背景:来自单细胞RNA测序数据的谱系追踪和轨迹推断在揭示驱动发育和疾病的遗传程序方面具有巨大潜力。单细胞数据集被认为提供了关于组织的不同细胞结构的无偏视图。采样偏差,然而,可以使单细胞数据集偏离它们要代表的细胞组成。
结果:我们展示了一种新形式的抽样偏差,由与不断增长的重复采样有关的统计现象引起的,异质种群。细胞的相对生长速率影响它们将在多个时间点观察到的克隆中取样的概率。我们通过模拟研究和对T细胞发育的实时过程的分析来支持我们的概率推导。我们发现这种偏差会影响命运概率预测,我们探索如何开发对这种偏差具有鲁棒性的轨迹推理方法。
背景:用于模拟数据集和创建本手稿中的图形的源代码可以在https://github.com/rbonhamcarter/simulate-clones的python中免费获得。LineageOT方法扩展的python实现可在https://github.com/rbonhamcarter/LineageOT/tree/multi-time-clone上免费获得。
背景:补充数据可在Bioinformation在线获得。
BACKGROUND: Lineage tracing and trajectory inference from single-cell RNA-sequencing data hold tremendous potential for uncovering the genetic programs driving development and disease. Single cell datasets are thought to provide an unbiased view on the diverse cellular architecture of tissues. Sampling bias, however, can skew single cell datasets away from the cellular composition they are meant to represent.
RESULTS: We demonstrate a novel form of sampling bias, caused by a statistical phenomenon related to repeated sampling from a growing, heterogeneous population. Relative growth rates of cells influence the probability that they will be sampled in clones observed across multiple time points. We support our probabilistic derivations with a simulation study and an analysis of a real time-course of T-cell development. We find that this bias can impact fate probability predictions, and we explore how to develop trajectory inference methods which are robust to this bias.
METHODS: Source code for the simulated datasets and to create the figures in this manuscript is freely available in python at https://github.com/rbonhamcarter/simulate-clones. A python implementation of the extension of the LineageOT method is freely available at https://github.com/rbonhamcarter/LineageOT/tree/multi-time-clones.