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versatile and scalable single-cell data integration algorithm based on domain-adversarial and variational approximation.
- Source :
- Briefings in Bioinformatics; Jan2022, Vol. 23 Issue 1, p1-15, 15p
- Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- DATA integration
ALGORITHMS
CHROMATIN
SOURCE code
TRANSCRIPTOMES
Subjects
Details
- Language :
- English
- ISSN :
- 14675463
- Volume :
- 23
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Briefings in Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 155892284
- Full Text :
- https://doi.org/10.1093/bib/bbab400