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Partial alignment of Spateo for 2D slices

In this tutorials, we will learn

import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print("Running this notebook on: ", device)

import spateo as st
print("Last run with spateo version:", st.__version__)

# Other imports
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import scanpy as sc
import anndata as ad
import numpy as np

# Uncomment the following if running on the server
import os
os.system('/usr/bin/Xvfb :99 -screen 0 1024x768x24 &')
os.environ['DISPLAY'] = ':99'

%config InlineBackend.print_figure_kwargs={'facecolor' : "w"}
%config InlineBackend.figure_format='retina'
%load_ext autoreload
%autoreload 2

Running this notebook on:  cuda

2024-08-14 22:16:52.858430: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-08-14 22:16:53.020238: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2024-08-14 22:16:53.020265: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2024-08-14 22:16:53.695175: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2024-08-14 22:16:53.695263: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2024-08-14 22:16:53.695271: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

Last run with spateo version: 1.0.2.dev519+46c6d93.dirty

(EE)
Fatal server error:
(EE) Server is already active for display 99
        If this server is no longer running, remove /tmp/.X99-lock
        and start again.
(EE)

Load the data

The data used in this tutorial can be downloaded using the following links:

• Adult mouse central nervous system from STARMap Plus dataset sagittal #3

# Load the STARMap Plus slice
adata = st.read('./data/sagittal3.h5ad')

adata

AnnData object with n_obs × n_vars = 207591 × 1022
    obs: 'Sub_molecular_cell_type', 'Main_molecular_tissue_region', 'Sub_molecular_tissue_region', 'Molecular_spatial_cell_type'
    uns: 'Main_molecular_tissue_region_palette'
    obsm: 'spatial', 'spatial_raw'

Cut pesudo slices

import sys
sys.path.insert(0, "/home/ylu/project")
from utils import *

# slices = st.align.split_slice(adata=adata,spatial_key='spatial_raw',split_num=4,axis=2,)
slices = split_slice(adata=adata,spatial_key='spatial_raw',split_num=4,axis=2,)

spatial_key = 'spatial_raw'
cluster_key = 'Main_molecular_tissue_region'
palette = adata.uns['Main_molecular_tissue_region_palette']
st.pl.slices_2d(
    slices = slices,
    label_key = cluster_key,
    spatial_key = spatial_key,
    height=4,
    center_coordinate=False,
    show_legend=True,
    legend_kwargs={'loc': 'upper center', 'bbox_to_anchor': (0.49, 0) ,'ncol': 5, 'borderaxespad': -4, 'frameon': False},
    palette=palette,
)

slice1 = slices[0].copy()
slice2 = slices[1].copy()

slice1, slice2 = crop_slices(
    slice1,
    slice2,
    spatial_key='spatial',
    overlay_ratio = 0.7,
)

spatial_key = 'spatial'
cluster_key = 'Main_molecular_tissue_region'

st.pl.slices_2d(
    slices = [slice1, slice2],
    label_key = cluster_key,
    spatial_key = spatial_key,
    height=4,
    center_coordinate=False,
    show_legend=False,
    legend_kwargs={'loc': 'upper center', 'bbox_to_anchor': (0.49, 0) ,'ncol': 5, 'borderaxespad': -4, 'frameon': False},
    palette=palette,
)

st.pl.overlay_slices_2d(slices = [slice1, slice2], spatial_key = 'spatial', height=4, overlay_type='backward')

rigid_transformation(
    slice2,
    'spatial',
    'spatial',
    inplace=True
)

st.pl.overlay_slices_2d(slices = [slice1, slice2], spatial_key = 'spatial', height=4, overlay_type='backward')

Spateo alignment

adata_pca = ad.concat([slice1,slice2], label='batch')
sc.tl.pca(adata_pca)
slice1.obsm['X_pca'] = adata_pca[adata_pca.obs['batch'] == '0'].obsm['X_pca'].copy()
slice2.obsm['X_pca'] = adata_pca[adata_pca.obs['batch'] == '1'].obsm['X_pca'].copy()

key_added = 'align_spatial'
spatial_key = 'spatial'
# spateo return aligned slices as well as the mapping matrix
aligned_slices, pis = st.align.morpho_align(
    models=[slice1, slice2],
    spatial_key=spatial_key,
    key_added=key_added,
    device=device,
    lambdaVF=1e3,
    beta=1e-3,
    partial_robust_level=50,
    rep_layer=['X_pca'],
    rep_field=['obsm'],
    dissimilarity=['cos'],
)

|-----> [Models alignment based on morpho, mode: SN-S.] in progress: 100.0000%|-----> Filtered all samples for common genes. There are 1022 common genes.
|-----> Spatial coordinates normalization params:
|-----------> Scale: tensor([18108.9258, 18108.9258])...
|-----------> Scale: tensor([[20530.0488, 32617.4902],
        [22320.6562, 47592.1680]], device='cuda:0')...
|-----> Preprocess finished.
|-----> Performing coarse rigid alignment...
|-----> Coarse rigid alignment done.
|-----> [Start Spateo pairwise alignment] in progress: 100.0000%
|-----> [Start Spateo pairwise alignment] finished [11.5115s]
|-----> Key Parameters: gamma: 0.6598348021507263; sigma2: 0.0010000000474974513; probability_parameters: [tensor(0.0100, device='cuda:0')]
|-----> [Models alignment based on morpho, mode: SN-S.] in progress: 100.0000%
|-----> [Models alignment based on morpho, mode: SN-S.] finished [0.5604s]

# Visualize the alignment results
st.pl.overlay_slices_2d(slices = aligned_slices, spatial_key = key_added, height=5, overlay_type='backward')