Drukier Institute for Children's Health and Department of Pediatrics, Weill Cornell Medicine, New York, NY 10021, United States.
Bioinformatics. 2024 Jun 3;40(6). doi: 10.1093/bioinformatics/btae366.
Subcluster analysis is a powerful means to improve clustering and characterization of single cell RNA-Seq data. However, there are no existing tools to systematically integrate results from multiple subclusters, which creates hurdles for accurate data quantification, visualization, and interpretation in downstream analysis. To address this issue, we developed Ragas, an R package that integrates multi-level subclustering objects for streamlined analysis and visualization. A new data structure was implemented to seamlessly connect and assemble miscellaneous single cell analyses from different levels of subclustering, along with several new or enhanced visualization functions. Moreover, a re-projection algorithm was developed to integrate nearest-neighbor graphs from multiple subclusters in order to maximize their separability on the combined cell embeddings, which significantly improved the presentation of rare and homogeneous subpopulations.
The Ragas package and its documentation can be accessed through https://github.com/jig4003/Ragas and its source code is also available at https://zenodo.org/records/11244921.
子聚类分析是一种强大的方法，可以改进单细胞 RNA-Seq 数据的聚类和特征描述。然而，目前还没有工具可以系统地整合来自多个子聚类的结果，这给下游分析中的准确数据量化、可视化和解释带来了障碍。为了解决这个问题，我们开发了 Ragas，这是一个 R 包，它可以整合多层次的子聚类对象，以便进行简化的分析和可视化。我们实现了一种新的数据结构，可以无缝连接和组装来自不同层次子聚类的各种单细胞分析，同时还提供了几个新的或增强的可视化功能。此外，我们还开发了一种重新投影算法，用于整合来自多个子聚类的最近邻图，以便在组合的细胞嵌入中最大化它们的可分离性，这显著改善了稀有和同质亚群的呈现效果。
Ragas 包及其文档可通过 https://github.com/jig4003/Ragas 访问，其源代码也可在 https://zenodo.org/records/11244921 上获取。
