Assembling spatial clustering framework for heterogeneous spatial transcriptomics data with GRAPHDeep.

ABSTRACT:

Motivation

Spatial clustering is essential and challenging for spatial transcriptomics' data analysis to unravel tissue microenvironment and biological function. Graph neural networks are promising to address gene expression profiles and spatial location information in spatial transcriptomics to generate latent representations. However, choosing an appropriate graph deep learning module and graph neural network necessitates further exploration and investigation.

Results

In this article, we present GRAPHDeep to assemble a spatial clustering framework for heterogeneous spatial transcriptomics data. Through integrating 2 graph deep learning modules and 20 graph neural networks, the most appropriate combination is decided for each dataset. The constructed spatial clustering meth

SUBMITTER: Liu T 

PROVIDER: S-EPMC10832355 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature