Supporting data for "Spacemake: processing and analysis of large-scale spatial transcriptomics data"

Spatial sequencing methods increasingly gain popularity within RNA biology studies. State-of-the-art techniques can read mRNA expression levels from tissue sections and at the same time register information about the original locations of the molecules in the tissue. The resulting datasets are processed and analyzed by accompanying software which, however, is incompatible across inputs from different technologies. Here, we present spacemake, a modular, robust and scalable spatial transcriptomics pipeline built in snakemake and python. Spacemake is designed to handle all major spatial transcriptomics datasets and can be readily configured to run on other technologies. It can process and analyze several samples in parallel, even if they stem from different experimental methods. Spacemakes unified framework enables reproducible data processing from raw sequencing data to automatically generated downstream analysis reports. Moreover, spacemake is built with a modular design and offers additional functionality such as sample merging, saturation analysis and analysis of long-reads as separate modules. Moreover, spacemake employs novoSpaRc to integrate spatial and single-cell transcriptomics data, resulting in increased gene counts for the spatial dataset. Spacemake is open-source, extendable and can be readily integrated with existing computational workflows.

空间测序方法在RNA生物学研究中的应用日益广泛。最先进的技术能够从组织切片中读取mRNA表达水平，同时记录分子在组织中的原始位置信息。生成的数据集由配套软件进行处理和分析，但这些软件在处理不同技术来源的输入时互不兼容。在此，我们提出spacemake——一个基于snakemake和Python构建的模块化、稳健且可扩展的空间转录组学流程。Spacemake旨在处理所有主流空间转录组学数据集，且可轻松配置以适用于其他技术。它能够并行处理和分析多个样本，即使这些样本来自不同的实验方法。Spacemake的统一框架支持从原始测序数据到自动生成下游分析报告的可重复数据处理。此外，Spacemake采用模块化设计，提供样本合并、饱和度分析和长读长（long-reads）分析等附加功能，这些功能以独立模块形式存在。此外，Spacemake采用novoSpaRc整合空间和单细胞转录组学数据，从而提高空间数据集的基因计数。Spacemake是开源的、可扩展的，且可轻松与现有计算工作流程集成。