Ding Wubin, Goldberg David, Zhou Wanding
Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, PA, 19104, USA.
Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Imeta. 2023 Aug;2(3). doi: 10.1002/imt2.115. Epub 2023 May 25.
Python has emerged as a robust programming language increasingly employed in genomics data analysis, largely due to its comprehensive deep learning libraries and proficiency in handling large-scale data, such as single-cell multi-omics datasets. Although Python has become a prominent data science ecosystem for bioinformatics, there remains a growing demand for advanced heatmap visualization and assembly tools, which are not sufficiently addressed by existing Python-based data visualization libraries. We present PyComplexHeatmap, an all-inclusive Python library for heatmap visualization, inspired by the ComplexHeatmap package currently available in R. PyComplexHeatmap is built upon the matplotlib library and features a versatile, modular interface that seamlessly integrates with other Python-based data science tools, such as Pandas, NumPy, and genomics tools, such as Scanpy, in a standard-compliant manner. This library caters to the requirements of exquisite rendering of multimodal matrix data, incorporating both textual and graphical annotations, thereby enabling efficient integrative analysis of multimodal data and associated metadata.
Python已成为一种强大的编程语言，越来越多地应用于基因组数据分析，这主要归功于其全面的深度学习库以及处理大规模数据（如单细胞多组学数据集）的能力。尽管Python已成为生物信息学领域一个突出的数据科学生态系统，但对先进的热图可视化和组装工具的需求仍在不断增长，而现有的基于Python的数据可视化库并未充分满足这一需求。我们展示了PyComplexHeatmap，这是一个用于热图可视化的综合性Python库，其灵感来自于R语言中当前可用的ComplexHeatmap包。PyComplexHeatmap基于matplotlib库构建，具有通用的模块化接口，能够以符合标准的方式与其他基于Python的数据科学工具（如Pandas、NumPy）以及基因组学工具（如Scanpy）无缝集成。该库满足了对多模态矩阵数据进行精美渲染的要求，结合了文本和图形注释，从而能够对多模态数据及相关元数据进行高效的综合分析。
