In [1]:
import scanpy as sc
import anndata
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import cell2location
import scvi
In [2]:
%matplotlib inline
sc.settings.verbosity = 3
sc.logging.print_version_and_date()
sc.settings.set_figure_params(dpi=100)
In [3]:
def again(adata):
bdata = scl.get_raw(adata)
bdata = bdata.copy()
sc.pp.highly_variable_genes(bdata, min_mean=0.0125, max_mean=4, min_disp=0.5)
sc.pl.highly_variable_genes(bdata)
scl.sc_process(bdata, 'spku')
return bdata
def leiden_auto(adata):
for x in np.arange(0.2,2.2,0.2):
a = format(x, ".1f")
sc.tl.leiden(adata, resolution=float(a), key_added='leiden_'+str(a))
def harmony_umap(adata, batch, max_harmony=10, max_kmeans=20):
sc.external.pp.harmony_integrate(adata, batch, max_iter_harmony=max_harmony, max_iter_kmeans=max_kmeans)
sc.pp.neighbors(adata, use_rep='X_pca_harmony')
scl.sc_process(adata, 'u')
def leiden_restrict(adata, annotation, sub, resolution, name):
# sub = list
sc.tl.leiden(adata, restrict_to=(annotation, sub), resolution=resolution,
key_added=name)
adata.obs[name] = [x.replace(',','_') for x in adata.obs[name]]
scl.us(adata, name)
GSE192736 data¶
In [4]:
results_folder = '/home/kytak/kwonyongtak/02_aging/write/stereopy/cell2location'
# create paths and names to results folders for reference regression and cell2location models
results_folder = '/home/kytak/kwonyongtak/02_aging/write/stereopy/cell2location'
# create paths and names to results folders for reference regression and cell2location models
ref_run_name = f'{results_folder}/reference_signatures'
run_name = f'{results_folder}/cell2location_map'
In [5]:
test_key = 'Old_liver'
In [12]:
old_liver = sc.read('/home/kytak/kwonyongtak/02_aging/write/stereopy/Old_liver.h5ad')
In [18]:
pd.DataFrame(old_liver.obsm['spatial'], index=old_liver.obs_names).to_csv('/home/kytak/kwonyongtak/02_aging/write/stereopy/old_liver_coord.csv')
In [11]:
old_liver.obs[old_liver.uns['mod']['factor_names']] = old_liver.obsm['q05_cell_abundance_w_sf']
In [12]:
old_liver
Out[12]:
In [223]:
old_liver.
Out[223]:
In [229]:
old_liver.obs[['Endothelial_Fibrotic']]
Out[229]:
In [838]:
sc.pl.spatial(old_liver, color=['score_Cd8_Trm_Pdcd1high', 'score_Cd8_Trm_Pdcd1low'],
spot_size=200)
In [21]:
sc.pl.spatial(old_liver, cmap='viridis',
# show first 8 cell types
color=['Endothelial_CV', 'Endothelial_Fibrotic', 'Endothelial_Lymphatic', 'Endothelial_Midzone', 'Endothelial_PV'],
ncols=4, size=1.3,
img_key='hires',
# limit color scale at 99.2% quantile of cell abundance
vmin=0, vmax=0.2, spot_size=100
)
In [34]:
import scipy
In [41]:
sns.kdeplot(old_liver.obs['Endothelial_PV'])
Out[41]:
In [348]:
old_liver.obs['Gross_Endothelial'] = old_liver.obs[[x for x in old_liver.obs.columns if 'Endo' in x]].sum(1)
In [349]:
old_liver.obs['Gross_Fibroblast'] = old_liver.obs[[x for x in old_liver.obs.columns if 'Fibrob' in x]].sum(1)
In [350]:
old_liver.obs['Gross_Cd4T'] = old_liver.obs[[x for x in old_liver.obs.columns if 'Cd4' in x]+['Treg']].sum(1)
In [351]:
old_liver.obs['Gross_Cd8T'] = old_liver.obs[[x for x in old_liver.obs.columns if 'Cd8' in x]].sum(1)
In [352]:
old_liver.obs['Gross_Monocyte'] = old_liver.obs[[x for x in old_liver.obs.columns if 'Monocyte' in x]].sum(1)
In [353]:
old_liver.obs['Gross_Macrophage'] = old_liver.obs[['KC','MP','Foam Cells','Transitioning']].sum(1)
In [354]:
old_liver.obs['Gross_VSMC'] = old_liver.obs[[x for x in old_liver.obs.columns if 'VSMC' in x]].sum(1)
In [355]:
old_liver.obs['Gross_NK'] = old_liver.obs[[x for x in old_liver.obs.columns if 'NK' in x]].sum(1)
In [356]:
old_liver.obs['Gross_DC'] = old_liver.obs[[x for x in old_liver.obs.columns if 'DC' in x]].sum(1)
In [357]:
old_liver.obs['Gross_B'] = old_liver.obs[[x for x in old_liver.obs.columns if 'B' in x]].sum(1)
In [358]:
sc.pl.spatial(old_liver, color=['Hepatocyte','Cholangiocyte'], spot_size=100)
In [359]:
sc.pl.spatial(old_liver, color=[x for x in old_liver.obs.columns if 'Gross' in x], spot_size=100)
