RT Journal Article
SR Electronic
T1 Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.05.11.443540
DO 10.1101/2021.05.11.443540
A1 Kopp, Wolfgang
A1 Akalin, Altuna
A1 Ohler, Uwe
YR 2021
UL http://biorxiv.org/content/early/2021/05/12/2021.05.11.443540.abstract
AB Advances in single-cell technologies enable the routine interrogation of chromatin accessibility for tens of thousands of single cells, shedding light on gene regulatory processes at an unprecedented resolution. Meanwhile, size, sparsity and high dimensionality of the resulting data continue to pose challenges for its computational analysis, and specifically the integration of data from different sources. We have developed a dedicated computational approach, a variational auto-encoder using a noise model specifically designed for single-cell ATAC-seq data, which facilitates simultaneous dimensionality reduction and batch correction via an adversarial learning strategy. We showcase both its individual advantages on carefully chosen real and simulated data sets, as well as the benefits for detailed cell type characterization via integrating multiple complex datasets.Competing Interest StatementThe authors have declared no competing interest.