Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, 45267, USA.
Nat Commun. 2023 Jan 25;14(1):406. doi: 10.1038/s41467-023-36016-y.
Decisively delineating cell identities from uni- and multimodal single-cell datasets is complicated by diverse modalities, clustering methods, and reference atlases. We describe scTriangulate, a computational framework to mix-and-match multiple clustering results, modalities, associated algorithms, and resolutions to achieve an optimal solution. Rather than ensemble approaches which select the "consensus", scTriangulate picks the most stable solution through coalitional iteration. When evaluated on diverse multimodal technologies, scTriangulate outperforms alternative approaches to identify high-confidence cell-populations and modality-specific subtypes. Unlike existing integration strategies that rely on modality-specific joint embedding or geometric graphs, scTriangulate makes no assumption about the distributions of raw underlying values. As a result, this approach can solve unprecedented integration challenges, including the ability to automate reference cell-atlas construction, resolve clonal architecture within molecularly defined cell-populations and subdivide clusters to discover splicing-defined disease subtypes. scTriangulate is a flexible strategy for unified integration of single-cell or multimodal clustering solutions, from nearly unlimited sources.
从单模态和多模态单细胞数据集果断地区分细胞身份，这受到多种模态、聚类方法和参考图谱的影响。我们描述了 scTriangulate，这是一个计算框架，可以混合和匹配多个聚类结果、模态、相关算法和分辨率，以实现最佳解决方案。scTriangulate 不是像集成方法那样选择“共识”，而是通过联合迭代选择最稳定的解决方案。在评估各种多模态技术时，scTriangulate 优于其他方法，可以识别高置信度的细胞群体和特定模态的亚型。与依赖于模态特定联合嵌入或几何图形的现有集成策略不同，scTriangulate 不对原始基础值的分布做出任何假设。因此，这种方法可以解决前所未有的集成挑战，包括能够自动构建参考细胞图谱、解析分子定义的细胞群体中的克隆结构以及细分聚类以发现剪接定义的疾病亚型。scTriangulate 是一种灵活的策略，可用于统一整合单细胞或多模态聚类解决方案，几乎可以从任何来源获取。
