PeakVI: A deep generative model for single-cell chromatin accessibility analysis.

ABSTRACT: Single-cell ATAC sequencing (scATAC-seq) is a powerful and increasingly popular technique to explore the regulatory landscape of heterogeneous cellular populations. However, the high noise levels, degree of sparsity, and scale of the generated data make its analysis challenging. Here, we present PeakVI, a probabilistic framework that leverages deep neural networks to analyze scATAC-seq data. PeakVI fits an informative latent space that preserves biological heterogeneity while correcting batch effects and accounting for technical effects, such as library size and region-specific biases. In addition, PeakVI provides a technique for identifying differential accessibility at a single-region resolution, which can be used for cell-type annotation as well as identification of key cis-regul

SUBMITTER: Ashuach T 

PROVIDER: S-EPMC9017241 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature