PT  - JOURNAL ARTICLE
AU  - Kopp, Wolfgang
AU  - Akalin, Altuna
AU  - Ohler, Uwe
TI  - Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning
AID  - 10.1101/2021.05.11.443540
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.11.443540
4099  - http://biorxiv.org/content/early/2021/05/12/2021.05.11.443540.short
4100  - http://biorxiv.org/content/early/2021/05/12/2021.05.11.443540.full
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.