phd-ms 1.2

Identifying multiscale tissue domains for spatial transcriptomic data using persistent homology.

PHD-MS: Persistent Homology for Domains at Multiple Scales

Multiscale domain identification for spatial transcriptomic data.

Installation

Simply install with pip:

pip install phd_ms

Or install from source by downloading, cd to this directory, using:

pip install .

Usage/Examples

Detailed jupyter notebook tutorials are available in the examples folder. Or simply download the file titled 'point_and_click.py' and run the file to use our clickable graphical interface. When using the point_and_click.py interface, update the directory where your data is stored by changing this line:

DATA = '/home/pbeamer/Documents/graphst/visium_hne_graphst'

Here we'll show a simple example with Visium DLPFC data, using default parameters. First, import necessary components.

import phd-ms
import scanpy

Preprocessing steps here (note that we assume a spatially-aware embedding has already been computed for your data):

INPUT_FILE= '/home/pbeamer/Documents/graphst/adata_151673.h5ad'
phd_ms.tl.preprocess_leiden(INPUT_FILE,output_file=INPUT_FILE)

Compute persistent homology and plot 10 most prominent multiscale domains:

cluster_complex,clusterings= phd_ms.tl.cluster_filtration(adata)
phd_ms.tl.map_multiscale(adata.obsm['spatial'],cluster_complex,clusterings,num_domains=10)