Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning.

ABSTRACT: Recent advances in single-cell sequencing technology have made it possible to measure multiple paired omics simultaneously in a single cell such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq). However, the widespread application of these single-cell multiomics profiling technologies has been limited by their experimental complexity, noise in nature, and high cost. In addition, single-omics sequencing technologies have generated tremendous and high-quality single-cell datasets but have yet to be fully utilized. Here, single-cell multiomics generation (scMOG), a deep learning-based framework to generate single-cell assay for transposase-accessible chromatin (ATAC) data in silic

SUBMITTER: Lan M 

PROVIDER: S-EPMC10375161 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature