ovrawm/t_mofa_glue.txt

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

# # Multi omics analysis by MOFA and GLUE
# MOFA is a factor analysis model that provides a general framework for the integration of multi-omic data sets in an unsupervised fashion.
# 
# Most of the time, however, we did not get paired cells in the multi-omics analysis. Here, we can pair cells using GLUE, a dimensionality reduction algorithm that can integrate different histological layers, and it can efficiently merge data from different histological layers.
# 
# This tutorial focuses on how to perform mofa in multi-omics using GLUE.
# 
# Paper: [MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02015-1) and [Multi-omics single-cell data integration and regulatory inference with graph-linked embedding](https://www.nature.com/articles/s41587-022-01284-4)
# 
# Code: https://github.com/bioFAM/mofapy2 and https://github.com/gao-lab/GLUE
# 
# Colab_Reproducibility：https://colab.research.google.com/drive/1zlakFf20IoBdyuOQDocwFQHu8XOVizRL?usp=sharing
# 
# We used the result anndata object `rna-emb.h5ad` and `atac.emb.h5ad` from [GLUE'tutorial](https://scglue.readthedocs.io/en/latest/training.html)

# In[1]:


import omicverse as ov
ov.utils.ov_plot_set()


# ## Load the data
# 
# We use `ov.utils.read` to read the `h5ad` files

# In[2]:


rna=ov.utils.read("chen_rna-emb.h5ad")
atac=ov.utils.read("chen_atac-emb.h5ad")


# ## Pair the omics 
# 
# Each cell in our rna and atac data has a feature vector, X_glue, based on which we can calculate the Pearson correlation coefficient to perform cell matching.

# In[3]:


pair_obj=ov.single.GLUE_pair(rna,atac)
pair_obj.correlation()


# We counted the top 50 highly correlated cells in another histology layer for each cell in one of the histology layers to avoid missing data due to one cell being highly correlated with multiple cells. The default minimum threshold for high correlation is 0.9. We can obtain more paired cells by increasing the depth, but note that increasing the depth may lead to higher errors in cell matching

# In[4]:


res_pair=pair_obj.find_neighbor_cell(depth=20)
res_pair.to_csv('models/chen_pair_res.csv')


# We filter to get paired cells

# In[14]:


rna1=rna[res_pair['omic_1']]
atac1=atac[res_pair['omic_2']]
rna1.obs.index=res_pair.index
atac1.obs.index=res_pair.index
rna1,atac1


# We can use mudata to store the multi-omics 

# In[6]:


from mudata import MuData

mdata = MuData({'rna': rna1, 'atac': atac1})
mdata


# In[7]:


mdata.write("chen_mu.h5mu",compression='gzip')


# ## MOFA prepare
# 
# In the MOFA analysis, we only need to use highly variable genes, for which we perform one filter

# In[22]:


rna1=mdata['rna']
rna1=rna1[:,rna1.var['highly_variable']==True]
atac1=mdata['atac']
atac1=atac1[:,atac1.var['highly_variable']==True]
rna1.obs.index=res_pair.index
atac1.obs.index=res_pair.index


# In[23]:


import random
random_obs_index=random.sample(list(rna1.obs.index),5000)


# In[25]:


from sklearn.metrics import adjusted_rand_score as ari
ari_random=ari(rna1[random_obs_index].obs['cell_type'], atac1[random_obs_index].obs['cell_type'])
ari_raw=ari(rna1.obs['cell_type'], atac1.obs['cell_type'])
print('raw ari:{}, random ari:{}'.format(ari_raw,ari_random))


# In[26]:


#rna1=rna1[random_obs_index]
#atac1=atac1[random_obs_index]


# ## MOFA analysis
# 
# In this part, we construct a model of mofa by scRNA-seq and scATAC-seq

# In[28]:


test_mofa=ov.single.pyMOFA(omics=[rna1,atac1],
                             omics_name=['RNA','ATAC'])


# In[29]:


test_mofa.mofa_preprocess()
test_mofa.mofa_run(outfile='models/chen_rna_atac.hdf5')


# ## MOFA Visualization
# 
# In this part, we provide a series of function to visualize the result of mofa.

# In[30]:


pymofa_obj=ov.single.pyMOFAART(model_path='models/chen_rna_atac.hdf5')


# In[31]:


pymofa_obj.get_factors(rna1)
rna1


# ### Visualize the varience of each view

# In[32]:


pymofa_obj.plot_r2()


# In[33]:


pymofa_obj.get_r2()


# ### Visualize the correlation between factor and celltype

# In[37]:


pymofa_obj.plot_cor(rna1,'cell_type',figsize=(4,6))


# In[38]:


pymofa_obj.get_cor(rna1,'cell_type')


# In[46]:


pymofa_obj.plot_factor(rna1,'cell_type','Ast',figsize=(3,3),
                    factor1=1,factor2=3,)


# ### Visualize the factor in UMAP
# 
# To visualize the GLUE’s learned embeddings, we use the pymde package wrapperin scvi-tools. This is an alternative to UMAP that is GPU-accelerated.
# 
# You can use `sc.tl.umap` insteaded.

# In[41]:


from scvi.model.utils import mde
import scanpy as sc
sc.pp.neighbors(rna1, use_rep="X_glue", metric="cosine")
rna1.obsm["X_mde"] = mde(rna1.obsm["X_glue"])


# In[47]:


sc.pl.embedding(
    rna1,
    basis="X_mde",
    color=["factor1","factor3","cell_type"],
    frameon=False,
    ncols=3,
    #palette=ov.utils.pyomic_palette(),
    show=False,
    cmap='Greens',
    vmin=0,
)


# ### Weights ranked
# A visualization of factor weights familiar to MOFA and MOFA+ users is implemented with some modifications in `plot_weight_gene_d1`, `plot_weight_gene_d2`, and `plot_weights`.

# In[48]:


pymofa_obj.plot_weight_gene_d1(view='RNA',factor1=1,factor2=3,)


# In[50]:


pymofa_obj.plot_weights(view='RNA',factor=1,
                        ascending=False)


# ### Weights heatmap
# 
# While trying to annotate factors, a global overview of top features defining them could be helpful.

# In[51]:


pymofa_obj.plot_top_feature_heatmap(view='RNA')


# In[ ]: