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#!/usr/bin/env python
# coding: utf-8
# # Celltype annotation transfer in multi-omics
#
# In the field of multi-omics research, transferring cell type annotations from one data modality to another is a crucial step. For instance, when annotating cell types in single-cell ATAC sequencing (scATAC-seq) data, it's often desirable to leverage the cell type labels already annotated in single-cell RNA sequencing (scRNA-seq) data. This process involves integrating information from both scRNA-seq and scATAC-seq data modalities.
#
# GLUE is a prominent algorithm used for cross-modality integration, allowing researchers to combine data from different omics modalities effectively. However, GLUE does not inherently provide a method for transferring cell type labels from scRNA-seq to scATAC-seq data. To address this limitation, an approach was implemented in the omicverse platform using K-nearest neighbor (KNN) graphs.
#
# The KNN graph-based approach likely involves constructing KNN graphs separately for scRNA-seq and scATAC-seq data. In these graphs, each cell is connected to its K nearest neighbors based on certain similarity metrics, which could be calculated using gene expression profiles in scRNA-seq and accessibility profiles in scATAC-seq. Once these graphs are constructed, the idea is to transfer the cell type labels from the scRNA-seq side to the scATAC-seq side by assigning labels to scATAC-seq cells based on the labels of their KNN neighbors in the scRNA-seq graph.
#
# Colab_Reproducibility：https://colab.research.google.com/drive/1aIMmSgyIw-PGjJ65WvMgz4Ob3EtoK_UV?usp=sharing
# In[3]:
import omicverse as ov
import matplotlib.pyplot as plt
import scanpy as sc
ov.ov_plot_set()
# ## Loading the data preprocessed with GLUE
#
# Here, we use two output files from the GLUE cross-modal integration, and their common feature is that they both have the `obsm['X_glue']` layer. And the rna have been annotated.
# In[4]:
rna=sc.read("data/analysis_lymph/rna-emb.h5ad")
atac=sc.read("data/analysis_lymph/atac-emb.h5ad")
# We can visualize the intergrated effect of GLUE with UMAP
# In[5]:
import scanpy as sc
combined=sc.concat([rna,atac],merge='same')
combined
# In[6]:
combined.obsm['X_mde']=ov.utils.mde(combined.obsm['X_glue'])
# We can see that the two layers are correctly aligned
# In[8]:
ov.utils.embedding(combined,
basis='X_mde',
color='domain',
title='Layers',
show=False,
palette=ov.utils.red_color,
frameon='small'
)
# And the RNA modality has an already annotated cell type label on it
# In[22]:
ov.utils.embedding(rna,
basis='X_mde',
color='major_celltype',
title='Cell type',
show=False,
#palette=ov.utils.red_color,
frameon='small'
)
# ## Celltype transfer
#
# We train a knn nearest neighbour classifier using `X_glue` features
# In[13]:
knn_transformer=ov.utils.weighted_knn_trainer(
train_adata=rna,
train_adata_emb='X_glue',
n_neighbors=15,
)
# In[14]:
labels,uncert=ov.utils.weighted_knn_transfer(
query_adata=atac,
query_adata_emb='X_glue',
label_keys='major_celltype',
knn_model=knn_transformer,
ref_adata_obs=rna.obs,
)
# We migrate the training results of the KNN classifier to atac. `unc` stands for uncertainty, with higher uncertainty demonstrating lower migration accuracy, suggesting that the cell in question may be a double-fate signature or some other type of cell.
# In[15]:
atac.obs["transf_celltype"]=labels.loc[atac.obs.index,"major_celltype"]
atac.obs["transf_celltype_unc"]=uncert.loc[atac.obs.index,"major_celltype"]
# In[24]:
atac.obs["major_celltype"]=atac.obs["transf_celltype"].copy()
# In[27]:
ov.utils.embedding(atac,
basis='X_umap',
color=['transf_celltype_unc','transf_celltype'],
#title='Cell type Un',
show=False,
palette=ov.palette()[11:],
frameon='small'
)
# ## Visualization
#
# We can merge atac and rna after migration annotation and observe on the umap plot whether the cell types are consistent after merging the modalities.
# In[28]:
import scanpy as sc
combined1=sc.concat([rna,atac],merge='same')
combined1
# In[29]:
combined1.obsm['X_mde']=ov.utils.mde(combined1.obsm['X_glue'])
# We found that the annotation was better, suggesting that the KNN nearest-neighbour classifier we constructed can effectively migrate cell type labels from RNA to ATAC.
# In[31]:
ov.utils.embedding(combined1,
basis='X_mde',
color=['domain','major_celltype'],
title=['Layers','Cell type'],
show=False,
palette=ov.palette()[11:],
frameon='small'
)
# In[ ]:
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