A multi-modality and multi-granularity collaborative learning framework for identifying spatial domains and spatially variable genes.

ABSTRACT:

Motivation

Recent advances in spatial transcriptomics technologies have provided multi-modality data integrating gene expression, spatial context, and histological images. Accurately identifying spatial domains and spatially variable genes is crucial for understanding tissue structures and biological functions. However, effectively combining multi-modality data to identify spatial domains and determining SVGs closely related to these spatial domains remains a challenge.

Results

In this study, we propose spatial transcriptomics multi-modality and multi-granularity collaborative learning (spaMMCL). For detecting spatial domains, spaMMCL mitigates the adverse effects of modality bias by masking portions of gene expression data, integrates gene and image features using a shared

SUBMITTER: Liang X 

PROVIDER: S-EPMC11513014 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature