High-throughput single-cell technologies hold the promise of discovering novel cellular relationships with disease. However, analytical workflows constructed for these technologies to associate cell proportions with disease often employ unsupervised clustering techniques that overlook the valuable hierarchical structures that have been used to define cell types. We present treekoR, a framework that empirically recapitulates these structures, facilitating multiple quantifications and comparisons of cell type proportions. Our results from twelve case studies reinforce the importance of quantifying proportions relative to parent populations in the analyses of cytometry data — as failing to do so can lead to missing important biological insights.

中文翻译：

---

treekoR：通过阐明高维细胞术数据中的层次关系来识别细胞与表型的关联

高通量单细胞技术有望发现新的细胞与疾病的关系。然而，为这些技术构建的用于将细胞比例与疾病相关联的分析工作流程通常采用无监督聚类技术，这些技术忽略了用于定义细胞类型的有价值的层次结构。我们提出了treekoR，一个凭经验概括这些结构的框架，促进了细胞类型比例的多重量化和比较。我们来自 12 个案例研究的结果强调了在细胞计数数据分析中量化相对于父母群体的比例的重要性——因为不这样做可能会导致错过重要的生物学见解。