Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany.
Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Genome Biol. 2024 Jul 8;25(1):181. doi: 10.1186/s13059-024-03322-7.
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.
单细胞多组学分析表观基因组、转录组和蛋白质组，可以全面描述构成细胞身份和状态的分子电路。然而，由于缺乏系统的、联合评估不同模态的方法，因此此类数据集的整体解释仍然具有挑战性。在这里，我们提出了 Panpipes，这是一组计算工作流程，旨在通过整合广泛使用的基于 Python 的工具来自动执行多模态单细胞和空间转录组分析，从而实现大规模的质量控制、预处理、集成、聚类和参考映射。Panpipes 允许对单个和集成模态进行可靠和可定制的分析和评估，从而在下游研究之前为决策提供支持。
