=============================================================== SpatialDM: Spatial Direct Messaging Detected by bivariate Moran

About

SpatialDM (Spatial Direct Messaging, or Spatial co-expressed ligand and receptor Detected by Moran's bivariant extension), a statistical model and toolbox to identify the spatial co-expression (i.e., spatial association) between a pair of ligand and receptor. \

Uniquely, SpatialDM can distinguish co-expressed ligand and receptor pairs from spatially separating pairs, and identify the spots of interaction.

.. image:: https://github.com/StatBiomed/SpatialDM/blob/main/docs/.figs/AvsB-1.png?raw=true :width: 900px :align: center

With the analytical testing method, SpatialDM is scalable to 1 million spots within 12 min with only one core.

.. image:: https://github.com/StatBiomed/SpatialDM/blob/main/docs/.figs/runtime_aug16-1.png?raw=true :width: 600px :align: center

It comprises two main steps: \ 1) global selection spatialdm_global to identify significantly interacting LR pairs; \ 2) local selection spatialdm_local to identify local spots for each interaction.

Installation

SpatialDM is available through PyPI <https://pypi.org/project/SpatialDM/>_. To install, type the following command line and add -U for updates:

.. code-block:: bash

pip install -U SpatialDM

Alternatively, you can install from this GitHub repository for latest (often development) version by the following command line:

.. code-block:: bash

pip install -U git+https://github.com/StatBiomed/SpatialDM

Installation time: < 1 min

Quick example

Using the build-in melanoma dataset as an example, the following Python script will compute the p-value indicating whether a certain Ligand-Receptor is spatially co-expressed.

.. code-block:: python

    import spatialdm as sdm
    adata = sdm.datasets.dataset.melanoma()
    sdm.weight_matrix(adata, l=1.2, cutoff=0.2, single_cell=False) # weight_matrix by rbf kernel
    sdm.extract_lr(adata, 'human', min_cell=3)      # find overlapping LRs from CellChatDB
    sdm.spatialdm_global(adata, 1000, specified_ind=None, method='both', nproc=1)     # global Moran selection
    sdm.sig_pairs(adata, method='permutation', fdr=True, threshold=0.1)     # select significant pairs
    sdm.spatialdm_local(adata, n_perm=1000, method='both', specified_ind=None, nproc=1)     # local spot selection
    sdm.sig_spots(adata, method='permutation', fdr=False, threshold=0.1)     # significant local spots

    # visualize global and local pairs
    import spatialdm.plottings as pl
    pl.global_plot(adata, pairs=['SPP1_CD44'])
    pl.plot_pairs(adata, ['SPP1_CD44'], marker='s')

.. image:: https://github.com/StatBiomed/SpatialDM/blob/main/docs/.figs/global_plot.png?raw=true :width: 200px :align: center

.. image:: https://github.com/StatBiomed/SpatialDM/blob/main/docs/.figs/SPP1_CD44.png?raw=true :width: 600px :align: center

Detailed Manual

The full manual is at https://spatialdm.readthedocs.io, including:

Permutation-based SpatialDM (Recommended for small datasets, <10k spots)_.
Differential analyses of whole interactome among varying conditions_.

.. _Permutation-based SpatialDM (Recommended for small datasets, <10k spots): tutorial/melanoma.ipynb

.. _Differential analyses of whole interactome among varying conditions: tutorial/differential_test_intestine.ipynb

References

SpatialDM manuscript with more details is available on bioRxiv_ now and is currently under review.

.. _bioRxiv: https://www.biorxiv.org/content/10.1101/2022.08.19.504616v1/

StatBiomed / SpatialDM