Integration for Mouse Spleen data
loading package
[ ]:
import os
import dgl
import pandas as pd
import sys
import scanpy as sc
import importlib
import torch as th
import torch.nn as nn
from sklearn.utils import shuffle
from model_integration import *
from matplotlib import rcParams
config = {
"font.family":'Times New Roman',
"font.size":20,
"axes.unicode_minus": False
}
rcParams.update(config)
/data/xiangdw/conda_env/GNNS/lib/python3.8/site-packages/torchdata/datapipes/__init__.py:18: UserWarning:
################################################################################
WARNING!
The 'datapipes', 'dataloader2' modules are deprecated and will be removed in a
future torchdata release! Please see https://github.com/pytorch/data/issues/1196
to learn more and leave feedback.
################################################################################
deprecation_warning()
/data/xiangdw/conda_env/GNNS/lib/python3.8/site-packages/tqdm/auto.py:21: 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
/data/xiangdw/MODEL
Integration
[2]:
import torch.nn.functional as F
from SpaMIE.create_graph import Sagegraph
from SpaMIE.spamie_main import Sagewrapper
import numpy as np
from model_integration import set_seed
device = th.device('cuda:0' if th.cuda.is_available() else 'cpu')
for i in range(1):
seeds = str(i+1)
path = '/data/xiangdw/data/data/'
adata_omics1 = sc.read_h5ad(path + 'adata_RNA_spots.h5ad')
adata_omics2 = sc.read_h5ad(path + 'adata_ADT_spots.h5ad')
adata_omics1.obs['batch'] = adata_omics1.obs['batch'].astype(str)
adata_omics2.obs['batch'] = adata_omics2.obs['batch'].astype(str)
set_seed(2024)
modalities = [adata_omics1, adata_omics2]
g_spatial_omics1, g_feature_omics1, g_spatial_omics2, g_feature_omics2, adata_omics1, adata_omics2 = Sagegraph(modalities, device, task='Integration',
datatype="spots",batch=True)
in_feat = adata_omics1.obsm['feat'].shape[1]
out_feat = adata_omics2.X.shape[1]
weight = [1,1,1]
model = Sagewrapper(seed=(int(seeds)), device=device, in_feat=in_feat, n_hidden=256, out_feat=out_feat, task='integration', datatype='spots',
layers_nums=int(3), weight=weight, epoch=600, res_type='res_add', activation=nn.LeakyReLU
, sagetype='mean', lr=2e-4, lr2 = 0.002)
output = model.fit( adata_omics1, adata_omics2, g_spatial_omics1, g_feature_omics1, g_spatial_omics2, g_feature_omics2, weight_factors=[1,5,1,1])
adata_omics2.obsm['SpaMIE'] = output[0].detach().cpu().numpy()
/data/xiangdw/conda_env/GNNS/lib/python3.8/site-packages/scanpy/preprocessing/_highly_variable_genes.py:61: UserWarning: `flavor='seurat_v3'` expects raw count data, but non-integers were found.
warnings.warn(
2025-10-17 16:05:38,700 - harmonypy - INFO - Computing initial centroids with sklearn.KMeans...
2025-10-17 16:05:41,244 - harmonypy - INFO - sklearn.KMeans initialization complete.
2025-10-17 16:05:41,284 - harmonypy - INFO - Iteration 1 of 10
2025-10-17 16:05:42,828 - harmonypy - INFO - Iteration 2 of 10
2025-10-17 16:05:44,442 - harmonypy - INFO - Iteration 3 of 10
2025-10-17 16:05:46,008 - harmonypy - INFO - Iteration 4 of 10
2025-10-17 16:05:47,612 - harmonypy - INFO - Iteration 5 of 10
2025-10-17 16:05:49,220 - harmonypy - INFO - Iteration 6 of 10
2025-10-17 16:05:50,030 - harmonypy - INFO - Iteration 7 of 10
2025-10-17 16:05:50,708 - harmonypy - INFO - Iteration 8 of 10
2025-10-17 16:05:51,249 - harmonypy - INFO - Iteration 9 of 10
2025-10-17 16:05:51,790 - harmonypy - INFO - Iteration 10 of 10
2025-10-17 16:05:53,327 - harmonypy - INFO - Stopped before convergence
2025-10-17 16:05:53,345 - harmonypy - INFO - Computing initial centroids with sklearn.KMeans...
2025-10-17 16:05:55,490 - harmonypy - INFO - sklearn.KMeans initialization complete.
2025-10-17 16:05:55,509 - harmonypy - INFO - Iteration 1 of 10
2025-10-17 16:05:56,840 - harmonypy - INFO - Iteration 2 of 10
2025-10-17 16:05:58,064 - harmonypy - INFO - Iteration 3 of 10
2025-10-17 16:05:59,338 - harmonypy - INFO - Iteration 4 of 10
2025-10-17 16:06:00,579 - harmonypy - INFO - Iteration 5 of 10
2025-10-17 16:06:01,875 - harmonypy - INFO - Iteration 6 of 10
2025-10-17 16:06:03,160 - harmonypy - INFO - Iteration 7 of 10
2025-10-17 16:06:04,474 - harmonypy - INFO - Iteration 8 of 10
2025-10-17 16:06:05,546 - harmonypy - INFO - Iteration 9 of 10
2025-10-17 16:06:06,201 - harmonypy - INFO - Iteration 10 of 10
2025-10-17 16:06:06,907 - harmonypy - INFO - Converged after 10 iterations
/data/xiangdw/MODEL/SpaMIE/spamie_net.py:98: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
self.alpha = F.softmax(torch.squeeze(self.vu) + 1e-6)
/data/xiangdw/MODEL/SpaMIE/spamie_net.py:66: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
self.alpha = F.softmax(torch.squeeze(self.vu) + 1e-6)
Early stopped.
Visualisation
[ ]:
path = '/data/xiangdw/data/data/spots/'
from SpatialGlue.utils import clustering
fig, axs = plt.subplots(1, 2, figsize=(14, 7), dpi=300)
plt.rcParams['font.size'] = 20
tool = 'louvain'
adata = sc.read_h5ad(path + "MouseSpleen_concat_methods.h5ad")
clustering(adata_omics2, key='SpaMIE', add_key='SpaMIE',start=0.001, end=1, increment=0.1, n_clusters=5, method=tool, use_pca=False)
adata.obsm['SpaMIE'] = adata_omics2.obs['SpaMIE'].copy()
adata1 = adata[adata.obs['batch'] == '1'].copy()
adata2 = adata[adata.obs['batch'] == '1'].copy()
sc.pl.embedding(adata1, basis='spatial', color=['SpaMIE'], ax=axs[0], title='SpaMIE', s=150, show=False)
sc.pl.embedding(adata2, basis='spatial', color=['SpaMIE'], ax=axs[1], title='SpaMIE', s=150, show=False)
Searching resolution...
resolution=0.901, cluster number=5
/data/xiangdw/conda_env/GNNS/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
cax = scatter(
/data/xiangdw/conda_env/GNNS/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
cax = scatter(
<Axes: title={'center': 'SpaMIE'}, xlabel='spatial1', ylabel='spatial2'>
