Basic usage

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

import matplotlib.pyplot as plt

Load spatial transcriptomics dataset. e.g., a Visium dataset.

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

Basic preprocessing

The method assumes non-negative values that reflect the abundancy of signaling molecules.

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

Specify ligand-receptor pairs

Here, we use an example with only three LR pairs.

A user-defined LR database can be specified in the same way or alternatively, built-in LR databases can be obtained with the function commot.pp.ligand_receptor_database().

For example df_ligrec=ct.pp.ligand_receptor_database(database='CellChat', species='mouse').

LR=np.array([['Tgfb1', 'Tgfbr1_Tgfbr2', 'Tgfb_pathway'],
    ['Tgfb2', 'Tgfbr1_Tgfbr2', 'Tgfb_pathway'],
    ['Tgfb3', 'Tgfbr1_Tgfbr2', 'Tgfb_pathway'],
    ['Fgf1', 'Fgfr1', 'FGF_pathway'],
    ['Fgf1', 'Fgfr2', 'FGF_pathway']],dtype=str)
df_ligrec = pd.DataFrame(data=LR)

Construct CCC networks

Use optimal transport to construct CCC networks for the ligand-receptor pairs with a spatial distance constraint of 500 \(\mu\)m (coupling between cells with distance greater than 500 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'].

ct.tl.spatial_communication(adata,
    database_name='user_database', df_ligrec=df_ligrec, dis_thr=500, heteromeric=True, pathway_sum=True)

Visualize signaling level

Plot the amount of sent and received signal, for example, of the LR pair Fgf1-Fgfr1.

pts = adata.obsm['spatial']
s = adata.obsm['commot-user_database-sum-sender']['s-Fgf1-Fgfr1']
r = adata.obsm['commot-user_database-sum-receiver']['r-Fgf1-Fgfr1']
fig, ax = plt.subplots(1,2, figsize=(10,4))
ax[0].scatter(pts[:,0], pts[:,1], c=s, s=5, cmap='Blues')
ax[0].set_title('Sender')
ax[1].scatter(pts[:,0], pts[:,1], c=r, s=5, cmap='Reds')
ax[1].set_title('Receiver')

Text(0.5, 1.0, 'Receiver')

Visualize signaling directions

Interpolate the signaling directions as vector fields.

ct.tl.communication_direction(adata, database_name='user_database', lr_pair=('Fgf1','Fgfr1'), k=5)
ct.pl.plot_cell_communication(adata, database_name='user_database', lr_pair=('Fgf1','Fgfr1'), plot_method='grid', background_legend=True,
    scale=0.00003, ndsize=8, grid_density=0.4, summary='sender', background='image', clustering='leiden', cmap='Alphabet',
    normalize_v = True, normalize_v_quantile=0.995)

<AxesSubplot: >

Or plot the directions with the level of received signal.

ct.pl.plot_cell_communication(adata, database_name='user_database', lr_pair=('Fgf1','Fgfr1'), plot_method='grid', background_legend=True,
    scale=0.00003, ndsize=8, grid_density=0.4, summary='receiver', background='summary', clustering='leiden', cmap='Reds',
    normalize_v = True, normalize_v_quantile=0.995)

<AxesSubplot: >

COMMOT data

The results of COMMOT analysis are attached to the anndata object: - adata.uns['commot-user_database-info']: the ligand-receptor database used - adata.obsm['commot-user_database-sum-sender']: the inferred amount of sent signaling through each ligand-receptor pair and the summation over each signaling pathway - adata.obsm['commot-user_database-sum-receiver']: the inferred amount of received signaling through each ligand-receptor pair and the summation over each signaling pathway - adata.obsm['commot_sender_vf-user_database-Fgf1-Fgfr1']: the interpolated signaling direction on each spot with respect to sent signal - adata.obsm['commot_receiver_vf-user_database-Fgf1-Fgfr1']: the interpolated signaling direction on each spot with respect to received signal - adata.obsp['commot-user_database-Fgf1-Fgfr1']: a sparse matrix where the \((i,j)\)th element is the CCC score through Fgf1-Fgfr1 from spot \(i\) to spot \(j\)

adata

AnnData object with n_obs × n_vars = 3355 × 32285
    obs: 'in_tissue', 'array_row', 'array_col'
    var: 'gene_ids', 'feature_types', 'genome'
    uns: 'spatial', 'log1p', 'commot-user_database-info'
    obsm: 'spatial', 'commot-user_database-sum-sender', 'commot-user_database-sum-receiver', 'commot_sender_vf-user_database-Fgf1-Fgfr1', 'commot_receiver_vf-user_database-Fgf1-Fgfr1'
    obsp: 'commot-user_database-Fgf1-Fgfr2', 'commot-user_database-Fgf1-Fgfr1', 'commot-user_database-Tgfb2-Tgfbr1_Tgfbr2', 'commot-user_database-Tgfb3-Tgfbr1_Tgfbr2', 'commot-user_database-Tgfb1-Tgfbr1_Tgfbr2', 'commot-user_database-FGF_pathway', 'commot-user_database-Tgfb_pathway', 'commot-user_database-total-total'