Tutorial

1. Import CellRefiner and packages

[2]:

import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
import scanpy as sc
import cellrefiner as cr

2. Load Data

The paired datasets here are from Visium spatial transcriptomic data and scRNA-seq samples of the mouse cortex available via the squidpy package.

[4]:

import squidpy as sq
adata_st = sq.datasets.visium_fluo_adata_crop()
adata_st = adata_st[adata_st.obs.cluster.isin([f"Cortex_{i}" for i in np.arange(1, 5)])].copy()
adata_sc = sq.datasets.sc_mouse_cortex()
print(adata_st)
print(adata_sc)

/home/kxy/miniconda3/envs/cr_39_1/lib/python3.9/site-packages/xarray_schema/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  from pkg_resources import DistributionNotFound, get_distribution
/home/kxy/miniconda3/envs/cr_39_1/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
  warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)

AnnData object with n_obs × n_vars = 324 × 16562
    obs: 'in_tissue', 'array_row', 'array_col', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'total_counts_MT', 'log1p_total_counts_MT', 'pct_counts_MT', 'n_counts', 'leiden', 'cluster'
    var: 'gene_ids', 'feature_types', 'genome', 'MT', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
    uns: 'cluster_colors', 'hvg', 'leiden', 'leiden_colors', 'neighbors', 'pca', 'spatial', 'umap'
    obsm: 'X_pca', 'X_umap', 'spatial'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'
AnnData object with n_obs × n_vars = 21697 × 36826
    obs: 'sample_name', 'organism', 'donor_sex', 'cell_class', 'cell_subclass', 'cell_cluster', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'n_counts'
    var: 'mt', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
    uns: 'cell_class_colors', 'cell_subclass_colors', 'hvg', 'neighbors', 'pca', 'umap'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'

4. Cell shape modeling

[7]:

sem = cr.tl.cell_shape_modeling(adata_cr,cluster_key = 'cell_subclass')

Simulation:   0%|          | 0/2000 [00:00<?, ?it/s]/home/kxy/miniconda3/envs/cr_39_1/lib/python3.9/site-packages/numba/cuda/dispatcher.py:536: NumbaPerformanceWarning: Grid size 85 will likely result in GPU under-utilization due to low occupancy.
  warn(NumbaPerformanceWarning(msg))
Simulation: 100%|██████████| 2000/2000 [00:26<00:00, 75.41it/s]

add .obsp['contacts'], .uns['contacts']
Computing alpha-shape with parameters: alpha=None, ns=10, r=1.2

Processing Cell Shapes: 100%|██████████| 1620/1620 [00:14<00:00, 109.03it/s]

[8]:

fig,ax=plt.subplots(figsize=(12,12))
cr.pl.plot_cell_shape(sem,ax=ax,boundary_color='gray',boundary_width=0.5)

[8]:

<Axes: >

../_images/notebooks_tutorial_mouse_visium_cortex_11_1.png

5. Contact-based communication analysis

[9]:

db_lr = cr.pp.ligand_receptor_database(signaling_types='Cell-Cell Contact')
db_lr = cr.pp.filter_lr_database(db_lr,adata_cr, min_cell_pct=0.01)
cr.tl.contact_communication(db_lr, adata = adata_cr)

add .uns['contact_signal_info']
add .obsm['sender_signal'], .obsm['receiver_signal']

[10]:

fig,ax=plt.subplots(figsize=(12,12))
cr.pl.plot_cell_shape(sem,ax=ax,vis_key='NOTCH',boundary_color='gray',boundary_width=0.1)
cr.pl.plot_contact_signal(sem,ax=ax,signal = 'NOTCH')

[10]:

<Axes: >

../_images/notebooks_tutorial_mouse_visium_cortex_14_1.png

Cluster-level communication

[11]:

cr.tl.cluster_communication(adata_cr,cluster_key = 'cell_subclass',signal = 'NOTCH')
fig,ax=plt.subplots(figsize=(6,4))
sns.heatmap(adata_cr.uns[f'cell_subclass-NOTCH']['communication_pvalue']<0.05,square=True,linewidths=1,ax=ax,cmap='Reds',cbar=False)

[11]:

<Axes: >

../_images/notebooks_tutorial_mouse_visium_cortex_16_1.png

Visualize all contact-based communications

[12]:

fig,ax=plt.subplots(figsize=(12,12))
cr.pl.plot_cell_shape(sem,ax=ax,boundary_color='gray',boundary_width=0.5,save_name = 'cell_shape.png')
plt.close(fig)

# plot all pathway
summary = 'sender'
for pth in adata_cr.uns['contact_signal_info']['pathway']:
    fig,ax=plt.subplots(figsize=(12,12))
    if summary == 'cell':
        cr.pl.plot_cell_shape(sem,boundary_color='gray',boundary_width=0.4,ax=ax,enable_colorbar=False,enable_legend=True)
    else:
        cr.pl.plot_cell_shape(sem,vis_key=pth,summary=summary,boundary_color='gray',cmap_name='Reds',boundary_width=0.4,ax=ax,enable_colorbar=True)
    cr.pl.plot_contact_signal(sem,signal=pth,line_color = 'k', line_width=0.8, ax=ax, line_alpha=0.8)
    ax.set_axis_off()
    fig.savefig(f"{pth}_{summary}.png", dpi=500, bbox_inches='tight')
    plt.close(fig)

[13]:

for sig_key in adata_cr.uns['contact_signal_info']['pathway']:
    cr.tl.cluster_communication(adata_cr,'cell_subclass',sig_key)

[14]:

# cluster-level
for pth in adata_cr.uns['contact_signal_info']['pathway']:
    fig,ax=plt.subplots(figsize=(6,4))
    sns.heatmap(adata_cr.uns[f'cell_subclass-{pth}']['communication_pvalue']<0.05,square=True,linewidths=1,ax=ax,cmap='Reds',cbar=False)
    fig.savefig(f"{pth}_significance.png", dpi=500, bbox_inches='tight')
    plt.close(fig)