PDF(Pubmed)
Abstract：
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.
摘要：
表观基因组的单细胞多体分析，转录组，和蛋白质组允许对支撑细胞身份和状态的分子电路进行全面表征。然而,鉴于缺乏系统的方法，对此类数据集的整体解释提出了挑战，不同模式的联合评价。这里,我们介绍Panpipes，一组计算工作流程，旨在通过整合广泛使用的基于Python的工具来执行质量控制，从而自动进行多模式单细胞和空间转录组学分析。预处理,一体化,聚类，和比例尺的参考绘图。Panpipes允许对个人和集成模式进行可靠和可定制的分析和评估，从而在下游调查之前授权决策。
