Tutorial 3: Slide-seq or Stereo-seq dataset
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In this tutorial, we show how to apply scSTADE to identify spatial domains on Slide-seq or Stereo-seq. As a example, we analyse the Stereo dataset.
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The source code package is freely available at https://github.com/cuiyaxuan/scSTADE/tree/master. The datasets used in this study can be found at https://drive.google.com/drive/folders/1H-ymfCqlDR1wpMRX-bCewAjG5nOrIF51?usp=sharing.
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.. code:: ipython3
import sys
sys.path.append("/.../scSTADE-master/scSTADE_Cluster_Functions")
# Input the path.
from scSTADE import scSTADE
import os
import torch
import pandas as pd
import scanpy as sc
from sklearn import metrics
import multiprocessing as mp
def setup_seed(seed=41):
import torch
import os
import numpy as np
import random
torch.manual_seed(seed)
np.random.seed(seed) # Numpy module.
random.seed(seed) # Python random module.
if torch.cuda.is_available():
# torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
#os.environ['PYTHONHASHSEED'] = str(seed)
setup_seed(41)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
n_clusters = 10 ###### the number of spatial domains.
file_path = '/home/cuiyaxuan/spatialLIBD/5.Mouse_Olfactory/' #please replace 'file_path' with the download path
adata = sc.read_h5ad(file_path + 'filtered_feature_bc_matrix.h5ad') #### project name
adata.var_names_make_unique()
model = scSTADE(adata,datatype='Slide',device=device,n_top_genes=4000)
adata = model.train()
radius = 50
tool = 'mclust' # mclust, leiden, and louvain
from utils import clustering
if tool == 'mclust':
clustering(adata, n_clusters, radius=radius, method=tool, refinement=True)
elif tool in ['leiden', 'louvain']:
clustering(adata, n_clusters, radius=radius, method=tool, start=0.1, end=2.0, increment=0.01, refinement=False)
adata.obs['domain']
adata.obs['domain'].to_csv("label.csv")
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/home/cuiyaxuan/anaconda3/envs/pytorch/lib/python3.8/site-packages/scanpy/preprocessing/_highly_variable_genes.py:62: UserWarning: `flavor='seurat_v3'` expects raw count data, but non-integers were found.
warnings.warn(
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Graph constructed!
Building sparse matrix ...
Begin to train ST data...
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