Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.
Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.
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DOI：
10.1371/journal.pcbi.1005212
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发表时间：
2016-11
影响因子：
4.3
通讯作者：
Yau C
中科院分区：
文献类型：
--
作者：
Campbell KR;Yau C
单细胞基因表达谱可用于量化细胞分化等时间过程中的转录动态，使用计算方法用“伪时间”标记每个细胞，而真正的时间序列实验太难执行。然而，由于个体细胞之间基因表达的高度可变性，细胞的精确时间排序存在固有的不确定性。现有的伪时间估计方法主要给出了点估计，排除了对不确定性影响的严格分析。我们使用概率建模技术来量化伪时间的不确定性，并将其传播到下游的差分表达式分析。我们表明，依赖于一个点估计的伪时间可能会导致膨胀的错误发现率和概率方法提供更大的鲁棒性和措施的时间分辨率，可以从伪时间推断。了解控制基本动态生物过程的“细胞编程”对于确定正常细胞功能和可能引起生理紊乱的潜在扰动非常重要。理想情况下，研究将采用时间序列实验来定期测量每个细胞的特性。这将使我们能够理解构成所遵循程序的基因激活序列。在实践中，这样的实验可能难以进行，因为细胞活动可能是异步的，每个细胞占据感兴趣的过程的不同阶段。此外，所有转录物或蛋白质的无偏测量需要捕获和裂解细胞，从而排除了对该细胞的持续监测。在缺乏进行真实时间序列实验的能力的情况下，伪时间算法利用这些系统的异步细胞性质，基于其分子谱将“伪时间”数学地分配给每个细胞，从而允许细胞进行比对并回顾性地推断基因激活事件的序列。现有的方法主要使用确定性方法，忽略了与问题相关的统计不确定性。本文表明，这种统计不确定性限制了可以从细胞表达谱的静态快照中提取的时间分辨率，并且也可以在精神上影响下游分析。
Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a ‘pseudotime’ where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference. Understanding the “cellular programming” that controls fundamental, dynamic biological processes is important for determining normal cellular function and potential perturbations that might give rise to physiological disorders. Ideally, investigations would employ time series experiments to periodically measure the properties of each cell. This would allow us to understand the sequence of gene (in)activations that constitute the program being followed. In practice, such experiments can be difficult to perform as cellular activity may be asynchronous with each cell occupying a different phase of the process of interested. Furthermore, the unbiased measurement of all transcripts or proteins requires the cells to be captured and lysed precluding the continued monitoring of that cell. In the absence of the ability to conduct true time series experiments, pseudotime algorithms exploit the asynchronous cellular nature of these systems to mathematically assign a “pseudotime” to each cell based on its molecular profile allowing the cells to be aligned and the sequence of gene activation events retrospectively inferred. Existing approaches predominantly use deterministic methods that ignore the statistical uncertainties associated with the problem. This paper demonstrates that this statistical uncertainty limits the temporal resolution that can be extracted from static snapshots of cell expression profiles and can also detrimentally affect downstream analysis.
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