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
复制标题
不确定性下的顺序：使用伪时间推理的概率模型进行稳健的差异表达分析
DOI：
10.1101/047365
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Campbell K
中科院分区：
文献类型：
--
作者：
Campbell K
单细胞基因表达谱可用于量化时间过程中的转录动力学，例如细胞分化，使用计算方法用“伪时间”标记每个细胞，其中真实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。
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.