SKNY - Spatial omics analysis tools for tumor microenvironment

SKNY (SpatialKNifeY) is a tools for spatial omics analysis of tumor microenvironment for multiple platform such as Xenium, CosMx, and PhenoCycler. SKNY automatically contours the tumor based on the expression data and calculates the distance from these contours to each coordinate in space. Using this distance data, we provide two analyses:

1. A quantitative evaluation of the peri- and intra-tumoral gene expressions.

2. “Single tumor microenvironment analysis” with bulk gene expression within each contour.

Since v0.3.0 SKNY also provides a cell-label based variant (skny.pp.calculate_region) for cell-level data with annotated cell types (e.g. scanpy / Xenium 5K): the tumor is defined by a cell label such as "Malignant" and every cell is assigned to the tumor core (within 15 um of a tumor cell), the peri-tumoral margin (stroma within 100 um of the core) or dist (farther tissue), with an all-cell tissue mask so that empty space is never counted. skny.pp.region_density then gives the density (cells / mm^2) of any cell group (e.g. immune subtypes) per region, and skny.pl.region_map / skny.pl.region_density_plot draw the results.

import skny as sk
sk.pp.calculate_region(adata, "Malignant", label_key="cell_type", sample_key="sample")  # coordinates in um in adata.obsm["spatial"]
df = sk.pp.region_density(adata, "immune_subtype", sample_key="sample")
sk.pl.region_map(adata, sample="S1", highlight=["T cell", "Myeloid"])
sk.pl.region_density_plot(df, groups=["CD8+ T", "Treg", "M2-like"])

Also, anndata object-based programming makes it compatible with scanpy, squidpy and stlearn.

Recommended Python version: We recommend using Python 3.8 or 3.9 to ensure compatibility.