Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.

中文翻译：

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Panpipes：多组学单细胞和空间转录组数据分析的管道


表观基因组、转录组和蛋白质组的单细胞多组学分析可以全面表征支撑细胞身份和状态的分子电路。然而，由于缺乏对不同模式进行系统、联合评估的方法，对此类数据集的整体解释提出了挑战。在这里，我们展示了 Panpipes，这是一组计算工作流程，旨在通过结合广泛使用的基于 Python 的工具来自动化多模式单细胞和空间转录组分析，以大规模执行质量控制、预处理、集成、聚类和参考映射。 Panpipes 允许对单个和集成模式进行可靠且可定制的分析和评估，从而在下游调查之前增强决策能力。