SPAC: A Python Package for Spatial Single-Cell Analysis of Multiplex Imaging
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910980321050624 |
|---|---|
| author | Liu, Fang He, Rui Bombin, Andrei Abdallah, Ahmad B. Eldaghar, Omar Sheeley, Tommy R. Ying, Sam E. Zaki, George |
| author_facet | Liu, Fang He, Rui Bombin, Andrei Abdallah, Ahmad B. Eldaghar, Omar Sheeley, Tommy R. Ying, Sam E. Zaki, George |
| contents | Multiplexed immunofluorescence microscopy captures detailed measurements of spatially resolved, multiple biomarkers simultaneously, revealing tissue composition and cellular interactions in situ among single cells. The growing scale and dimensional complexity of these datasets demand reproducible, comprehensive and user-friendly computational tools. To address this need, we developed SPAC (SPAtial single-Cell analysis), a Python-based package and a corresponding shiny application within an integrated, modular SPAC ecosystem (Liu et al., 2025) designed specifically for biologists without extensive coding expertise. Following image segmentation and extraction of spatially resolved single-cell data, SPAC streamlines downstream phenotyping and spatial analysis, facilitating characterization of cellular heterogeneity and spatial organization within tissues. Through scalable performance, specialized spatial statistics, highly customizable visualizations, and seamless workflows from dataset to insights, SPAC significantly lowers barriers to sophisticated spatial analyses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01560 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SPAC: A Python Package for Spatial Single-Cell Analysis of Multiplex Imaging Liu, Fang He, Rui Bombin, Andrei Abdallah, Ahmad B. Eldaghar, Omar Sheeley, Tommy R. Ying, Sam E. Zaki, George Software Engineering Genomics 62P10 J.3; I.5.4 Multiplexed immunofluorescence microscopy captures detailed measurements of spatially resolved, multiple biomarkers simultaneously, revealing tissue composition and cellular interactions in situ among single cells. The growing scale and dimensional complexity of these datasets demand reproducible, comprehensive and user-friendly computational tools. To address this need, we developed SPAC (SPAtial single-Cell analysis), a Python-based package and a corresponding shiny application within an integrated, modular SPAC ecosystem (Liu et al., 2025) designed specifically for biologists without extensive coding expertise. Following image segmentation and extraction of spatially resolved single-cell data, SPAC streamlines downstream phenotyping and spatial analysis, facilitating characterization of cellular heterogeneity and spatial organization within tissues. Through scalable performance, specialized spatial statistics, highly customizable visualizations, and seamless workflows from dataset to insights, SPAC significantly lowers barriers to sophisticated spatial analyses. |
| title | SPAC: A Python Package for Spatial Single-Cell Analysis of Multiplex Imaging |
| topic | Software Engineering Genomics 62P10 J.3; I.5.4 |
| url | https://arxiv.org/abs/2506.01560 |