Tutorial 2: Clustering on mouse_anterior_posterior_brain_merged

In this tutorial, we will show how to use the stCAMBL model to perform spatial transcriptomic analysis of mouse brain slices. Relevant data can be obtained from https://drive.google.com/drive/folders/141__9Q4zYK_6A4stKsOE5WikIHgxFkhk

Import the relevant python analysis package

import scanpy as sc
import pandas as pd
from sklearn import metrics
import torch
import matplotlib.pyplot as plt
import os
import warnings
warnings.filterwarnings('ignore')
import stCAMBL
import os
#Please change this path to your local R environment path
os.environ['R_HOME'] = '/data3/wkcui/env/anaconda3/envs/stCAMBL/lib/R'

Read data and perform data preprocessing

dataset = 'Mouse_brain'
random_seed = 2050
stCAMBL.set_seed(random_seed)
device = 'cuda:1' if torch.cuda.is_available() else 'cpu'
# read data
# Please change this path to your local data path
file_path = '/data3/yfchen/stCAMBL/data/mouse_anterior_posterior_brain_merged.h5ad'
n_clusters = 26
adata = sc.read_h5ad(file_path)
adata.var_names_make_unique()
# preprocess data
sc.pp.filter_genes(adata, min_cells=50)
sc.pp.filter_genes(adata, min_counts=10)
sc.pp.normalize_total(adata, target_sum=1e6)
sc.pp.highly_variable_genes(adata, flavor="seurat_v3", n_top_genes=2000)

adata = adata[:, adata.var['highly_variable'] == True]

sc.pp.scale(adata)

Perform stCAMBL analysis

from sklearn.decomposition import PCA
adata_X = PCA(n_components=200, random_state=42).fit_transform(adata.X)
adata.obsm['X_pca'] = adata_X

graph_dict = stCAMBL.graph_construction(adata, 12)
model = stCAMBL.stCAMBL(dataset, adata.obsm['X_pca'], graph_dict, device=device)
# Begin to train the model
model.train_model(epochs=400)
stCAMBL_feat, defeat, _, _, _ = model.process()
adata.obsm['emb'] = stCAMBL_feat

100%|██████████| 400/400 [01:49<00:00,  3.65it/s]

clustering

radius = 50
tool = 'mclust'
# clustering
from stCAMBL.clust_func import clustering
clustering(adata, n_clusters, radius=radius, method=tool, refinement=True)

# plotting spatial clustering result
adata.obsm['spatial'][:,1] = -1*adata.obsm['spatial'][:,1]
import seaborn as sns
rgb_values = sns.color_palette("tab20", len(adata.obs['mclust'].unique()))
color_fine = dict(zip(list(adata.obs['mclust'].unique()), rgb_values))

plt.rcParams["figure.figsize"] = (17, 6)
sc.pl.embedding(adata, basis="spatial",
                color="mclust",
                s=100,
                palette=color_fine,
                show=True,
                title='Mouse Anterior & Posterior Brain (Section 1)')

R[write to console]:                    __           __
   ____ ___  _____/ /_  _______/ /_
  / __ `__ \/ ___/ / / / / ___/ __/
 / / / / / / /__/ / /_/ (__  ) /_
/_/ /_/ /_/\___/_/\__,_/____/\__/   version 6.1.1
Type 'citation("mclust")' for citing this R package in publications.

fitting ...
  |======================================================================| 100%