PT  - JOURNAL ARTICLE
AU  - Han Yuan
AU  - David R Kelley
TI  - scBasset: Sequence-based modeling of single cell ATAC-seq using convolutional neural networks
AID  - 10.1101/2021.09.08.459495
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.09.08.459495
4099  - http://biorxiv.org/content/early/2021/09/10/2021.09.08.459495.short
4100  - http://biorxiv.org/content/early/2021/09/10/2021.09.08.459495.full
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.