Plot the Results of the Mouse Spleen Dataset

Packages Loading

import warnings
warnings.filterwarnings('ignore')

import matplotlib.pyplot as plt
import scanpy as sc
from sklearn.metrics.cluster import adjusted_rand_score

import sys
sys.path.append(r'../../../../')
from Model.utils import reorder_categories

Results Loading

slice_id = 1    # 1, 2

adata_results = sc.read_h5ad(f'../../../Mouse_Spleen_Replicate{slice_id}.h5ad')

adata_results

AnnData object with n_obs × n_vars = 2568 × 0
    obs: 'SpatialCOC', 'SpatialGlue', 'STAGATE', 'SpaGCN', 'Modality1', 'Modality2', 'MultiMAP', 'MultiVI', 'Seurat', 'COSMOS'
    uns: 'COSMOS_colors', 'MultiMAP_colors', 'Seurat_colors'
    obsm: 'COSMOS', 'Modality1', 'Modality2', 'MultiMAP', 'MultiVI', 'STAGATE', 'Seurat', 'SpatialCOC', 'SpatialGlue', 'spatial'

Plot the Spatial Domain Identifications

## define the plot parameters
colors_domain = {
    'SpatialCOC':   [   '#fdf0d5', '#f9c74f', '#83c5be', '#99582a', '#3f5e66', '#ee6055'    ],

    'COSMOS':       [   '#ee6055', '#f9c74f', '#83c5be', '#3f5e66', '#99582a', '#fdf0d5'    ],

    'SpatialGlue':  [   '#fdf0d5', '#f9c74f', '#99582a', '#83c5be', '#ee6055', '#3f5e66'    ],

    'Seurat':       [   '#83c5be', '#99582a', '#fdf0d5', '#f9c74f', '#ee6055', '#3f5e66'    ],

    'MultiMAP':     [   '#ee6055', '#99582a', '#83c5be', '#fdf0d5', '#f9c74f', '#3f5e66'    ],

    'MultiVI':      [   '#ee6055', '#99582a', '#83c5be', '#fdf0d5', '#f9c74f', '#3f5e66'    ],

    'SpaGCN':       [   '#ee6055', '#99582a', '#f9c74f', '#fdf0d5', '#83c5be', '#3f5e66'    ],

    'STAGATE':      [   '#fdf0d5', '#99582a', '#f9c74f', '#ee6055', '#83c5be', '#3f5e66'    ],

    'Modality1':    [   '#ee6055', '#99582a', '#fdf0d5', '#f9c74f', '#3f5e66', '#83c5be'    ],

    'Modality2':    [   '#99582a', '#83c5be', '#fdf0d5', '#ee6055', '#f9c74f', '#3f5e66'    ],
}
font_size = 24

save_path = f'../../Mouse_Spleen/replicate{slice_id}/'

result_key = ['SpatialCOC', 'COSMOS', 'SpatialGlue', 'Seurat', 'MultiMAP', 'MultiVI', 'SpaGCN', 'STAGATE', 'Modality1', 'Modality2']

plt.rcParams['font.sans-serif'] = ['Arial']
plt.rcParams['font.size'] = font_size

for result in result_key:

    fig, ax = plt.subplots(1, 1, figsize=(6, 6))
    sc.pl.embedding(adata_results, basis='spatial', color=[result], ax=ax, s=140, show=False, palette=colors_domain[result])
    ax.set_title(f"")
    ax.set_xlabel('')
    ax.set_ylabel('')

    ax.get_legend().remove()

    # Hide axis borders
    for spine in ax.spines.values():
        spine.set_visible(False)

    # Adjust subplot parameters
    plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
    plt.tight_layout()

    plt.savefig(f'{result}.png', dpi=500)
    plt.savefig(f'{result}.eps')

    plt.show()