Computational assignment Of cell-cycle stage from single-cell transcriptome data
Computational assignment Of cell-cycle stage from single-cell transcriptome data
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DOI：
10.1016/j.ymeth.2015.06.021
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发表时间：
2015-09-01
期刊：
影响因子：
4.8
通讯作者：
Buettner, Florian
中科院分区：
文献类型：
--
作者：
Scialdone, Antonio;Natarajan, Kedar N.;Buettner, Florian
单细胞的转录组可以揭示关于细胞状态和细胞群体内异质性的重要信息。最近，单细胞RNA测序促进了大量单细胞的平行表达谱分析。为了充分利用这些数据，开发合适的计算方法至关重要。一个关键的挑战，特别是在考虑细胞分裂群体时，是了解每个捕获细胞的细胞周期阶段。在这里，我们描述并比较了五种已建立的监督机器学习方法和一种定制的预测器，用于根据转录组将细胞分配到细胞周期阶段。特别是，我们评估了不同的标准化策略和先验知识的使用对分类器的预测能力的影响。我们在以前发布的数据集上测试了这些方法，发现基于PCA的方法和自定义预测器表现最好。此外，我们的分析表明，性能强烈依赖于规范化和先验知识的使用。只有通过利用细胞周期注释基因形式的先验知识，并通过使用基于秩的归一化对数据进行预处理，才有可能在不同的细胞类型，生物体和实验方案中稳健地捕获转录细胞周期特征。(C)2015作者爱思唯尔公司出版这是CC BY许可下的开放获取文章（http：//creativecommons.orgilicenses/by/4.0/）。
The transcriptome of single cells can reveal important information about cellular states and heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has facilitated expression profiling of large numbers of single cells in parallel. To fully exploit these data, it is critical that suitable computational approaches are developed. One key challenge, especially pertinent when considering dividing populations of cells, is to understand the cell-cycle stage of each captured cell. Here we describe and compare five established supervised machine learning methods and a custom-built predictor for allocating cells to their cell-cycle stage on the basis of their transcriptome. In particular, we assess the impact of different normalisation strategies and the usage of prior knowledge on the predictive power of the classifiers. We tested the methods on previously published datasets and found that a PCA-based approach and the custom predictor performed best. Moreover, our analysis shows that the performance depends strongly on normalisation and the usage of prior knowledge. Only by leveraging prior knowledge in form of cell-cycle annotated genes and by preprocessing the data using a rank-based normalisation, is it possible to robustly capture the transcriptional cell-cycle signature across different cell types, organisms and experimental protocols. (C) 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.orgilicenses/by/4.0/).