ovrawm/t_slat.txt

#!/usr/bin/env python
# coding: utf-8

# # Single cell spatial alignment tools
# 
# SLAT (Spatially-Linked Alignment Tool), a graph-based algorithm for efficient and effective alignment of spatial slices. Adopting a graph adversarial matching strategy, SLAT is the first algorithm capable of aligning heterogenous spatial data across distinct technologies and modalities. 
# 
# We made two improvements in integrating the STT algorithm in OmicVerse:
# 
# - **Fix the running error in alignment**: We fixed some issues with the scSLAT package on pypi.
# - **Added more downstream analysis**: We have expanded on the original tutorial by combining the tutorial and reproduce code given by the authors for downstream analysis.
# 
# If you found this tutorial helpful, please cite SLAT and OmicVerse: 
# 
# - Xia, CR., Cao, ZJ., Tu, XM. et al. Spatial-linked alignment tool (SLAT) for aligning heterogenous slices. Nat Commun 14, 7236 (2023). https://doi.org/10.1038/s41467-023-43105-5

# In[1]:


import omicverse as ov
import os

import scanpy as sc
import numpy as np
import pandas as pd
import torch
ov.plot_set()


# In[2]:


#import scSLAT
from omicverse.externel.scSLAT.model import load_anndatas, Cal_Spatial_Net, run_SLAT, scanpy_workflow, spatial_match
from omicverse.externel.scSLAT.viz import match_3D_multi, hist, Sankey, match_3D_celltype, Sankey,Sankey_multi,build_3D
from omicverse.externel.scSLAT.metrics import region_statistics


# ## Preprocess Data
# 
# adata1.h5ad: E11.5 mouse embryo dataset, download from [here](https://drive.google.com/uc?export=download&id=1KkuJt6aSlKS1AJzFZjE_odypY-GINRuD)
# 
# adata2.h5ad: E12.5 mouse embryo dataset, download from [here](https://drive.google.com/uc?export=download&id=1YIiEmjGfHxcDbGn4nv2kzmTHUo3_q5hJ)

# In[3]:


adata1 = sc.read_h5ad('data/E115_Stereo.h5ad')
adata2 = sc.read_h5ad('data/E125_Stereo.h5ad')


# In[4]:


adata1.obs['week']='E11.5'
adata2.obs['week']='E12.5'


# In[5]:


sc.pl.spatial(adata1, color='annotation', spot_size=3)
sc.pl.spatial(adata2, color='annotation', spot_size=3)


# ## Run SLAT
# 
# Then we run SLAT as usual

# In[6]:


Cal_Spatial_Net(adata1, k_cutoff=20, model='KNN')
Cal_Spatial_Net(adata2, k_cutoff=20, model='KNN')
edges, features = load_anndatas([adata1, adata2], feature='DPCA', check_order=False)


# In[7]:


embd0, embd1, time = run_SLAT(features, edges, LGCN_layer=5)


# In[8]:


best, index, distance = spatial_match([embd0, embd1], reorder=False, adatas=[adata1,adata2])


# In[9]:


matching = np.array([range(index.shape[0]), best])
best_match = distance[:,0]
region_statistics(best_match, start=0.5, number_of_interval=10)


# ## Visualization of alignment

# In[10]:


import matplotlib.pyplot as plt
matching_list=[matching]
model = build_3D([adata1,adata2], matching_list,subsample_size=300, )
ax=model.draw_3D(hide_axis=True, line_color='#c2c2c2', height=1, size=[6,6], line_width=1)


# Then we check the alignment quality of the whole slide

# In[11]:


adata2.obs['low_quality_index']= best_match
adata2.obs['low_quality_index'] = adata2.obs['low_quality_index'].astype(float)


# In[12]:


adata2.obsm['spatial']


# In[13]:


sc.pl.spatial(adata2, color='low_quality_index', spot_size=3, title='Quality')


# We use a Sankey diagram to show the correspondence between cell types at different stages of development

# In[33]:


fig=Sankey_multi(adata_li=[adata1,adata2],
             prefix_li=['E11.5','E12.5'],
             matching_li=[matching],
                clusters='annotation',filter_num=10,
             node_opacity = 0.8,
             link_opacity = 0.2,
                layout=[800,500],
           font_size=12,
           font_color='Black',
           save_name=None,
           format='png',
           width=1200,
           height=1000,
           return_fig=True)
fig.show()


# In[34]:


fig.write_html("slat_sankey.html")


# ## Focus on developing Kidney
# 
# We highlighted the “Kidney” cells in E12.5 and their aligned precursor cells in E11.5 in alignment results. Consistent with our biological priors, the precursors of the kidney are the mesonephros and the metanephros
# 
# Then we focus on another organ: ‘Ovary’, and found ovary only has single spatial origin. It is interesting that precursors of ovary are spatially close to the mesonephros (see Kidney part), because mammalian ovary originates from the regressed mesonephros.

# In[27]:


color_dict1=dict(zip(adata1.obs['annotation'].cat.categories,
                    adata1.uns['annotation_colors'].tolist()))
adata1_df = pd.DataFrame({'index':range(embd0.shape[0]),
                          'x': adata1.obsm['spatial'][:,0],
                          'y': adata1.obsm['spatial'][:,1],
                          'celltype':adata1.obs['annotation'],
                         'color':adata1.obs['annotation'].map(color_dict1)
                         }
                        )
color_dict2=dict(zip(adata2.obs['annotation'].cat.categories,
                    adata2.uns['annotation_colors'].tolist()))
adata2_df = pd.DataFrame({'index':range(embd1.shape[0]),
                          'x': adata2.obsm['spatial'][:,0],
                          'y': adata2.obsm['spatial'][:,1],
                          'celltype':adata2.obs['annotation'],
                         'color':adata2.obs['annotation'].map(color_dict2)
                         }
                        )


# In[28]:


kidney_align = match_3D_celltype(adata1_df, adata2_df, matching, meta='celltype', 
                                 highlight_celltype = [['Urogenital ridge'],['Kidney','Ovary']],
                                 subsample_size=10000, highlight_line = ['blue'], scale_coordinate = True )
kidney_align.draw_3D(size= [6, 6], line_width =0.8, point_size=[0.6,0.6], hide_axis=True)


# We can get the lineage of the query's cells and mappings using the following function

# In[15]:


def cal_matching_cell(target_adata,query_adata,matching,query_cell,clusters='annotation',):
    adata1_df = pd.DataFrame({'index':range(target_adata.shape[0]),
                          'x': target_adata.obsm['spatial'][:,0],
                          'y': target_adata.obsm['spatial'][:,1],
                          'celltype':target_adata.obs[clusters]})
    adata2_df = pd.DataFrame({'index':range(query_adata.shape[0]),
                              'x': query_adata.obsm['spatial'][:,0],
                              'y': query_adata.obsm['spatial'][:,1],
                              'celltype':query_adata.obs[clusters]})
    query_adata = target_adata[matching[1,adata2_df.loc[adata2_df.celltype==query_cell,'index'].values],:]
    #adata2_df['target_celltype'] = adata1_df.iloc[matching[1,:],:]['celltype'].to_list()
    #adata2_df['target_obs_names'] = adata1_df.iloc[matching[1,:],:].index.to_list()
    
    #query_obs=adata2_df.loc[adata2_df['celltype']==query_cell,'target_obs_names'].tolist()
    return query_adata
    


# We find that maps mapped on 3D also show up well on 2D

# In[21]:


query_adata=cal_matching_cell(target_adata=adata1,
                              query_adata=adata2,
                              matching=matching,
                              query_cell='Kidney',clusters='annotation')
query_adata


# In[17]:


adata1.obs['kidney_anno']=''
adata1.obs.loc[query_adata.obs.index,'kidney_anno']=query_adata.obs['annotation']


# In[18]:


sc.pl.spatial(adata1, color='kidney_anno', spot_size=3,
             palette=['#F5F5F5','#ff7f0e', 'green',])


# We are concerned with Kidney lineage development, so we integrated the cells corresponding to the Kidney lineage on the two sections of E11 and E12, and then we could use the method of difference analysis to study the dynamic process of Kidney lineage development.

# In[22]:


kidney_lineage_ad=sc.concat([query_adata,adata2[adata2.obs['annotation']=='Kidney']],merge='same')
kidney_lineage_ad=ov.pp.preprocess(kidney_lineage_ad,mode='shiftlog|pearson',n_HVGs=3000,target_sum=1e4)
kidney_lineage_ad.raw = kidney_lineage_ad
kidney_lineage_ad = kidney_lineage_ad[:, kidney_lineage_ad.var.highly_variable_features]
ov.pp.scale(kidney_lineage_ad)
ov.pp.pca(kidney_lineage_ad)
ov.pp.neighbors(kidney_lineage_ad,use_rep='scaled|original|X_pca',metric="cosine")
ov.utils.cluster(kidney_lineage_ad,method='leiden',resolution=1)
ov.pp.umap(kidney_lineage_ad)


# In[23]:


ov.pl.embedding(kidney_lineage_ad,basis='X_umap',
               color=['annotation','week','leiden'],
               frameon='small')


# In[25]:


# Nphs1 https://www.nature.com/articles/s41467-021-22266-1
sc.pl.dotplot(kidney_lineage_ad,{'nephron progenitors':['Wnt9b','Osr1','Nphs1','Lhx1','Pax2','Pax8'],
                         'metanephric':['Eya1','Shisa3','Foxc1'], 
                         'kidney':['Wt1','Wnt4','Nr2f2','Dach1','Cd44']} ,
              'leiden',dendrogram=False,colorbar_title='Expression')


# In[26]:


kidney_lineage_ad.obs['re_anno'] = 'Unknown'
kidney_lineage_ad.obs.loc[kidney_lineage_ad.obs.leiden.isin(['4']),'re_anno'] = 'Nephron progenitors (E11.5)'
kidney_lineage_ad.obs.loc[kidney_lineage_ad.obs.leiden.isin(['2','3','1','5']),'re_anno'] = 'Metanephron progenitors (E11.5)'
kidney_lineage_ad.obs.loc[kidney_lineage_ad.obs.leiden=='0','re_anno'] = 'Kidney (E12.5)'


# In[28]:


# kidney_all = kidney_all[kidney_all.obs.leiden!='3',:]
kidney_lineage_ad.obs.leiden = list(kidney_lineage_ad.obs.leiden)
ov.pl.embedding(kidney_lineage_ad,basis='X_umap',
               color=['annotation','re_anno'],
               frameon='small')


# In[29]:


adata1.obs['kidney_anno']=''
adata1.obs.loc[kidney_lineage_ad[kidney_lineage_ad.obs['week']=='E11.5'].obs.index,'kidney_anno']=kidney_lineage_ad[kidney_lineage_ad.obs['week']=='E11.5'].obs['re_anno']


# In[41]:


import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
sc.pl.spatial(adata1, color='kidney_anno', spot_size=1.5,
             palette=['#F5F5F5','#ff7f0e', 'green',],show=False,ax=ax)


# We can also differentially analyse Kidney's developmental pedigree to find different marker genes, and we can analyse transcription factors and thus find the regulatory units involved.

# In[42]:


test_adata=kidney_lineage_ad
dds=ov.bulk.pyDEG(test_adata.to_df(layer='lognorm').T)
dds.drop_duplicates_index()
print('... drop_duplicates_index success')
treatment_groups=test_adata.obs[test_adata.obs['week']=='E12.5'].index.tolist()
control_groups=test_adata.obs[test_adata.obs['week']=='E11.5'].index.tolist()
result=dds.deg_analysis(treatment_groups,control_groups,method='ttest')
# -1 means automatically calculates
dds.foldchange_set(fc_threshold=-1,
                   pval_threshold=0.05,
                   logp_max=10)


# In[43]:


dds.plot_volcano(title='DEG Analysis',figsize=(4,4),
                 plot_genes_num=8,plot_genes_fontsize=12,)


# In[52]:


up_gene=dds.result.loc[dds.result['sig']=='up'].sort_values('qvalue')[:3].index.tolist()
down_gene=dds.result.loc[dds.result['sig']=='down'].sort_values('qvalue')[:3].index.tolist()
deg_gene=up_gene+down_gene


# In[53]:


sc.pl.dotplot(kidney_lineage_ad,deg_gene,
             groupby='re_anno')


# In addition to analysing directly using differential expression, we can also look for weekly marker genes by considering weeks as categories.

# In[55]:


sc.tl.dendrogram(kidney_lineage_ad,'re_anno',use_rep='scaled|original|X_pca')
sc.tl.rank_genes_groups(kidney_lineage_ad, 're_anno', use_rep='scaled|original|X_pca',
                        method='t-test',use_raw=False,key_added='re_anno_ttest')
sc.pl.rank_genes_groups_dotplot(kidney_lineage_ad,groupby='re_anno',
                                cmap='RdBu_r',key='re_anno_ttest',
                                standard_scale='var',n_genes=3)