SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics.

ABSTRACT: The basic analysis steps of spatial transcriptomics require obtaining gene expression information from both space and cells. The existing tools for these analyses incur performance issues when dealing with large datasets. These issues involve computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the analysis. Here, a high-performance and accurate spatial transcriptomics data analysis workflow, called Stereo-seq Analysis Workflow (SAW), was developed for the Stereo-seq technology developed at BGI. SAW includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation, and clustering. The workflow outputs files in a universal format for

SUBMITTER: Gong C 

PROVIDER: S-EPMC10905255 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature