Tutorial 1: 10X Visium DLPFC dataset¶
In this tutorial, we show how to apply DiffusionST to identify spatial domains on 10X Visium data. As a example, we analyse the 151673 sample of the dorsolateral prefrontal cortex (DLPFC) dataset.
The source code package is freely available at https://github.com/cuiyaxuan/DiffusionST. The datasets used in this study can be found at https://drive.google.com/drive/folders/1qgn2UKpu4q14ysCoCKjWYVEHXIzHNoqq?usp=drive_link.
First, cd /home/.../DiffusionST-main
from DenoiseST import DenoiseST
import os
import torch
import pandas as pd
import numpy as np
import scanpy as sc
from sklearn import metrics
import multiprocessing as mp
device = torch.device('cuda:2' if torch.cuda.is_available() else 'cpu')
n_clusters = 7
file_fold = '/home/cuiyaxuan/spatialLIBD/151673'
adata = sc.read_visium(file_fold, count_file='151673_filtered_feature_bc_matrix.h5', load_images=True)
adata.var_names_make_unique()
/home/cuiyaxuan/anaconda3/envs/DiffusionST/lib/python3.8/site-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call .var_names_make_unique.
utils.warn_names_duplicates("var")
model = DenoiseST(adata,device=device,n_top_genes=4096)
adata = model.train()
Begin to train ST data...
0%|▏ | 2/500 [00:00<03:16, 2.53it/s]
*********************epoch: 0 loss: tensor(19.6874, device='cuda:2', grad_fn=<AddBackward0>)
0
*********************epoch: 1 loss: tensor(18.9238, device='cuda:2', grad_fn=<AddBackward0>)
0
1%|▎ | 4/500 [00:01<01:41, 4.87it/s]
*********************epoch: 2 loss: tensor(16.1389, device='cuda:2', grad_fn=<AddBackward0>)
0
*********************epoch: 3 loss: tensor(16.8656, device='cuda:2', grad_fn=<AddBackward0>)
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*********************epoch: 4 loss: tensor(16.5659, device='cuda:2', grad_fn=<AddBackward0>)
0
1%|▌ | 6/500 [00:01<01:15, 6.53it/s]
100%|████████████████████████████████████████▊| 498/500 [00:47<00:00, 10.55it/s]
*********************epoch: 497 loss: tensor(8.3172, device='cuda:2', grad_fn=<AddBackward0>)
0
*********************epoch: 498 loss: tensor(8.3168, device='cuda:2', grad_fn=<AddBackward0>)
0
*********************epoch: 499 loss: tensor(8.3162, device='cuda:2', grad_fn=<AddBackward0>)
0
100%|█████████████████████████████████████████| 500/500 [00:48<00:00, 10.41it/s]
from repair_model import main_repair
df=pd.DataFrame(adata.obsm['emb'])
main_repair(adata,df,device)
/home/cuiyaxuan/anaconda3/envs/DiffusionST/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
Processing: 0%| | 1/3639 [00:03<3:55:26, 3.88s/it]
50
Processing: 0%| | 2/3639 [00:06<3:23:57, 3.36s/it]
Processing: 10%|██▋ | 369/3639 [18:30<2:45:40, 3.04s/it]
50
Processing: 100%|█████████████████████████| 3639/3639 [3:08:34<00:00, 3.11s/it]
csv_file = "example.csv"
data_df = pd.read_csv(csv_file, header=None)
data_df = data_df.values
adata.obsm['emb'] = data_df
from utils import clustering
radius = 50
tool = 'mclust' # mclust, leiden, and louvain
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)
df=adata.obs['domain']
df.to_csv("label_673.csv")
import matplotlib as mpl
import scanpy as sc
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
import visual
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams["font.sans-serif"] = "Arial"
warnings.filterwarnings('ignore')
file_fold = '/home/cuiyaxuan/spatialLIBD/151673/' # your path
adata = sc.read_visium(file_fold, count_file='151673_filtered_feature_bc_matrix.h5', load_images=True)
df_label=pd.read_csv('./label_673.csv', index_col=0)
#df_label=pd.read_csv('./label_5000.csv', index_col=0) ##If the dropout rate is less than 0.85, visualize the data using "label_5000.csv".
visual.visual(adata,df_label)
#cells after MT filter: 3639
WARNING: saving figure to file figures/showvisualdomainplot_plot.pdf
