Getting Started

To install SpatialTis, please see the Installation section. It’s recommended to start a new virtual environment before installing the SpatialTis.

Prerequisites

Must learn:

• anndata: Tutorial written by the author of anndata and scanpy

Optional:

• numpy: For array operation

• pandas: For table operation

• scanpy: Used for cell type calling and scRNA-seq related analysis

Basic Usage

A recommended way to import SpatialTis

>>> import spatialtis as st
>>> import spatialtis.plotting as sp

Let’s load some data for analysis, SpatialTis takes AnnData as input,

>>> from anndata import read_h5ad
>>> data = read_h5ad('seqFISH.h5ad')
>>> data
AnnData object with n_obs × n_vars = 913 × 9566
    obs: 'Field of View', 'centroid', 'Region', 'cell_type'
    var: 'markers'

We could start to construct the neighbors network and profile cell-cell interactions.

>>> st.find_neighbors(data,
...                   r=180,
...                   exp_obs=['Region', 'Field of View'],
...                   centroid_key='centroid')
⏳ Find neighbors
🛠 Method: kdtree
📦 Added to AnnData, obs: 'cell_neighbors'
📦 Added to AnnData, obs: 'cell_neighbors_count'
⏱ 10ms

>>> cci = st.cell_interactions(data,
...                            exp_obs = ['Region', 'Field of View'],
...                            centroid_key='centroid',
...                            cell_type_key='cell_type')
⏳ Cell interaction
📦 Added to AnnData, uns: 'cell_interaction'
⏱ 164ms

Use Config to avoid boilerplate

Notice that we repeatedly enter exp_obs and centroid_key. Obviously, we don’t want to write these for every analysis. SpatialTis allows you to set it via Global config.

>>> from spatialtis import Config
>>> Config.exp_obs = ['Region', 'Field of View']
>>> Config.cell_type_key = 'cell_type'
>>> Config.centroid_key = 'centroid'
>>> Config.marker_key = 'markers'
>>> Config.dumps(data)  # save your config
>>> Config.loads(data)  # load config

Now we could run the analysis in a much cleaner way.

>>> st.find_neighbors(data)
>>> st.cell_interaction(data)

Save and get results

To access the result of cell-cell interactions analysis, call the get_result to get it from AnnData.uns

>>> result = st.get_result(data, 'cell_interaction')

To visualize the analysis.

>>> sp.cell_interaction(data)