SSAM-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data
The combination of a cell’s transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A partic...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
28 February 2022
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| In: |
Frontiers in genetics
Year: 2022, Volume: 13, Pages: 1-7 |
| ISSN: | 1664-8021 |
| DOI: | 10.3389/fgene.2022.785877 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3389/fgene.2022.785877 Verlag, lizenzpflichtig, Volltext: https://www.frontiersin.org/article/10.3389/fgene.2022.785877 
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| Author Notes: | Sebastian Tiesmeyer, Shashwat Sahay, Niklas Müller-Bötticher, Roland Eils, Sebastian D. Mackowiak and Naveed Ishaque |
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| 520 | |a The combination of a cell’s transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data. | ||
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| 700 | 1 | |a Ishaque, Naveed |e VerfasserIn |4 aut | |
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