API

Import Monkeybread as:

import monkeybread as mb

Calculations: calc

calc.cell_contact(adata, groupby, group1, group2)	Detects contact between two groups of cells.
calc.cell_transcript_proximity(adata, cells)	Detects transcripts in proximity or inside a given list of cells.
calc.kernel_density(adata[, groupby, ...])	Calculates kernel density estimation on large cell quantities.
calc.shortest_distances(adata, groupby, ...)	Calculates the distance from each cell in one group to the nearest cell in another group.
calc.neighborhood_profile(adata, groupby[, ...])	Calculates a neighborhood profile for each cell.
calc.ligand_receptor_score(adata, contacts, ...)	Calculates scores for ligand-receptor pairs in contacting cells.

Plotting: plot

plot.cell_contact_embedding(adata, contacts)	Shows embeddings of cells in contact.
plot.cell_contact_histplot(adata, groupby, ...)	Plots a histogram highlighting the observed cell contact compared to permutations.
plot.cell_contact_heatmap(adata, groupby[, ...])	Produces a heatmap highlighting cell contact between groups in a pairwise manner.
plot.cell_transcript_proximity(adata, cells)	Plots cell boundaries and optionally transcripts in the surrounding area.
plot.kernel_density(adata, key[, spot_size, ...])	Plots the results of monkeybread.calc.kernel_density() using scanpy.pl.embedding().
plot.shortest_distances(distances[, ...])	Plots the results of shortest distances calculations in histogram format.
plot.embedding_filter(adata, mask[, group, ...])	Shows a filtered embedding, allowing for examination of specific cells.
plot.embedding_zoom(adata[, left_pct, ...])	Shows embeddings of cells in contact with zoomed focus.
plot.ligand_receptor_scatter(actual_scores, ...)	Plots a scatterplot highlighting the observed ligand-receptor scores and significance.

Statistical Tests: stat

stat.cell_contact(adata, groupby, group1, ...)	Calculates expected cell contact and p-value using a permutation test.
stat.shortest_distances(adata, groupby, ...)	Calculates an expected distribution of shortest distances via permutation of labels.
stat.ligand_receptor_score(adata, contacts, ...)	Calculates statistical significance of ligand-receptor pairs in contacting cells.

Utility Functions: util

util.load_merscope([folder, use_cache, ...])	Loads data from MERSCOPE, in accordance to the folder structure of the FFPE data release.
util.randomize_positions(adata, radius[, ...])	Randomizes positions within a given radius.
util.subset_cells(adata, by, subset[, ...])	Subsets cells based on gene expression and optionally labels them.
util.contact_count(contacts, group1, group2)	Counts contact observed by monkeybread.calc.cell_contact().