Tutorial 1: DLPFC Data Analysis (10X Visium)

This tutorial guides you through the process of identifying spatial domains in Visium data from the human dorsolateral prefrontal cortex (DLPFC) using SemanticST.

Import necessary packages

from sklearn import metrics
import torch
import copy
import os
import random
import numpy as np
from semanticst.loading_batches import PrepareDataloader
from semanticst.loading_batches import Dataloader
import scanpy as sc
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import torch.utils.data as data
from semanticst.main import Config

/home/roxana/anaconda3/envs/semanticst3/lib/python3.9/site-packages/torch/__config__.py:10: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 11040). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)
  return torch._C._show_config()

Read data and import device

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(f"You are using *{device}*")
BASE_PATH = Path('/media/rokny/DATA1/Roxana/Data/1.DLPFC/')
sample_name ='151673'
section_id = '151673'
spot_paths= Path(f'{BASE_PATH}/{sample_name}/')
if sample_name in ['151669', '151670', '151671', '151672']:
    n_clusters = 5
else:
    n_clusters = 7

You are using *cuda:0*

dataset="DLPFC"
adata = sc.read_visium(spot_paths)
adata.var_names_make_unique()
print(adata)

/home/roxana/anaconda3/envs/semanticst3/lib/python3.9/site-packages/anndata/_core/anndata.py:1756: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.
  utils.warn_names_duplicates("var")
/home/roxana/anaconda3/envs/semanticst3/lib/python3.9/site-packages/anndata/_core/anndata.py:1756: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.
  utils.warn_names_duplicates("var")

AnnData object with n_obs × n_vars = 3639 × 33538
    obs: 'in_tissue', 'array_row', 'array_col'
    var: 'gene_ids', 'feature_types', 'genome'
    uns: 'spatial'
    obsm: 'spatial'

Train the model

Available data types: ‘’Xenium’, ‘Visium’, ‘Stereo’, Slide.
It is essential to specify the type of ST data, as different ST technologies require distinct preprocessing steps. Additionally, you have the option to select between mini-batch training for large datasets and full dataset training for smaller ones, ensuring efficient data processing and model performance.

dtype = "Visium"  # Change to "h5ad" if necessary
config=Config(device=device,dtype=dtype, use_mini_batch=False)
from semanticst.SemanticST_main import Semantic as Trainer
config_used = copy.copy(config)
model = Trainer(adata,config)  
adata=model.train()  # Train the model

🚀 Welcome to SemanticST! 🚀

📢 Recommendation: If your dataset contains more than 40000 spots or cells, we suggest using **mini-batch training** for efficiency.

✅ Using Full Dataset Training (No Mini-Batching). 🔥

/home/roxana/anaconda3/envs/semanticst3/lib/python3.9/site-packages/numba/np/ufunc/parallel.py:371: NumbaWarning: The TBB threading layer requires TBB version 2021 update 6 or later i.e., TBB_INTERFACE_VERSION >= 12060. Found TBB_INTERFACE_VERSION = 12050. The TBB threading layer is disabled.
  warnings.warn(problem)

Begin to train ST data...

Learning Semantic graphs: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 250/250 [00:12<00:00, 19.68epoch/s]

Semantic Graph Learning Completed

Feature Learning Epochs: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [05:25<00:00,  3.07it/s]

4. Plot Ground Truth

df_meta = pd.read_csv(f'{spot_paths}/metadata.tsv', sep='\t')
df_meta_layer = df_meta['layer_guess']
adata.obs['ground_truth'] = df_meta_layer.values
adata = adata[~pd.isnull(adata.obs['ground_truth'])]

plt.rcParams["figure.figsize"] = (3,3)
sc.pl.embedding(adata, basis="spatial", color="ground_truth",palette='coolwarm', show=False, title='')
plt.gca().invert_yaxis()  # This will invert the y-axis
plt.axis('off')
plt.show()

Clustering

- mclust 

# clustering

n_cluster=7
tool = 'mclust' # mclust, leiden, and louvain

# clustering
from semanticst.utils import clustering
clustering(adata,seed=2025, n_clusters=n_cluster, method=tool,key='emb_decoder')

/home/roxana/anaconda3/envs/semanticst3/lib/python3.9/site-packages/semanticst/utils.py:68: ImplicitModificationWarning: Setting element `.obsm['emb_pca']` of view, initializing view as actual.
  adata.obsm['emb_pca'] = embedding
Unable to determine R library path: Command '('/usr/lib/R/bin/Rscript', '-e', 'cat(Sys.getenv("LD_LIBRARY_PATH"))')' returned non-zero exit status 1.
R[write to console]:                    __           __ 
   ____ ___  _____/ /_  _______/ /_
  / __ `__ \/ ___/ / / / / ___/ __/
 / / / / / / /__/ / /_/ (__  ) /_  
/_/ /_/ /_/\___/_/\__,_/____/\__/   version 6.0.0
Type 'citation("mclust")' for citing this R package in publications.

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

ARI = metrics.adjusted_rand_score(adata.obs['domain'], adata.obs['ground_truth'])
NMI = metrics.normalized_mutual_info_score(adata.obs['domain'], adata.obs['ground_truth'])
HMO= metrics.homogeneity_score(adata.obs['domain'], adata.obs['ground_truth'])
print('===== {} ARI score: {:.3f}'.format(sample_name, ARI))

print('===== {} NMI score: {:.3f}'.format(sample_name, NMI))

print('===== {} HMO score: {:.3f}'.format(sample_name, HMO))

plt.rcParams["figure.figsize"] = (3,3)
sc.pl.embedding(adata, basis="spatial", color="domain",palette='coolwarm', show=False, title="SemanticST")
plt.gca().invert_yaxis()  # This will invert the y-axis
plt.axis('off')
# plt.savefig(f"{dir_output}/pred.png", dpi=300,bbox_inches='tight')
plt.show()

===== 151673 ARI score: 0.641
===== 151673 NMI score: 0.744
===== 151673 HMO score: 0.725