ovrawm/t_simba.txt

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

# # Data integration and batch correction with SIMBA
# 
# Here we will use three scRNA-seq human pancreas datasets of different studies as an example to illustrate how SIMBA performs scRNA-seq batch correction for multiple batches
# 
# We follow the corresponding tutorial at [SIMBA](https://simba-bio.readthedocs.io/en/latest/rna_human_pancreas.html). We do not provide much explanation, and instead refer to the original tutorial.
# 
# Paper: [SIMBA: single-cell embedding along with features](https://www.nature.com/articles/s41592-023-01899-8)
# 
# Code: https://github.com/huidongchen/simba

# In[1]:


import omicverse as ov
from omicverse.utils import mde
workdir = 'result_human_pancreas'
ov.utils.ov_plot_set()


# We need to install simba at first
# 
# ```
# conda install -c bioconda simba
# ```
# 
# or
# 
# ```
# pip install git+https://github.com/huidongchen/simba
# pip install git+https://github.com/pinellolab/simba_pbg
# ```

# ## Read data
# 
# The anndata object was concat from three anndata in simba: `simba.datasets.rna_baron2016()`, `simba.datasets.rna_segerstolpe2016()`, and `simba.datasets.rna_muraro2016()`
# 
# It can be downloaded from figshare: https://figshare.com/ndownloader/files/41418600

# In[2]:


adata=ov.utils.read('simba_adata_raw.h5ad')


# We need to set workdir to initiate the pySIMBA object

# In[3]:


simba_object=ov.single.pySIMBA(adata,workdir)


# ## Preprocess
# 
# Follow the raw tutorial, we set the paragument as default.

# In[4]:


simba_object.preprocess(batch_key='batch',min_n_cells=3,
                    method='lib_size',n_top_genes=3000,n_bins=5)


# ## Generate a graph for training
# 
# Observations and variables within each Anndata object are both represented as nodes (entities).
# 
# the data store in `simba_object.uns['simba_batch_edge_dict']`

# In[5]:


simba_object.gen_graph()


# ## PBG training
# 
# Before training, let’s take a look at the current parameters:
# 
# - dict_config['workers'] = 12 #The number of CPUs.

# In[10]:


simba_object.train(num_workers=6)


# In[6]:


simba_object.load('result_human_pancreas/pbg/graph0')


# ## Batch correction
# 
# Here, we use `simba_object.batch_correction()` to perform the batch correction
# 
# <div class="admonition note">
#   <p class="admonition-title">Note</p>
#   <p>
#     If the batch is greater than 10, then the batch correction is less effective
#   </p>
# </div>

# In[7]:


adata=simba_object.batch_correction()
adata


# ## Visualize
# 
# We also use `mde` instead `umap` to visualize the result

# In[8]:


adata.obsm["X_mde"] = mde(adata.obsm["X_simba"])


# In[11]:


sc.pl.embedding(adata,basis='X_mde',color=['cell_type1','batch'])


# Certainly, umap can also be used to visualize

# In[10]:


import scanpy as sc
sc.pp.neighbors(adata, use_rep="X_simba")
sc.tl.umap(adata)
sc.pl.umap(adata,color=['cell_type1','batch'])


# In[ ]: