an efficient analysis pipeline on stereo-seq transcriptome data
The basic analysis steps of spatial transcriptomics involve obtaining gene expression information from both space and cells. This process requires a set of tools to be completed, and existing tools face performance issues when dealing with large data sets. These issues include computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the process. To address these issues, a high-performance and accurate spatial transcriptomics data analysis workflow called Stereo-Seq Analysis Workflow (SAW) has been developed for the Stereo-Seq technology developed by BGI. This workflow includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation and clustering, and generate results files in a universal format for subsequent personalized analysis.