ovrawm/t_bulk_combat.txt

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

# # Batch correction in Bulk RNA-seq or microarray data
# 
# Variability in datasets are not only the product of biological processes: they are also the product of technical biases (Lander et al, 1999). ComBat is one of the most widely used tool for correcting those technical biases called batch effects.
# 
# pyComBat (Behdenna et al, 2020) is a new Python implementation of ComBat (Johnson et al, 2007), a software widely used for the adjustment of batch effects in microarray data. While the mathematical framework is strictly the same, pyComBat:
# 
# - has similar results in terms of batch effects correction;
# - is as fast or faster than the R implementation of ComBat and;
# - offers new tools for the community to participate in its development.
# 
# Paper: [pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods](https://doi.org/10.1101/2020.03.17.995431)
# 
# Code: https://github.com/epigenelabs/pyComBat
# 
# Colab_Reproducibility：https://colab.research.google.com/drive/121bbIiI3j4pTZ3yA_5p8BRkRyGMMmNAq?usp=sharing

# In[7]:


import anndata
import pandas as pd
import omicverse as ov
ov.ov_plot_set()


# ## Loading dataset
# 
# This minimal usage example illustrates how to use pyComBat in a default setting, and shows some results on ovarian cancer data, freely available on NCBI’s [Gene Expression Omnibus](https://www.ncbi.nlm.nih.gov/geo/), namely:
# 
# - GSE18520
# - GSE66957
# - GSE69428
# 
# The corresponding expression files are available on [GitHub](https://github.com/epigenelabs/pyComBat/tree/master/data).

# In[15]:


dataset_1 = pd.read_pickle("data/combat/GSE18520.pickle")
adata1=anndata.AnnData(dataset_1.T)
adata1.obs['batch']='1'
adata1


# In[16]:


dataset_2 = pd.read_pickle("data/combat/GSE66957.pickle")
adata2=anndata.AnnData(dataset_2.T)
adata2.obs['batch']='2'
adata2


# In[17]:


dataset_3 = pd.read_pickle("data/combat/GSE69428.pickle")
adata3=anndata.AnnData(dataset_3.T)
adata3.obs['batch']='3'
adata3


# We use the concat function to join the three datasets together and take the intersection for the same genes

# In[18]:


adata=anndata.concat([adata1,adata2,adata3],merge='same')
adata


# ## Removing batch effect

# In[31]:


ov.bulk.batch_correction(adata,batch_key='batch')


# ## Saving results
# 
# Raw datasets

# In[70]:


raw_data=adata.to_df().T
raw_data.head()


# Removing Batch datasets

# In[71]:


removing_data=adata.to_df(layer='batch_correction').T
removing_data.head()


# save

# In[ ]:


raw_data.to_csv('raw_data.csv')
removing_data.to_csv('removing_data.csv')


# You can also save adata object

# In[ ]:


adata.write_h5ad('adata_batch.h5ad',compression='gzip')
#adata=ov.read('adata_batch.h5ad')


# ## Compare the dataset before and after correction
# 
# We specify three different colours for three different datasets

# In[51]:


color_dict={
    '1':ov.utils.red_color[1],
    '2':ov.utils.blue_color[1],
    '3':ov.utils.green_color[1],
}


# In[57]:


fig,ax=plt.subplots( figsize = (20,4))
bp=plt.boxplot(adata.to_df().T,patch_artist=True)
for i,batch in zip(range(adata.shape[0]),adata.obs['batch']):
    bp['boxes'][i].set_facecolor(color_dict[batch])
ax.axis(False)
plt.show()


# In[58]:


fig,ax=plt.subplots( figsize = (20,4))
bp=plt.boxplot(adata.to_df(layer='batch_correction').T,patch_artist=True)
for i,batch in zip(range(adata.shape[0]),adata.obs['batch']):
    bp['boxes'][i].set_facecolor(color_dict[batch])
ax.axis(False)
plt.show()


# In addition to using boxplots to observe the effect of batch removal, we can also use PCA to observe the effect of batch removal

# In[59]:


adata.layers['raw']=adata.X.copy()


# We first calculate the PCA on the raw dataset

# In[60]:


ov.pp.pca(adata,layer='raw',n_pcs=50)
adata


# We then calculate the PCA on the batch_correction dataset

# In[61]:


ov.pp.pca(adata,layer='batch_correction',n_pcs=50)
adata


# In[62]:


ov.utils.embedding(adata,
                  basis='raw|original|X_pca',
                  color='batch',
                  frameon='small')


# In[63]:


ov.utils.embedding(adata,
                  basis='batch_correction|original|X_pca',
                  color='batch',
                  frameon='small')


# In[ ]: