API

Import Monkeybread as:

import monkeybread as mb

Calculations: calc

calc.cellular_niches(adata, cell_type_key, ...)	Compute cellular niches.
calc.neighborhood_profile(adata, groupby[, ...])	Calculates a neighborhood profile for each cell.
calc.cell_density(adata[, groupby, groups, ...])	Calculates the spatial distribution of cells of a given cell type using kernel density estimation.
calc.ligand_receptor_score(adata, ...[, ...])	Calculates an average co-expression score of a ligand-receptor pair between neighboring cells..
calc.ligand_receptor_score_per_niche(adata, ...)	Calculates an average co-expression score of a ligand-receptor pair between neighboring cells within each cellular niche calculated by monkeybread.calc.cellular_niches().
calc.shortest_distances(adata, groupby, ...)	Calculates the distance from each cell in one group to the nearest cell in another group.
calc.shortest_distances_pairwise(adata, ...)	Calculates the distance from each cell in one group to the nearest cell in another group.
calc.cell_neighbors(adata, groupby, group1, ...)	Calculate cell neighbors.
calc.cell_neighbors_from_masks(adata, ...[, ...])	Calculate cell neighbors.
calc.number_of_neighbors(adata, groupby, ...)	Given a set of query groups of cells, group_1, group_2, .
calc.number_of_neighbors_from_masks(adata, ...)	Given a set of query groups of cells, group_1, group_2, .

Plotting: plot

plot.neighbors_profile_matrixplot(...[, ...])	Heatmap plotting the mean, normalized neighbor count of each cell type within each niche.
plot.cell_density(adata, key[, spot_size, ...])	Plots the spatial density of cells across the tissue, as calculated by monkeybread.calc.cell_density().
plot.location_and_density(adata, groupby, groups)	A wrapper around both scanpy.pl.embedding() and monkeybread.calc.cell_density() that creates multi-panel figures showing both the raw location of cells of a given cell type, by calling scanpy.pl.embedding() and their density across the tissue by calling monkeybread.calc.cell_density().
plot.shortest_distances(observed_distances)	Plot the distribution of observed distances from each cell in one group (e.g., all T cells) to its closest cell in a second group (e.g., all macrophages) as calculated by monkeybread.calc.shortest_distances().
plot.shortest_distances_pairwise(...[, ...])	Plot a heatmap of the p-values calculated by monkeybread.stat.shortest_distances_pairwise() that is the result of testing the co-localization between every cell type in one set to every cell type in a second set (e.g., myeloid cell types to T cell subtypes).
plot.cell_neighbor_embedding(adata, ...[, ...])	Shows embeddings of cells with their neighbors.
plot.number_of_neighbors(neighbor_counts[, ...])
plot.embedding_filter(adata, mask[, color, ...])	Shows a filtered embedding, allowing for examination of specific cells.
plot.embedding_zoom(adata[, left_pct, ...])	Shows embeddings of cells neighboring other cells with zoomed focus.
plot.ligand_receptor_scatter(actual_scores, ...)	Plots a scatterplot highlighting the observed ligand-receptor scores and significance.
plot.ligand_receptor_embedding(adata, ...[, ...])	Plot the ligand-receptor co-expression score between pairs of neighboring cells.
plot.ligand_receptor_embedding_zoom(adata, ...)	Plot the ligand-receptor co-expression score between pairs of neighboring cells.
plot.ligand_receptor_score_barplot_per_niche(...)	Create a barplot showing the ligand/receptor score for a specific ligand/receptor pair within each niche.
plot.ligand_receptor_score_heatmap_per_niche(...)	Create a heatmap showing the ligand/receptor scores for a specific set of ligand/receptor pairs within each niche.

Statistical Tests: stat

stat.shortest_distances(adata, groupby, ...)	Calculates an expected null distribution of shortest distances from cells in group1 to cells in group2 by permuting coordinates of cells that aren't in group1.
stat.shortest_distances_pairwise(adata, ...)	Performs the same statistical analyses implemented by monkeybread.stat.shortest_distances(), but in a pairwise fashion between every cell type in group1 and every cell type in group2.
stat.ligand_receptor_score(adata, ...[, n_perms])	Calculates statistical significance of the co-expression of ligand-receptor pairs between neighboring cells.

Utility Functions: util

util.load_merscope([folder, ...])	Loads data from MERSCOPE, in accordance to the folder structure of the FFPE data release.
util.subset_cells(adata, by, subset[, ...])	Subsets cells based on gene expression and optionally labels them.
util.load_ligand_receptor_pairs_omnipath(adata)	Load ligand-receptor pairs from the Omnipath database that are also in the provided dataset.