Supporting data for "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 generates results files in a universal format for subsequent personalized analysis. The execution 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.

空间转录组学的基础分析流程，旨在获取空间维度与细胞层面的基因表达信息。该流程需依托一系列工具完成，但现有工具在处理大规模数据集时存在性能瓶颈：包括计算密集型的空间定位、RNA基因组比对，以及大芯片场景下的内存占用过高问题。上述问题会制约该流程的适用性与运行效率。为解决此类痛点，针对华大基因（BGI）研发的Stereo-Seq技术，研究人员开发出一款高性能且精准的空间转录组数据分析流程——Stereo-Seq分析流程（Stereo-Seq Analysis Workflow，SAW）。该流程涵盖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环节，并生成通用格式的结果文件，以供后续个性化分析使用。在搭载1G条读段的1×1 cm芯片测试数据集上，整套分析流程的运行时长约为148分钟，较未优化的流程提速1.8倍。