Powerful and accurate case-control analysis of spatial molecular data.

ABSTRACT: As spatial molecular data grow in scope and resolution, there is a pressing need to identify key spatial structures associated with disease. Current approaches typically make restrictive assumptions such as representing tissue regions by local abundances of manually typed, discrete cell types, or representing samples in terms of abundances of manually called, discrete spatial structures; this risks overlooking important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method that combines deep learning with principled statistics to discover associated spatial features with greater flexibility and precision. VIMA trains an ensemble of variational autoencoders to extract numerical "fingerprints" from small tissue patches that capture their biological conte

SUBMITTER: Reshef Y 

PROVIDER: S-EPMC11839118 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature