SPEX: A modular end-to-end platform for high-plex tissue spatial omics analysis Article Swipe
Xiaoli Li
,
Ximo Pechuan-Jorge
,
Tyler Risom
,
Conrad Foo
,
A. Ya. Prilipko
,
Artem Zubkov
,
Caleb Chan
,
Patrick Chang
,
Frank Peale
,
James Ziai
,
Sandra Rost
,
Derrek P. Hibar
,
Lisa McGinnis
,
Evgeniy Tabatsky
,
Xin Ye
,
Héctor Corrada Bravo
,
Zhen Shi
,
Małgorzata Nowicka
,
Jon Scherdin
,
James D. Cowan
,
Jennifer M. Giltnane
,
Darya Orlova
,
Rajiv Jesudason
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.08.22.504841
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.08.22.504841
Recent advancements in transcriptomics and proteomics have opened the possibility for spatially resolved molecular characterization of tissue architecture with the promise of enabling a deeper understanding of tissue biology in either homeostasis or disease. The wealth of data generated by these technologies has recently driven the development of a wide range of computational methods. These methods have the requirement of advanced coding fluency to be applied and integrated across the full spatial omics analysis process thus presenting a hurdle for widespread adoption by the biology research community. To address this, we introduce SPEX (Spatial Expression Explorer), a web-based analysis platform that employs modular analysis pipeline design, accessible through a user-friendly interface. SPEX’s infrastructure allows for streamlined access to open source image data management systems,analysis modules, and fully integrated data visualization solutions. Analysis modules include essential steps covering image processing, single-cell and spatial analysis. We demonstrate SPEX’s ability to facilitate the discovery of biological insights in spatially resolved omics datasets from healthy tissue to tumor samples.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.08.22.504841
- https://www.biorxiv.org/content/biorxiv/early/2022/08/23/2022.08.22.504841.full.pdf
- OA Status
- green
- Cited By
- 10
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4292767359
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4292767359Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2022.08.22.504841Digital Object Identifier
- Title
-
SPEX: A modular end-to-end platform for high-plex tissue spatial omics analysisWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-23Full publication date if available
- Authors
-
Xiaoli Li, Ximo Pechuan-Jorge, Tyler Risom, Conrad Foo, A. Ya. Prilipko, Artem Zubkov, Caleb Chan, Patrick Chang, Frank Peale, James Ziai, Sandra Rost, Derrek P. Hibar, Lisa McGinnis, Evgeniy Tabatsky, Xin Ye, Héctor Corrada Bravo, Zhen Shi, Małgorzata Nowicka, Jon Scherdin, James D. Cowan, Jennifer M. Giltnane, Darya Orlova, Rajiv JesudasonList of authors in order
- Landing page
-
https://doi.org/10.1101/2022.08.22.504841Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/08/23/2022.08.22.504841.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/08/23/2022.08.22.504841.full.pdfDirect OA link when available
- Concepts
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Modular design, Graphical user interface, Omics, Proteomics, Computer science, Analytics, Software, Interface (matter), Data science, Computational biology, Biology, Bioinformatics, Operating system, Bubble, Maximum bubble pressure method, Parallel computing, Biochemistry, Gene, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
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2025: 3, 2024: 2, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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