Institute for Cell Engineering, Johns Hopkins University, 733 N. Broadway, Baltimore MD, 21205, United States.
Department of Biomedical Engineering, Johns Hopkins University, 733 N. Broadway, Baltimore MD, 21205, United States.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae536.
Methods that predict fate potential or degree of differentiation from transcriptomic data have identified rare progenitor populations and uncovered developmental regulatory mechanisms. However, some state-of-the-art methods are too computationally burdensome for emerging large-scale data and all methods make inaccurate predictions in certain biological systems. We developed a method in R (stemFinder) that predicts single cell differentiation time based on heterogeneity in cell cycle gene expression. Our method is computationally tractable and is as good as or superior to competitors. As part of our benchmarking, we implemented four different performance metrics to assist potential users in selecting the tool that is most apt for their application. Finally, we explore the relationship between differentiation time and cell fate potential by analyzing a lineage tracing dataset with clonally labelled hematopoietic cells, revealing that metrics of differentiation time are correlated with the number of downstream lineages.
方法，预测命运的潜力或从转录组数据的分化程度已经确定罕见的祖细胞群体，并揭示了发育调控机制。然而，一些最先进的方法太计算负担新兴的大规模数据和所有的方法使不准确的预测在某些生物系统。我们开发了一种方法在 R （stemFinder) 预测单细胞分化时间基于细胞周期基因表达的异质性。我们的方法是计算上可行的，并且与竞争对手一样好或更好。作为我们的基准测试的一部分，我们实现了四个不同的性能指标，以帮助潜在用户选择最适合他们应用的工具。最后，我们通过分析带有克隆标记造血细胞的谱系追踪数据集来探讨分化时间与细胞命运潜力之间的关系，揭示了分化时间的度量与下游谱系的数量相关。
