Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data

Simultaneous profiling transcriptomic and chromatin accessibility information in the same individual cells offers an unprecedented resolution to understand cell states. However, computationally effective methods for the integration of these inherent sparse and heterogeneous data are lacking. Here, we present a single-cell multimodal variational autoencoder model, which combines three types of joint-learning strategies with a probabilistic Gaussian Mixture Model to learn the joint latent features that accurately represent these multilayer profiles. Studies on both simulated datasets and real datasets demonstrate that it has more preferable capability (i) dissecting cellular heterogeneity in the joint-learning space, (ii) denoising and imputing data and (iii) constructing the association between multilayer omics data, which can be used for understanding transcriptional regulatory mechanisms.

Automated summary

The study introduces a single-cell multimodal variational autoencoder model that effectively integrates transcriptomic and chromatin accessibility information, accurately representing the multilayer profiles of cells and demonstrating good capabilities in dissecting cellular heterogeneity, denoising and imputing data, as well as constructing associations between multilayer omics data.