MethylNet: an automated and modular deep learning approach for DNA methylation analysis
MethylNet: an automated and modular deep learning approach for DNA methylation analysis
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DOI：
10.1186/s12859-020-3443-8
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发表时间：
2020-03-17
影响因子：
3
通讯作者：
Christensen, Brock C.
中科院分区：
文献类型：
--
作者：
Levy, Joshua J.;Titus, Alexander J.;Christensen, Brock C.
DNA甲基化（DNA methylation, DNAm）是基因表达程序的表观遗传调控因子，可被环境暴露、衰老和发病机制所改变。由于数据的高维、连续、相互作用和非线性性质，将DNAm改变与表型相关联的传统分析受到多重假设检验和多重共线性的影响。深度学习分析在研究疾病异质性方面显示出很大的希望。DNAm深度学习方法尚未正式化为执行、训练和解释模型的用户友好框架。在这里，我们描述了MethylNet，一种DNAm深度学习方法，它可以构建嵌入，进行预测，生成新数据，并在最小的用户监督下发现未知的异质性。结果表明，MethylNet可以研究细胞差异，掌握癌症亚型的高阶信息，估计年龄，并根据已知差异捕获与吸烟相关的因素。MethylNet捕捉非线性相互作用的能力为进一步研究未知疾病、细胞异质性和衰老过程提供了机会。
Background DNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have shown much promise to study disease heterogeneity. DNAm deep learning approaches have not yet been formalized into user-friendly frameworks for execution, training, and interpreting models. Here, we describe MethylNet, a DNAm deep learning method that can construct embeddings, make predictions, generate new data, and uncover unknown heterogeneity with minimal user supervision. Results The results of our experiments indicate that MethylNet can study cellular differences, grasp higher order information of cancer sub-types, estimate age and capture factors associated with smoking in concordance with known differences. Conclusion The ability of MethylNet to capture nonlinear interactions presents an opportunity for further study of unknown disease, cellular heterogeneity and aging processes.