SSAM-lite : A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data
Copyright © 2022 Tiesmeyer, Sahay, Müller-Bötticher, Eils, Mackowiak and Ishaque..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
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| Enthalten in: |
Frontiers in genetics - 13(2022) vom: 11., Seite 785877 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tiesmeyer, Sebastian [VerfasserIn] |
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| Links: |
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| Themen: |
Cell typing |
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| Anmerkungen: |
Date Revised 19.03.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fgene.2022.785877 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2022 Tiesmeyer, Sahay, Müller-Bötticher, Eils, Mackowiak and Ishaque. | ||
| 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a cell typing | |
| 650 | 4 | |a in situ hybridization | |
| 650 | 4 | |a in situ sequencing | |
| 650 | 4 | |a spatial transcriptomics | |
| 650 | 4 | |a spatially resolved transcriptomics | |
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| 700 | 1 | |a Sahay, Shashwat |e verfasserin |4 aut | |
| 700 | 1 | |a Müller-Bötticher, Niklas |e verfasserin |4 aut | |
| 700 | 1 | |a Eils, Roland |e verfasserin |4 aut | |
| 700 | 1 | |a Mackowiak, Sebastian D |e verfasserin |4 aut | |
| 700 | 1 | |a Ishaque, Naveed |e verfasserin |4 aut | |
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