polt

# this notebook is a summary of extended data modalities apart from the original manuscript
# for each data, the h5ad is loaded from those dumped data generated by separate notebooks.

# data include:
#     ExSeq data
#     osmFISH data
#     slide-seq data
#     ST data
#     3D STARmap data
#     Sterep-seq data
#     Visium data
#     STARmapPlus data

imports

import warnings
warnings.filterwarnings("ignore")
import MENDER
import scanpy as sc
import pandas as pd
import numpy as np
from sklearn.metrics import *
import time
import palettable
import matplotlib.pyplot as plt

color palette

cmp_cls = []
cmp_cls1 = palettable.wesanderson.FantasticFox2_5.mpl_colors[:4]
cmp_cls2 = palettable.wesanderson.Moonrise1_5.mpl_colors[:3]
cmp_cls3 = palettable.wesanderson.Darjeeling3_5.mpl_colors[3:]
cmp_cls4 = palettable.tableau.PurpleGray_12.mpl_colors[:3]
cmp_cls.extend(cmp_cls1)
cmp_cls.extend(cmp_cls2)
cmp_cls.extend(cmp_cls3)
cmp_cls.extend(cmp_cls4)

cmp_meld = palettable.cartocolors.diverging.Tropic_7.mpl_colormap

ExSeq data

data_name = 'ExSeq'
data_path = f'dump/{data_name}.h5ad'
adata = sc.read_h5ad(data_path)

ax = sc.pl.embedding(adata,basis='spatial',color='MENDER',show=False,palette=cmp_cls)
ax.axis('equal')
plt.savefig(f'Figures/{data_name}.png',dpi=200,transparent=True,bbox_inches='tight')

osmFISH data

data_name = 'osmFISH'
data_path = f'dump/{data_name}.h5ad'
adata = sc.read_h5ad(data_path)

ax = sc.pl.embedding(adata,basis='spatial',color='MENDER',show=False,palette=cmp_cls)
ax.axis('equal')
plt.savefig(f'Figures/{data_name}.png',dpi=200,transparent=True,bbox_inches='tight')

slide-seq data

data_name = 'Slide-seq_Cerebellum'
data_path = f'dump/{data_name}.h5ad'
adata = sc.read_h5ad(data_path)

ax = sc.pl.embedding(adata,basis='spatial',color='MENDER',show=False,palette=cmp_cls)
ax.axis('equal')
plt.savefig(f'Figures/{data_name}.png',dpi=200,transparent=True,bbox_inches='tight')

data_name = 'Slide-seq_MOB'
data_path = f'dump/{data_name}.h5ad'
adata = sc.read_h5ad(data_path)

ax = sc.pl.embedding(adata,basis='spatial',color='MENDER',show=False,palette=cmp_cls)
ax.axis('equal')
plt.savefig(f'Figures/{data_name}.png',dpi=200,transparent=True,bbox_inches='tight')

data_name = 'Slide-seq_Hippocampus'
data_path = f'dump/{data_name}.h5ad'
adata = sc.read_h5ad(data_path)

ax = sc.pl.embedding(adata,basis='spatial',color='MENDER',show=False,palette=cmp_cls)
ax.axis('equal')
plt.savefig(f'Figures/{data_name}.png',dpi=200,transparent=True,bbox_inches='tight')

ST data

data_name = 'ST_MOB'
data_path = f'dump/{data_name}.h5ad'
adata = sc.read_h5ad(data_path)

ax = sc.pl.embedding(adata,basis='spatial',color='MENDER',show=False,palette=cmp_cls)
ax.axis('equal')
plt.savefig(f'Figures/{data_name}.png',dpi=200,transparent=True,bbox_inches='tight')

3D STARmap data

data_name = 'STARmap3D'
data_path = f'dump/{data_name}.h5ad'
adata = sc.read_h5ad(data_path)

ax = sc.pl.embedding(adata,basis='spatial',color='MENDER',show=False,palette=cmp_cls)
ax.axis('equal')
plt.savefig(f'Figures/{data_name}_01.png',dpi=200,transparent=True,bbox_inches='tight')

Stereo-seq data

data_name = 'StereoSeq_MOB'
data_path = f'dump/{data_name}.h5ad'
adata = sc.read_h5ad(data_path)

ax = sc.pl.embedding(adata,basis='spatial',color='MENDER',show=False,palette=cmp_cls)
ax.axis('equal')
plt.savefig(f'Figures/{data_name}.png',dpi=200,transparent=True,bbox_inches='tight')

Visium data

data_name = 'Visium_MOB'
data_path = f'dump/{data_name}.h5ad'
a_MENDER = sc.read_h5ad(data_path)

## load the data using pysodb, please install pysodb in advance [https://pysodb.readthedocs.io/en/latest/]
import pysodb
sodb = pysodb.SODB()
adata = sodb.load_experiment('Lebrigand2022The','GSM4656181_10x_Visium')

load experiment[GSM4656181_10x_Visium] in dataset[Lebrigand2022The]

adata.obs['MENDER'] = np.array(a_MENDER.obs['MENDER'])

ax = sc.pl.spatial(adata,color='MENDER',palette=cmp_cls,show=False)
plt.savefig(f'Figures/{data_name}.png',dpi=200,transparent=True,bbox_inches='tight')

data_name = 'Visium_brain'
data_path = f'dump/{data_name}.h5ad'
a_MENDER = sc.read_h5ad(data_path)

## load the data using pysodb, please install pysodb in advance [https://pysodb.readthedocs.io/en/latest/]
import pysodb
sodb = pysodb.SODB()
adata_raw0 = sodb.load_experiment('10x','V1_Human_Brain_Section_1_filtered_feature_bc_matrix')
adata_raw1 = sodb.load_experiment('10x','V1_Human_Brain_Section_2_filtered_feature_bc_matrix')

load experiment[V1_Human_Brain_Section_1_filtered_feature_bc_matrix] in dataset[10x]
load experiment[V1_Human_Brain_Section_2_filtered_feature_bc_matrix] in dataset[10x]

adata_raw0.obs['MENDER'] = np.array(a_MENDER[a_MENDER.obs['batch']=='0'].obs['MENDER'])
adata_raw1.obs['MENDER'] = np.array(a_MENDER[a_MENDER.obs['batch']=='1'].obs['MENDER'])

ax = sc.pl.spatial(adata_raw0,color='MENDER',palette=cmp_cls,show=False)
plt.savefig(f'Figures/{data_name}_1.png',dpi=200,transparent=True,bbox_inches='tight')

ax = sc.pl.spatial(adata_raw1,color='MENDER',palette=cmp_cls,show=False)
plt.savefig(f'Figures/{data_name}_2.png',dpi=200,transparent=True,bbox_inches='tight')

data_name = 'Visium_kidney'
data_path = f'dump/{data_name}.h5ad'
a_MENDER = sc.read_h5ad(data_path)

## load the data using pysodb, please install pysodb in advance [https://pysodb.readthedocs.io/en/latest/]
import pysodb
sodb = pysodb.SODB()
adata = sodb.load_experiment('10x','V1_Mouse_Kidney_filtered_feature_bc_matrix')

load experiment[V1_Mouse_Kidney_filtered_feature_bc_matrix] in dataset[10x]

adata.obs['MENDER'] = np.array(a_MENDER.obs['MENDER'])

ax = sc.pl.spatial(adata,color='MENDER',palette=cmp_cls,show=False)
plt.savefig(f'Figures/{data_name}.png',dpi=200,transparent=True,bbox_inches='tight')

STARmapPlus data

data_name = 'STARmapPlus_AD'
data_path = f'dump/{data_name}.h5ad'
adata = sc.read_h5ad(data_path)

for si in adata.obs['slice_id'].cat.categories:
    cur_a = adata[adata.obs['slice_id']==si]
    ax = sc.pl.embedding(cur_a,basis='spatial',color='MENDER',show=False,palette=cmp_cls)
    ax.axis('equal')
    plt.savefig(f'Figures/{data_name}_{si}.png',dpi=200,transparent=True,bbox_inches='tight')