SpatialCompassV

Spatial omics analysis tools for cell/gene clustering from a astandard region

• PyPI package: https://pypi.org/project/scomv/
• Free software: MIT License
• Documentation: https://spatialcompassv.readthedocs.io

Overview of the SpatialCompassV (SCOMV) Workflow

The overall workflow of SpatialCompassV (SCOMV) is summarized as follows:

• Extraction of a reference region
  A reference region (e.g., a tumor region) is identified using the SpatialKnifeY (SKNY) algorithm.

Vector construction from spatial grids

The AnnData object is discretized into spatial grids, and for each grid, the shortest-distance vector to the reference region is computed.

This vector information is stored for each cell/gene and projected onto a polar coordinate map. The horizontal axis represents distance, and the vertical axis also represents distance. Distances are defined as negative for locations inside the reference region.

A similarity matrix is then constructed, followed by PCoA and clustering, to classify spatial distribution patterns.

• Integration across multiple fields of view
  By integrating results from multiple regions of interest, clustering of the reference region itself (e.g., tumor malignancy states) can be performed.
  • Gene-wise contributions are calculated using PCA, enabling the identification of spatially differentially expressed genes (Spatial DEGs).

Additional functionality

• Gene distributions can also be visualized as 3D density maps, allowing direct comparison of the spatial distributions of two genes.

Credits

This package was created with Cookiecutter and the audreyfeldroy/cookiecutter-pypackage project template.

79d3344 (Initial commit (cookiecutter-scientific-python))