commot 0.0.2

Cell-cell communications in space of transcriptomics data via collective optimal transport.

COMMOT

Cell-cell communication network construction for spatial transcriptomics using collective optimal transport

Installation

1. Install the dependencies.
   pip install -r requirements.txt
2. Install COMMOT by cd to this directory and
   pip install .
3. 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.

Usage

Basic usage

Import packages

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

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

adata = sc.datasets.visium_sge(sample_id='V1_Breast_Cancer_Block_A_Section_1')

Basic processing

sc.pp.normalize_total(adata, inplace=True)
sc.pp.log1p(adata)

Specify ligand-receptor pairs

LR=np.array([['AMH', 'ACVR1'],['AMH', 'AMHR2'],['BMP10', 'ACVR1']],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='human').)

Construct CCC networks
Use collective optimal transport to construct CCC networks for the ligand-receptor pairs with a spatial distance constraint of 1000 (coupling between cells with distance greater than 1000 is prohibited). For example, the spot-by-spot matrix for the pair AMH (ligand) and ACVR1 (receptor)is stored in adata.obsp['commot-user_database-AMH-ACVR1']. 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'].

ct.tl.spatial_communication(adata,
    pathway_name='user_database', df_ligrec=df_ligrec, dis_thr=1000)

Documentation

See the documentation in docs/_build/html/index.html for all the APIs to perform visualization and analyses such as visualizing spatial signaling direction and identifying CCC differentially expressed genes.