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SpatialOne: end-to-end analysis of visium data at scale.
Mena Kamel1, Amrut Sarangi1, Pavel Senin1
1Digital R&D, Sanofi, Paris 75017, France.
Bioinformatics (Oxford, England)
|August 17, 2024
Summary
SpatialOne simplifies spatial transcriptomics analysis by integrating multiple computational methods for 10x Visium data. This pipeline enables scalable and reproducible quantification of spatial gene expression.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Spatial transcriptomics enables mRNA expression quantification within a spatial context.
- Analyzing spatial transcriptomics data is complex and difficult to scale due to numerous required methods and libraries.
Purpose of the Study:
- To present SpatialOne, an end-to-end pipeline for simplifying the analysis of 10x Visium spatial transcriptomics data.
- To streamline the segmentation, deconvolution, and quantification of spatial information.
- To enable reproducible spatial data analysis at scale.
Main Methods:
- SpatialOne integrates multiple state-of-the-art computational methods.
- The pipeline is designed as an end-to-end solution.
- It utilizes a docker container image for distribution.
Main Results:
- SpatialOne simplifies the analysis of 10x Visium data.
- The pipeline effectively segments, deconvolves, and quantifies spatial information.
- It facilitates reproducible spatial data analysis at scale.
Conclusions:
- SpatialOne offers a streamlined approach to spatial transcriptomics data analysis.
- The pipeline enhances the scalability and reproducibility of spatial data insights.
- It provides a unified solution for complex spatial omics analyses.
