omicverse.space.pySpaceFlow

omicverse.space.pySpaceFlow(adata) → None

SpaceFlow spatial flow analysis class.

SpaceFlow is a deep learning method for analyzing spatial transcriptomics data by learning spatially-aware cell representations. It combines graph neural networks with spatial regularization to capture both transcriptional and spatial relationships between cells.

The method: 1. Constructs a spatial neighborhood graph 2. Learns embeddings using deep graph infomax 3. Applies spatial regularization to preserve spatial structure 4. Generates pseudo-spatial maps for trajectory analysis

Parameters:

• adata (AnnData) – Spatial AnnData containing expression and coordinates in adata.obsm['spatial'].

• Attributes –

  adata: AnnData

  Input annotated data matrix containing: - Gene expression data in adata.X - Spatial coordinates in adata.obsm[‘spatial’]

  sf: SpaceFlow

  Internal SpaceFlow object for computations

  embedding: array

  Learned spatial-aware embeddings after training

• Examples –

  >>> import scanpy as sc
  >>> import omicverse as ov
  >>> # Load spatial data
  >>> adata = sc.read_visium(...)
  >>> # Initialize SpaceFlow
  >>> spaceflow = ov.space.pySpaceFlow(adata)
  >>> # Train model
  >>> embedding = spaceflow.train(
  ...     spatial_regularization_strength=0.1,
  ...     z_dim=50,
  ...     epochs=1000
  ... )
  >>> # Calculate pseudo-spatial map
  >>> psm = spaceflow.cal_pSM(n_neighbors=20)