Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.13974 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913699442196480 |
|---|---|
| author | Chelebian, Eduard Dasgupta, Pratiti Samadi, Zainalabedin Wählby, Carolina Askary, Amjad |
| author_facet | Chelebian, Eduard Dasgupta, Pratiti Samadi, Zainalabedin Wählby, Carolina Askary, Amjad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13974 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images Chelebian, Eduard Dasgupta, Pratiti Samadi, Zainalabedin Wählby, Carolina Askary, Amjad Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.13974 |