Xi Jingyue, Lee Jun Hee, Kang Hyun Min, Jun Goo
Department of Biostatistics, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Department of Molecular & Integrative Physiology, University of Michigan Medical School, 109 Zina Pitcher Place, Ann Arbor, MI 48109.
Bioinform Adv. 2022;2(1). doi: 10.1093/bioadv/vbac061. Epub 2022 Sep 1.
While there are many software pipelines for analyzing spatial transcriptomics data, few can process ultra high-resolution datasets generated by emerging technologies. There is a clear need for new software tools that can handle sub-micrometer resolution spatial transcriptomics data with computational scalability without compromising its resolution.
We developed STtools, a software pipeline that provides a versatile framework to handle spatial transcriptomics datasets with various resolutions, such as the ones produced by Seq-Scope (<1μm), Slide-seq (10μm) and VISIUM (100μm). It automatically processes raw FASTQ files and runs downstream analyses at several folds higher resolution than existing methods. It also generates various visualizations including transcriptome density, cell type mapping, marker gene highlighting, and subcellular architectures.
STtools is publically available for download at https://github.com/seqscope/STtools.
虽然有许多用于分析空间转录组学数据的软件流程，但很少有能够处理由新兴技术生成的超高分辨率数据集的。显然需要新的软件工具，能够在不影响分辨率的情况下，以可扩展的计算能力处理亚微米分辨率的空间转录组学数据。
我们开发了STtools，这是一个软件流程，提供了一个通用框架来处理各种分辨率的空间转录组学数据集，例如由Seq-Scope（<1μm）、Slide-seq（10μm）和VISIUM（100μm）产生的数据集。它自动处理原始FASTQ文件，并以比现有方法高几倍的分辨率运行下游分析。它还生成各种可视化结果，包括转录组密度、细胞类型映射、标记基因突出显示和亚细胞结构。
