Blair Andrew P, Hu Robert K, Farah Elie N, Chi Neil C, Pollard Katherine S, Przytycki Pawel F, Kathiriya Irfan S, Bruneau Benoit G
Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA 94143, USA.
Division of Cardiology, Department of Medicine, University of California, San Diego, CA 92093, USA.
Bioinform Adv. 2022 Aug 4;2(1):vbac051. doi: 10.1093/bioadv/vbac051. eCollection 2022.
MOTIVATION: Unsupervised clustering of single-cell transcriptomics is a powerful method for identifying cell populations. Static visualization techniques for single-cell clustering only display results for a single resolution parameter. Analysts will often evaluate more than one resolution parameter but then only report one. RESULTS: We developed Cell Layers, an interactive Sankey tool for the quantitative investigation of gene expression, co-expression, biological processes and cluster integrity across clustering resolutions. Cell Layers enhances the interpretability of single-cell clustering by linking molecular data and cluster evaluation metrics, providing novel insight into cell populations. AVAILABILITY AND IMPLEMENTATION: https://github.com/apblair/CellLayers.
动机：单细胞转录组学的无监督聚类是识别细胞群体的强大方法。单细胞聚类的静态可视化技术仅显示单个分辨率参数的结果。分析人员通常会评估多个分辨率参数，但随后只报告一个。 结果：我们开发了Cell Layers，这是一种交互式桑基工具，用于定量研究跨聚类分辨率的基因表达、共表达、生物学过程和聚类完整性。Cell Layers通过链接分子数据和聚类评估指标，增强了单细胞聚类的可解释性，为细胞群体提供了新的见解。 可用性和实现方式：https://github.com/apblair/CellLayers 。
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