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

The basic analysis steps of spatial transcriptomics involves 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. The excutation time for the entire analysis process is ~148 minutes on 1G reads 1*1 cm chip test data, 1.8 times faster than unoptimized workflow.

空间转录组学（spatial transcriptomics）的基础分析步骤，旨在从空间维度与细胞层面获取基因表达信息。该流程需依托一系列工具方可完成，而现有工具在处理大规模数据集时存在性能瓶颈，具体包括空间定位计算量庞大、RNA基因组比对耗时过长，以及在大芯片场景下内存占用过高。上述问题会制约该流程的适用性与运行效率。为解决这些问题，针对华大基因（BGI）开发的Stereo-Seq技术，科研人员研发出一款兼具高性能与高精度的空间转录组数据分析流程——Stereo-Seq分析工作流（Stereo-Seq Analysis Workflow，SAW）。该工作流涵盖mRNA空间位置重构、基因组比对、基因表达矩阵生成与聚类分析，并可生成通用格式的结果文件，以供后续个性化分析使用。在1G读长、1*1 cm芯片的测试数据上，整个分析流程的执行时长约为148分钟，较未优化的工作流提速1.8倍。