Metadata-Version: 2.1
Name: SpatialDM
Version: 0.1.0
Summary: SpatialDM: Spatial co-expression Detected by bivariate Moran
Home-page: https://github.com/leeyoyohku/SpatialDM
Author: ['Zhouxuan Li', 'Tianjie Wang', 'Yuanhua Huang']
Author-email: leeyoyo@connect.hku.hk
License: Apache-2.0
Keywords: Spatial transcriptomics,Spatial association,Ligand-recptor interaction
Platform: UNKNOWN
License-File: LICENSE
Requires-Dist: anndata (>=0.6)
Requires-Dist: scanpy (>=1.5)
Requires-Dist: SpatialDE
Requires-Dist: numpy (>=1.9.0)
Requires-Dist: scipy (>=1.4.0)
Requires-Dist: statsmodels (>=0.11)
Requires-Dist: pandas (>=0.23.0)
Requires-Dist: h5py (>=2.9.0)
Requires-Dist: matplotlib (>=3.1.2)
Requires-Dist: holoviews
Requires-Dist: seaborn
Requires-Dist: selenium
Requires-Dist: scikit-learn (>=0.23)
Requires-Dist: hilearn
Requires-Dist: threadpoolctl
Provides-Extra: docs
Requires-Dist: sphinx-bootstrap-theme ; extra == 'docs'

===============================================================
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/



