SPICEiST: subcellular RNA pattern enhances cell clustering of imaging-based spatial transcriptomics.

ABSTRACT:

Background

Imaging-based spatial transcriptomics (ST) enables the quantification of gene expression at single-cell resolution while preserving spatial context, but its utility is limited by small gene panels and challenges in accurate cell segmentation. To address these limitations, we present a graph autoencoder framework that integrates subcellular transcript distribution patterns with cell-level gene expression profiles for enhanced cell clustering in imaging-based ST (SPICEiST).

Results

The clustering performance of SPICEiST was systematically evaluated across several cancer datasets and gene panel sizes. The results demonstrate that SPICEiST consistently outperforms the conventional cell-level gene expression-based methods in distinguishing subtle differences in cell st

SUBMITTER: Bae S 

PROVIDER: S-EPMC12670746 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature