===============================================================
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
==========
| Li, Z., Wang, T., Liu, P., & Huang, Y. (2023). SpatialDM for rapid
identification of spatially co-expressed ligand–receptor and revealing
cell–cell communication patterns. Nature communications, 14(1), 3995.
https://www.nature.com/articles/s41467-023-39608-w
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"description": "===============================================================\nSpatialDM: Spatial Direct Messaging Detected by bivariate Moran\n===============================================================\n\nAbout\n=====\n\nSpatialDM (Spatial Direct Messaging, or Spatial co-expressed ligand and \nreceptor Detected by Moran's bivariant extension), a statistical model and \ntoolbox to identify the spatial co-expression (i.e., spatial association) \nbetween a pair of ligand and receptor. \\\n\nUniquely, SpatialDM can distinguish co-expressed ligand and receptor pairs from \nspatially separating pairs, and identify the spots of interaction.\n\n.. image:: https://github.com/StatBiomed/SpatialDM/blob/main/docs/.figs/AvsB-1.png?raw=true\n :width: 900px\n :align: center\n\nWith the analytical testing method, SpatialDM is scalable to 1 million spots \nwithin 12 min with only one core.\n\n.. image:: https://github.com/StatBiomed/SpatialDM/blob/main/docs/.figs/runtime_aug16-1.png?raw=true\n :width: 600px\n :align: center\n \nIt comprises two main steps: \\\n1) global selection `spatialdm_global` to identify significantly interacting LR pairs; \\\n2) local selection `spatialdm_local` to identify local spots for each interaction.\n\nInstallation\n============\n\nSpatialDM is available through `PyPI <https://pypi.org/project/SpatialDM/>`_. \nTo install, type the following command line and add ``-U`` for updates:\n\n.. code-block:: bash\n\n pip install -U SpatialDM\n\nAlternatively, you can install from this GitHub repository for latest (often \ndevelopment) version by the following command line:\n\n.. code-block:: bash\n\n pip install -U git+https://github.com/StatBiomed/SpatialDM\n\nInstallation time: < 1 min\n\n\n\nQuick example\n=============\n\nUsing the build-in melanoma dataset as an example, the following Python script\nwill compute the p-value indicating whether a certain Ligand-Receptor is \nspatially co-expressed. \n\n\n.. code-block:: python\n\n import spatialdm as sdm\n adata = sdm.datasets.dataset.melanoma()\n sdm.weight_matrix(adata, l=1.2, cutoff=0.2, single_cell=False) # weight_matrix by rbf kernel\n sdm.extract_lr(adata, 'human', min_cell=3) # find overlapping LRs from CellChatDB\n sdm.spatialdm_global(adata, 1000, specified_ind=None, method='both', nproc=1) # global Moran selection\n sdm.sig_pairs(adata, method='permutation', fdr=True, threshold=0.1) # select significant pairs\n sdm.spatialdm_local(adata, n_perm=1000, method='both', specified_ind=None, nproc=1) # local spot selection\n sdm.sig_spots(adata, method='permutation', fdr=False, threshold=0.1) # significant local spots\n\n # visualize global and local pairs\n import spatialdm.plottings as pl\n pl.global_plot(adata, pairs=['SPP1_CD44'])\n pl.plot_pairs(adata, ['SPP1_CD44'], marker='s')\n \n.. image:: https://github.com/StatBiomed/SpatialDM/blob/main/docs/.figs/global_plot.png?raw=true\n :width: 200px\n :align: center\n \n.. image:: https://github.com/StatBiomed/SpatialDM/blob/main/docs/.figs/SPP1_CD44.png?raw=true\n :width: 600px\n :align: center\n\n\n\nDetailed Manual\n===============\n\nThe full manual is at https://spatialdm.readthedocs.io, including: \n\n* `Permutation-based SpatialDM (Recommended for small datasets, <10k spots)`_.\n\n* `Differential analyses of whole interactome among varying conditions`_.\n\n.. _Permutation-based SpatialDM (Recommended for small datasets, <10k spots): tutorial/melanoma.ipynb\n\n.. _Differential analyses of whole interactome among varying conditions: tutorial/differential_test_intestine.ipynb\n\n\n\n\nReferences\n==========\n\n| Li, Z., Wang, T., Liu, P., & Huang, Y. (2023). SpatialDM for rapid \n identification of spatially co-expressed ligand\u2013receptor and revealing \n cell\u2013cell communication patterns. Nature communications, 14(1), 3995.\n https://www.nature.com/articles/s41467-023-39608-w\n\n",
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