SAILER: scalable and accurate invariant representation learning for single-cell ATAC-seq processing and integration.

ABSTRACT:

Motivation

Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) provides new opportunities to dissect epigenomic heterogeneity and elucidate transcriptional regulatory mechanisms. However, computational modeling of scATAC-seq data is challenging due to its high dimension, extreme sparsity, complex dependencies and high sensitivity to confounding factors from various sources.

Results

Here, we propose a new deep generative model framework, named SAILER, for analyzing scATAC-seq data. SAILER aims to learn a low-dimensional nonlinear latent representation of each cell that defines its intrinsic chromatin state, invariant to extrinsic confounding factors like read depth and batch effects. SAILER adopts the conventional encoder-decoder framework to learn

SUBMITTER: Cao Y 

PROVIDER: S-EPMC8275346 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature