Mapping the topography of spatial gene expression with interpretable deep learning.

ABSTRACT: Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of these data complicates analysis of spatial gene expression patterns. We address this issue by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a quantity called the isodepth. Contours of constant isodepths enclose domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in expression. We develop GASTON (gradient analysis of spatial transcriptomics organization with neural networks), an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gradients and piecewise linear expression functio

SUBMITTER: Chitra U 

PROVIDER: S-EPMC12370209 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature