MOSAIK: Multi-Origin Spatial Transcriptomics Analysis and Integration Kit
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912379998044160 |
|---|---|
| author | Baptista, Anthony Nuamah, Rosamond Chiappini, Ciro Grigoriadis, Anita |
| author_facet | Baptista, Anthony Nuamah, Rosamond Chiappini, Ciro Grigoriadis, Anita |
| contents | Spatial transcriptomics (ST) has revolutionised transcriptomics analysis by preserving tissue architecture, allowing researchers to study gene expression in its native spatial context. However, despite its potential, ST still faces significant technical challenges. Two major issues include: (1) the integration of raw data into coherent and reproducible analysis workflows, and (2) the accurate assignment of transcripts to individual cells. To address these challenges, we present MOSAIK, the first fully integrated, end-to-end workflow that supports raw data from both NanoString CosMx Spatial Molecular Imager (CosMx) and 10x Genomics Xenium In Situ (Xenium). MOSAIK (Multi-Origin Spatial Transcriptomics Analysis and Integration Kit) unifies transcriptomics and imaging data into a single Python object based on the spatialdata format. This unified structure ensures compatibility with a broad range of Python tools, enabling robust quality control and downstream analyses. With MOSAIK, users can perform advanced analyses such as re-segmentation (to more accurately assign transcripts to individual cells), cell typing, tissue domain identification, and cell-cell communication within a seamless and reproducible Python environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11384 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MOSAIK: Multi-Origin Spatial Transcriptomics Analysis and Integration Kit Baptista, Anthony Nuamah, Rosamond Chiappini, Ciro Grigoriadis, Anita Quantitative Methods Spatial transcriptomics (ST) has revolutionised transcriptomics analysis by preserving tissue architecture, allowing researchers to study gene expression in its native spatial context. However, despite its potential, ST still faces significant technical challenges. Two major issues include: (1) the integration of raw data into coherent and reproducible analysis workflows, and (2) the accurate assignment of transcripts to individual cells. To address these challenges, we present MOSAIK, the first fully integrated, end-to-end workflow that supports raw data from both NanoString CosMx Spatial Molecular Imager (CosMx) and 10x Genomics Xenium In Situ (Xenium). MOSAIK (Multi-Origin Spatial Transcriptomics Analysis and Integration Kit) unifies transcriptomics and imaging data into a single Python object based on the spatialdata format. This unified structure ensures compatibility with a broad range of Python tools, enabling robust quality control and downstream analyses. With MOSAIK, users can perform advanced analyses such as re-segmentation (to more accurately assign transcripts to individual cells), cell typing, tissue domain identification, and cell-cell communication within a seamless and reproducible Python environment. |
| title | MOSAIK: Multi-Origin Spatial Transcriptomics Analysis and Integration Kit |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2505.11384 |