially variable genes with Sepal.

Image

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

Plotting

pl.spatial_scatter(adata[, shape, color, ...])	Plot spatial omics data with data overlayed on top.
pl.spatial_segment(adata[, color, groups, ...])	Plot spatial omics data with segmentation masks on top.
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(adata, cluster_key[, mode, ...])	Plot Ripley's statistics 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.
pl.var_by_distance(adata, var, anchor_key[, ...])	Plot a variable using a smooth regression line with increasing distance to an anchor point.

Reading

read.visium(path, *[, counts_file, ...])	Read 10x Genomics Visium formatted dataset.
read.vizgen(path, *, counts_file, meta_file)	Read Vizgen formatted dataset.
read.nanostring(path, *, counts_file, meta_file)	Read Nanostring formatted dataset.

Tools

tl.var_by_distance(adata, groups, cluster_key)

Build a design matrix consisting of distance measurements to selected anchor point(s) for each observation.

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.merfish([path])	Pre-processed MERFISH dataset from Moffitt et al.
datasets.mibitof([path])	Pre-processed MIBI-TOF dataset from Hartmann et al.
datasets.slideseqv2([path])	Pre-processed SlideseqV2 dataset from Stickles et al.
datasets.sc_mouse_cortex([path])	Pre-processed scRNA-seq mouse cortex.
datasets.visium(sample_id, *[, ...])	Download Visium datasets from 10x Genomics.
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.