A versatile and scalable single-cell data integration algorithm based on domain-adversarial and variational approximation. Issue 1 (28th September 2021)

• Record Type: Journal Article
• Authors: Hu, Jialu; Zhong, Yuanke; Shang, Xuequn
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 1(2022)
• Publication Date: 2021-09-28
• Subjects: single cell analysis -- data integration -- domain-adversarial learning -- variational approximation -- regularized regression -- multimodal data; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab400
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Single-cell technologies provide us new ways to profile transcriptomic landscape, chromatin accessibility, spatial expression patterns in heterogeneous tissues at the resolution of single cell. With enormous generated single-cell datasets, a key analytic challenge is to integrate these datasets to gain biological insights into cellular compositions. Here, we developed a domain-adversarial and variational approximation, DAVAE, which can integrate multiple single-cell datasets across samples, technologies and modalities with a single strategy. Besides, DAVAE can also integrate paired data of ATAC profile and transcriptome profile that are simultaneously measured from a same cell. With a mini-batch stochastic gradient descent strategy, it is scalable for large-scale data and can be accelerated by GPUs. Results on seven real data integration applications demonstrated the effectiveness and scalability of DAVAE in batch-effect removing, transfer learning and cell-type predictions for multiple single-cell datasets across samples, technologies and modalities. Availability: DAVAE has been implemented in a toolkit package "scbean" in the pypi repository, and the source code can be also freely accessible at https://github.com/jhu99/scbean . All our data and source code for reproducing the results of this paper can be accessible at https://github.com/jhu99/davae_paper .