School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia.
Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
Genome Biol. 2021 Nov 29;22(1):324. doi: 10.1186/s13059-021-02526-5.
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.
高通量单细胞技术有望发现与疾病相关的新型细胞关系。然而，为将细胞比例与疾病相关联而构建的这些技术的分析工作流程通常采用无监督聚类技术，而忽略了已用于定义细胞类型的有价值的层次结构。我们提出了 treekoR，这是一个经验上再现这些结构的框架，促进了细胞类型比例的多种量化和比较。我们从十二个案例研究中得出的结果，强调了在分析细胞测定数据时相对于父群体进行比例量化的重要性 - 因为未能这样做可能会导致错过重要的生物学见解。
