In:
Bioinformatics, Oxford University Press (OUP), Vol. 39, No. 4 ( 2023-04-03)
Kurzfassung:
We have entered the multi-omics era and can measure cells from different aspects. Hence, we can get a more comprehensive view by integrating or matching data from different spaces corresponding to the same object. However, it is particularly challenging in the single-cell multi-omics scenario because such data are very sparse with extremely high dimensions. Though some techniques can be used to measure scATAC-seq and scRNA-seq simultaneously, the data are usually highly noisy due to the limitations of the experimental environment. Results To promote single-cell multi-omics research, we overcome the above challenges, proposing a novel framework, contrastive cycle adversarial autoencoders, which can align and integrate single-cell RNA-seq data and single-cell ATAC-seq data. Con-AAE can efficiently map the above data with high sparsity and noise from different spaces to a coordinated subspace, where alignment and integration tasks can be easier. We demonstrate its advantages on several datasets. Availability and implementation Zenodo link: https://zenodo.org/badge/latestdoi/368779433. github: https://github.com/kakarotcq/Con-AAE.
Materialart:
Online-Ressource
ISSN:
1367-4811
DOI:
10.1093/bioinformatics/btad162
Sprache:
Englisch
Verlag:
Oxford University Press (OUP)
Publikationsdatum:
2023
ZDB Id:
1468345-3
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