Screening cell-cell communication in spatial transcriptomics via collective optimal transport

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Install from PyPI by pip install commot or install from source by cd to this directory and pip install .

[Optional] Install tradeSeq in R to analyze the CCC differentially expressed genes.
Currently, tradeSeq version 1.0.1 with R version 3.6.3 has been tested to work.
In order for the R-python interface to work properly, rpy2==3.4.2 and anndata2ri==1.0.6 should be installed. To use this feature, install from PyPI by pip install commot[tradeSeq] or from source by pip install .[tradeSeq].


Basic usage

Import packages

import commot as ct
import scanpy as sc
import pandas as pd
import numpy as np

Load a spatial dataset
(e.g., a Visium dataset)

adata = sc.datasets.visium_sge(sample_id='V1_Mouse_Brain_Sagittal_Posterior')

Basic processing

sc.pp.normalize_total(adata, inplace=True)

Specify ligand-receptor pairs

LR=np.array([['Tgfb1', 'Tgfbr1_Tgfbr2', 'Tgfb_pathway'],['Tgfb2', 'Tgfbr1_Tgfbr2', 'Tgfb_pathway'],['Tgfb3', 'Tgfbr1_Tgfbr2', 'Tgfb_pathway']],dtype=str)
df_ligrec = pd.DataFrame(data=LR)

(or use pairs from a ligand-receptor database df_ligrec=ct.pp.ligand_receptor_database(database='CellChat', species='mouse').)

Construct CCC networks
Use collective optimal transport to construct CCC networks for the ligand-receptor pairs with a spatial distance constraint of 200 (coupling between cells with distance greater than 200 is prohibited). For example, the spot-by-spot matrix for the pair Tgfb1 (ligand) and Tgfbr1_Tgfbr2 (receptor)is stored in adata.obsp['commot-user_database-Tgfb1-Tgfbr1_Tgfbr2']. The total sent or received signal for each pair is stored in adata.obsm['commot-user_database-sum-sender'] and adata.obsm['commot-user_database-sum-receiver'].,
    database_name='user_database', df_ligrec=df_ligrec, dis_thr=200, heteromeric=True)


See detailed documentation and examples at


Cang, Zixuan, Yanxiang Zhao, Axel A. Almet, Adam Stabell, Raul Ramos, Maksim Plikus, Scott X. Atwood, and Qing Nie. “Screening cell-cell communication in spatial transcriptomics via collective optimal transport.” bioRxiv (2022): 505185