Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
Sci Rep. 2021 Apr 23;11(1):8880. doi: 10.1038/s41598-021-88115-9.
Chromatin conformation plays an important role in a variety of genomic processes. Hi-C is one of the most popular assays for inspecting chromatin conformation. However, the utility of Hi-C contact maps is bottlenecked by resolution. Here we present VEHiCLE, a deep learning algorithm for resolution enhancement of Hi-C contact data. VEHiCLE utilises a variational autoencoder and adversarial training strategy equipped with four loss functions (adversarial loss, variational loss, chromosome topology-inspired insulation loss, and mean square error loss) to enhance contact maps, making them more viable for downstream analysis. VEHiCLE expands previous efforts at Hi-C super resolution by providing novel insight into the biologically meaningful and human interpretable feature extraction. Using a deep variational autoencoder, VEHiCLE provides a user tunable, full generative model for generating synthetic Hi-C data while also providing state-of-the-art results in enhancement of Hi-C data across multiple metrics.
染色质构象在各种基因组过程中起着重要作用。Hi-C 是检测染色质构象的最流行的分析方法之一。然而，Hi-C 接触图谱的实用性受到分辨率的限制。在这里，我们提出了 VEHiCLE，这是一种用于增强 Hi-C 接触数据分辨率的深度学习算法。VEHiCLE 利用变分自动编码器和带有四个损失函数（对抗损失、变分损失、基于染色体拓扑的隔离损失和均方误差损失）的对抗训练策略来增强接触图谱，使其更适合下游分析。VEHiCLE 通过为生物有意义和人类可解释的特征提取提供新的见解，扩展了之前的 Hi-C 超分辨率工作。使用深度变分自动编码器，VEHiCLE 为生成合成 Hi-C 数据提供了用户可调节的、完整的生成模型，同时在多个指标上增强 Hi-C 数据方面也取得了最先进的结果。
