Tutorial 4: Integration of heterogeneous-structure datasets

In this tutorial, we utilized three mouse hippocampal slices from Slide-seq to assess the performance of spatiAlign in integrating heterogeneous-structure datasets with distinct biological characteristics.

• Import packages

import scanpy as sc
from spatialign import Spatialign
from warnings import filterwarnings
from anndata import AnnData

filterwarnings("ignore")

datasets = [
    "/media/Data/zhangchao/hipp1.h5ad",
    "/media/Data/zhangchao/hipp2.h5ad",
    "/media/Data/zhangchao/hipp3.h5ad"
]

• Initialize

  Read datasets and load spatiAlign model

model = Spatialign(
    *datasets,
    batch_key='batch',
    is_norm_log=True,
    is_scale=False,
    n_neigh=15,
    is_undirected=True,
    latent_dims=100,
    seed=42,
    gpu=0,
    save_path="./",
    is_verbose=False
)

• Training spatiAlign model

  spatiAlign aims to aligns the biological effects, while maximized preserving spots/cells biological variances in the latent embedding. It is possible to fine-tune the parameters (tau1, tau2, tau3) to optimize the model’s performance.

model.train()

• Inference alignment datasets

  After model training, the learned lower-dimensional representations will be saved in adata.obsm[‘correct’], and the higher-dimensional representations will be saved in adata.X.

model.alignment()

• Visualizing inference datasets

correct1 = sc.read_h5ad("./correct0.h5ad")
correct2 = sc.read_h5ad("./correct1.h5ad")
correct3 = sc.read_h5ad("./correct2.h5ad")

merge_data = correct1.concatenate(correct2, correct3)

sc.pp.neighbors(merge_data, use_rep="correct", random_state=42)
sc.tl.umap(merge_data, random_state=42)

sc.pl.umap(merge_data, color=["batch", "hclust_16"])

sc.pl.spatial(correct1, spot_size=40, color="hclust_16")

sc.pl.spatial(correct2, spot_size=30, color="hclust_16")

sc.pl.spatial(correct3, spot_size=10, color="hclust_16")