===============================================================
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/
Raw data
{
"_id": null,
"home_page": "https://github.com/leeyoyohku/SpatialDM",
"name": "SpatialDM",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "Spatial transcriptomics, Spatial association, Ligand-recptor interaction",
"author": "['Zhouxuan Li', 'Tianjie Wang', 'Yuanhua Huang']",
"author_email": "leeyoyo@connect.hku.hk",
"download_url": "https://files.pythonhosted.org/packages/ae/82/c14993592d15a444ca78a92cefeec42efba0f541cc837cb761266b1e43d7/SpatialDM-0.2.0.tar.gz",
"platform": null,
"description": "===============================================================\nSpatialDM: Spatial Direct Messaging Detected by bivariate Moran\n===============================================================\n\nAbout\n=====\n\nSpatialDM (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. \\\n\nUniquely, SpatialDM can distinguish co-expressed ligand and receptor pairs from spatially 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 within 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\nSpatialDM manuscript with more details is available on bioRxiv_ now and is currently under review.\n\n.. _bioRxiv: https://www.biorxiv.org/content/10.1101/2022.08.19.504616v1/\n\n\n\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "SpatialDM: Spatial co-expression Detected by bivariate Moran",
"version": "0.2.0",
"project_urls": {
"Homepage": "https://github.com/leeyoyohku/SpatialDM"
},
"split_keywords": [
"spatial transcriptomics",
" spatial association",
" ligand-recptor interaction"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "f9418708c0e50e0ec50b4a9e532eb5626a430cc11e4d7c5638f1e12cc0c31d0f",
"md5": "0cd2dfe5660e56507533fa3eb7fc763e",
"sha256": "9747a25a382c7a402cb0504a7eff24314f508a037b5935c2c4fab76e0f43fcb8"
},
"downloads": -1,
"filename": "SpatialDM-0.2.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "0cd2dfe5660e56507533fa3eb7fc763e",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 29066,
"upload_time": "2024-04-28T09:50:45",
"upload_time_iso_8601": "2024-04-28T09:50:45.769140Z",
"url": "https://files.pythonhosted.org/packages/f9/41/8708c0e50e0ec50b4a9e532eb5626a430cc11e4d7c5638f1e12cc0c31d0f/SpatialDM-0.2.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "ae82c14993592d15a444ca78a92cefeec42efba0f541cc837cb761266b1e43d7",
"md5": "044faf4e4b79767adecd9a881f5d17e9",
"sha256": "961007369246f94d5f08d8539377bfd4338f0866f5ff145e256a85dd7c5b611a"
},
"downloads": -1,
"filename": "SpatialDM-0.2.0.tar.gz",
"has_sig": false,
"md5_digest": "044faf4e4b79767adecd9a881f5d17e9",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 23199,
"upload_time": "2024-04-28T09:50:47",
"upload_time_iso_8601": "2024-04-28T09:50:47.971114Z",
"url": "https://files.pythonhosted.org/packages/ae/82/c14993592d15a444ca78a92cefeec42efba0f541cc837cb761266b1e43d7/SpatialDM-0.2.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-28 09:50:47",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "leeyoyohku",
"github_project": "SpatialDM",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "spatialdm"
}
JSON
