DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning.
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning.
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DOI：
10.1186/s13059-017-1189-z
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发表时间：
2017-04-11
期刊：
影响因子：
12.3
通讯作者：
Stegle O
中科院分区：
文献类型：
--
作者：
Angermueller C;Lee HJ;Reik W;Stegle O
最近的技术进步使DNA甲基化能够以单细胞分辨率进行测定。然而，目前的方案受到不完全CpG覆盖的限制，因此预测缺失甲基化状态的方法对于实现全基因组分析至关重要。我们报告了DeepCpG，一种基于深度神经网络的计算方法，用于预测单细胞中的甲基化状态。我们评估了DeepCpG对使用替代测序方案生成的五种细胞类型的单细胞甲基化数据的影响。DeepCpG产生比以前的方法更准确的预测。此外，我们表明，模型参数可以解释，从而提供洞察序列组成如何影响甲基化变异性。本文的在线版本（doi：10.1186/s13059-017-1189-z）包含补充材料，可供授权用户使用。
Recent technological advances have enabled DNA methylation to be assayed at single-cell resolution. However, current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. We report DeepCpG, a computational approach based on deep neural networks to predict methylation states in single cells. We evaluate DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols. DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the model parameters can be interpreted, thereby providing insights into how sequence composition affects methylation variability. The online version of this article (doi:10.1186/s13059-017-1189-z) contains supplementary material, which is available to authorized users.