Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA.
Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA.
BMC Bioinformatics. 2020 Mar 17;21(1):108. doi: 10.1186/s12859-020-3443-8.
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.
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.
The ability of MethylNet to capture nonlinear interactions presents an opportunity for further study of unknown disease, cellular heterogeneity and aging processes.
DNA 甲基化（DNAm）是一种基因表达程序的表观遗传调控因子，它可以通过环境暴露、衰老和发病机制发生改变。由于数据的高维性、连续性、相互作用和非线性，与 DNAm 改变相关联的表型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前景。DNAm 深度学习方法尚未形式化为用户友好的执行、训练和解释模型的框架。在这里，我们描述了 MethylNet，这是一种 DNAm 深度学习方法，它可以在最小的用户监督下构建嵌入、进行预测、生成新数据和揭示未知的异质性。
我们的实验结果表明，MethylNet 可以研究细胞差异，掌握癌症亚型的更高阶信息，估计年龄，并捕捉与吸烟相关的因素，与已知的差异一致。
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。
