Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP.

ABSTRACT: Spatial transcriptomics produces high-dimensional gene expression measurements with spatial context. Obtaining a biologically meaningful low-dimensional representation of such data is crucial for effective interpretation and downstream analysis. Here, we present Spatial Transcriptomics Analysis with topic Modeling to uncover spatial Patterns (STAMP), an interpretable spatially aware dimension reduction method built on a deep generative model that returns biologically relevant, low-dimensional spatial topics and associated gene modules. STAMP can analyze data ranging from a single section to multiple sections and from different technologies to time-series data, returning topics matching known biological domains and associated gene modules containing established markers highly ranked within.

SUBMITTER: Zhong C 

PROVIDER: S-EPMC11541207 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature