GigaScienceBioinformaticsPeer-reviewed journal article
SPEX: A modular end-to-end platform for high-plex tissue spatial omics analysis
Xiao LiiD, Ximo Pechuan-JorgeiD, Tyler RisomiD, Conrad FooiD, Alexander Prilipko, Artem Zubkov, Caleb K. ChaniD, Patrick S. ChangiD, Frank Peale, James M. ZiaiiD, Sandra RostiD, Derrek P. HibariD, Lisa M. McGinnisiD, Evgeniy Tabatsky, Xin YeiD, Héctor Corrada BravoiD, Zhen ShiiD, Małgorzata NowickaiD, Jon ScherdiniD, James D. Cowan, Jennifer Margaret GiltnaneiD, Darya Yu. OrlovaiD, Rajiv JesudasoniD
Abstract
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. SPEXs 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 SPEXs ability to facilitate the discovery of biological insights in spatially resolved omics datasets from healthy tissue to tumor samples.
- assay: spatial transcriptomics
- assay: proteomics
- research focus: database / resource
- research focus: software / tool
- approach: experimental assay
- Citations
- 13
- FWCI
- 2.09
- Field percentile
- top 12%
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