ReHiC: Enhancing Hi-C data resolution via residual convolutional network
Abstract
High-throughput chromosome conformation capture (Hi-C) is one of the most popular methods for studying the three-dimensional organization of genomes. However, Hi-C protocols can be expensive since they require large amounts of sample material and may be time-consuming. Most commonly used Hi-C data are low-resolution. Such data can only be used to identify large-scale genomic interactions and are not sufficient to identify the small-scale patterns. We propose a novel deep learning-based computational approach (named ReHiC) that enhances the resolution of Hi-C data and allows us to achieve high-resolution Hi-C data at a relatively low cost. Our model only requires 1/16 down-sampling ratio of the original sequence reading to predict higher resolution Hi-C data. This is very close to high-resolution data in terms of numerical distribution and interaction distribution. More importantly, our framework stacks deeper and converges faster due to residual blocks in the core of the network. Extensive experiments show that ReHiC performs better than HiCPlus and HiCNN, two recently developed and frequently used methods to look at the spatial organization of chromatin structure in the cell. Moreover, the portability of our framework verified by extensive experiments shows that the trained model can also enhance the Hi-C matrix of other cell types efficiently. In conclusion, ReHiC offers more accurate high-resolution image reconstruction in a broad field.
