RT Journal Article
SR Electronic
T1 scBasset: Sequence-based modeling of single cell ATAC-seq using convolutional neural networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.09.08.459495
DO 10.1101/2021.09.08.459495
A1 Yuan, Han
A1 Kelley, David R
YR 2021
UL http://biorxiv.org/content/early/2021/09/10/2021.09.08.459495.abstract
AB Single cell ATAC-seq (scATAC) shows great promise for studying cellular heterogeneity in epigenetic landscapes, but there remain significant challenges in the analysis of scATAC data due to the inherent high dimensionality and sparsity. Here we introduce scBasset, a sequence-based convolutional neural network method to model scATAC data. We show that by leveraging the DNA sequence information underlying accessibility peaks and the expressiveness of a neural network model, scBasset achieves state-of-the-art performance across a variety of tasks on scATAC and single cell multiome datasets, including cell type identification, scATAC profile denoising, data integration across assays, and transcription factor activity inference.Competing Interest StatementH.Y. and D.R.K. are paid employees of Calico Life Sciences.