Sainsc: A Computational Tool for Segmentation‐Free Analysis of In Situ Capture Data (Small Methods 5/2025)
Segmentation-Free Analyses Sainsc: a new tool for efficient whole organism spatial transcriptomics data analysis at the nanometre scale. Shown is a blended composite spatial map of total gene expression (top), cell types (middle) and assignment confidence (bottom) of a mouse embryo Stereo-seq dataset from Chen et al. 2022. More in article number 2401123 by Naveed Ishaque and co-workers.
Authors
- Roland Eils (ORCID: https://orcid.org/0000-0002-0034-4036)
- Naveed Ishaque (ORCID: https://orcid.org/0000-0002-8426-901X)
- Sebastian Tiesmeyer (ORCID: https://orcid.org/0000-0002-0492-1523)
- Niklas Müller‐Bötticher (ORCID: https://orcid.org/0000-0001-5103-7282)
Institutions
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Freie Universität Berlin (DE)
Publication Details
- Journal
- Small Methods
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1002/smtd.202570036
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00