API overview

Import chrysalis as:

import chrysalis as ch

Core functions

Identifying spatially variable genes, dimensionality reduction, archetypal analysis.

Main functions required to identify tissue compartments.

`detect_svgs`(adata[, min_spots, top_svg, ...])	Calculate spatial autocorrelation (Moran's I) to define spatially variable genes.
`pca`(adata[, n_pcs])	Perform PCA (Principal Component Analysis) to calculate PCA coordinates, loadings, and variance decomposition.
`aa`(adata[, n_archetypes, pca_key, n_pcs, ...])	Run archetypal analysis on the low-dimensional embedding.

Visualization module.

Visualizations to examine the identified compartments in the tissue space.

`plot`(adata[, dim, hexcodes, seed, ...])	Visualize tissue compartments using MIP (Maximum Intensity Projection).
`plot_samples`(adata, rows, cols, dim[, ...])	Visualize multiple samples from an AnnData object integrated with chrysalis.integrate_adatas in a single figure.
`plot_compartment`(adata, fig, ax, selected_dim)	Visualize individual tissue compartments.
`plot_compartments`(adata[, ncols, size, ...])	Visualize all compartments as individual subplots.

Plot quality control metrics to determine the correct number of spatially variable genes or PCs (Principal Components).

`plot_explained_variance`(adata)	Plot the explained variance of the calculated PCs (Principal Components).
`plot_svgs`(adata)	Plot a rank-order chart displaying the Moran's I values.

Generate a visualization of the top-contributing genes for each tissue compartment.

`plot_heatmap`(adata[, figsize, ...])	Plot heatmap showing the weights of spatially variable genes for each identified tissue compartment.
`plot_weights`(adata[, hexcodes, seed, ...])	Plot 20 top genes for each tissue compartment.

Sample interation, spatially variable gene contributions.

`integrate_adatas`(adatas[, sample_names, ...])	Integrate multiple samples stored in AnnData objects.
`harmony_integration`(adata, covariates[, ...])	Integrate data using harmonypy, the Python implementation of the R package Harmony.
`get_compartment_df`(adata[, weights])	Get spatially variable gene weights/expression values as a pandas DataFrame.