Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images
Journal:
arXiv
Published Date:
Feb 8, 2025
Abstract
This study introduces SEFI (SEgmentation-Free Integration), a novel method
for integrating morphological features of cell nuclei with spatial
transcriptomics data. Cell segmentation poses a significant challenge in the
analysis of spatial transcriptomics data, as tissue-specific structural
complexities and densely packed cells in certain regions make it difficult to
develop a universal approach. SEFI addresses this by utilizing self-supervised
learning to extract morphological features from fluorescent nuclear staining
images, enhancing the clustering of gene expression data without requiring
segmentation. We demonstrate SEFI on spatially resolved gene expression
profiles of the developing retina, acquired using multiplexed single molecule
Fluorescence In Situ Hybridization (smFISH). SEFI is publicly available at
https://github.com/eduardchelebian/sefi.
