Completing Single-Cell DNA Methylome Profiles via Transfer Learning Together With KL-Divergence

Abstract

The high level of sparsity in methylome profiles obtained using whole-genome bisulfite sequencing in the case of low biological material amount limits its value in the study of systems in which large samples are difficult to assemble, such as mammalian preimplantation embryonic development. The recently developed computational methods for addressing the sparsity by imputing missing have their limits when the required minimum data coverage or profiles of the same tissue in other modalities are not available. In this study, we explored the use of transfer learning together with Kullback-Leibler (KL) divergence to train predictive models for completing methylome profiles with very low coverage (below 2%). Transfer learning was used to leverage less sparse profiles that are typically available for different tissues for the same species, while KL divergence was employed to maximize the usage of information carried in the input data. A deep neural network was adopted to extract both DNA sequence and local methylation patterns for imputation. Our study of training models for completing methylome profiles of bovine oocytes and early embryos demonstrates the effectiveness of transfer learning and KL divergence, with individual increase of 29.98 and 29.43%, respectively, in prediction performance and 38.70% increase when the two were used together. The drastically increased data coverage (43.80–73.6%) after imputation powers downstream analyses involving methylomes that cannot be effectively done using the very low coverage profiles (0.06–1.47%) before imputation.

Cross Tissue DNAm Biomarker Prediction using Transfer Learning

“…TL, a powerful technique in machine learning, is adept at leveraging information from related data sources and applying knowledge from one context to enhance precision in another [19]. TL has been used in bioinformatics for tasks like imputing the methylome in low-coverage cases [20] and augmenting gene expression data [21], but its application in DNAm biomarkers, particularly for cross-tissue prediction, has yet to be explored.…”

Recall DNA methylation levels at low coverage sites using a CNN model in WGBS

“…In single-cell methylomes, there are sites not covered by any reads in some single cell. The DNA methylation status of other cells and DNA sequenced can be used as the features of the deep learning model to predict the methylation status [30][31][32]. However, in traditional bulk WGBS data, calling DNA methylation also has the problem of insufficient coverage.…”