ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning.

ABSTRACT: Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoidance of unpredictable tissue disentanglement steps. Although high-quality cell segmentation algorithms can aid in the extraction of valuable data, traditional methods are frequently non-spatial, do not account for spatial information efficiently, and perform poorly when confronted with the problem of spatial transcriptome cell segmentation with varying shapes. In this study, we propose ST-CellSeg, an image-based machine learning method for spatial transcriptomics that uses manifold for cell segmentation and is novel in its consideration of mult

SUBMITTER: Li Y 

PROVIDER: S-EPMC11236102 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature