Department of Computer Science and Engineering, The Chinese University of Hong Kong (CUHK), Hong Kong SAR 999077, China.
The Chinese University of Hong Kong (CUHK) Shenzhen Research Institute, Nanshan, Shenzhen 518057, China.
Bioinformatics. 2023 Apr 3;39(4). doi: 10.1093/bioinformatics/btad162.
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.
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.
Zenodo link: https://zenodo.org/badge/latestdoi/368779433. github: https://github.com/kakarotcq/Con-AAE.
我们已经进入了多组学时代，可以从不同的方面来测量细胞。因此，通过整合或匹配同一对象不同空间的数据，我们可以获得更全面的视图。然而，在单细胞多组学的情况下，这是特别具有挑战性的，因为这些数据非常稀疏，具有极高的维度。虽然有些技术可以同时测量 scATAC-seq 和 scRNA-seq，但由于实验环境的限制，数据通常非常嘈杂。
为了促进单细胞多组学的研究，我们克服了上述挑战，提出了一种新的框架，对比循环对抗自动编码器，可以对齐和整合单细胞 RNA-seq 数据和单细胞 ATAC-seq 数据。Con-AAE 可以有效地将来自不同空间的具有高稀疏性和噪声的上述数据映射到协调子空间，在这个子空间中，对齐和整合任务可以更容易完成。我们在几个数据集上证明了它的优势。
Zenodo 链接：https://zenodo.org/badge/latestdoi/368779433。github：https://github.com/kakarotcq/Con-AAE。
