Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/StatBiomed/SpatialDM

Spatial direct messaging detected by bivariate Moran
https://github.com/StatBiomed/SpatialDM

cell-cell-communication ligand-receptor-interaction spatial-correlation spatial-transcriptomics

Last synced: 4 months ago
JSON representation

Spatial direct messaging detected by bivariate Moran

Host: GitHub
URL: https://github.com/StatBiomed/SpatialDM
Owner: StatBiomed
License: apache-2.0
Created: 2022-06-10T13:19:37.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2023-12-12T16:09:14.000Z (6 months ago)
Last Synced: 2024-02-04T21:57:27.224Z (4 months ago)
Topics: cell-cell-communication, ligand-receptor-interaction, spatial-correlation, spatial-transcriptomics
Language: Jupyter Notebook
Homepage: https://spatialdm.readthedocs.io
Size: 53.1 MB
Stars: 17
Watchers: 2
Forks: 4
Open Issues: 9
Metadata Files:
- Readme: README.rst
- License: LICENSE

Lists

awesome-cell-cell-communication - SpatialDM - [python]- SpatialDM is a Python package for identifying spatial co-expressed ligand and receptor using Moran's bivariant extension. (Uncategorized / Uncategorized)

README

        ===============================================================

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 `_. 

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/