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

• DOI: 10.46471/gigabyte.111
• Publication Date: 2024-02-20T02:52:52Z
• Authors: Chun Gong; Shengkang Li; Leying Wang; Fuxiang Zhao; Shuangsang Fang; Dong Yuan; Zijian Zhao; Qiqi He; Mei Li; Weiqing Liu; Zhaoxun Li; Hongqing Xie; Sha Liao; Ao Chen; Yong Zhang; Yuxiang Li; Xun Xu
• Keywords: Technical Release; Electronic computers. Computer science; 0206 medical engineering; QA75.5-76.95; 02 engineering and technology

The basic analysis steps of spatial transcriptomics require obtaining gene expression information from both space and cells. existing tools for these analyses incur performance issues when dealing with large datasets. These involve computationally intensive localization, RNA genome alignment, excessive memory usage in chip scenarios. problems affect the applicability efficiency analysis. Here, a high-performance accurate data workflow, called Stereo-seq Analysis Workflow (SAW), was developed technology at BGI. SAW includes mRNA position reconstruction, matrix generation, clustering. workflow outputs files universal format subsequent personalized execution time entire is ∼148 min 1 GB reads × cm test data, 1.8 times faster than an unoptimized workflow.