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Coupled co-clustering-based unsupervised transfer learning for the integrative analysis of single-cell genomic data.
- Source :
- Briefings in Bioinformatics; Jul2021, Vol. 22 Issue 4, p1-13, 13p
- Publication Year :
- 2021
-
Abstract
- Unsupervised methods, such as clustering methods, are essential to the analysis of single-cell genomic data. The most current clustering methods are designed for one data type only, such as single-cell RNA sequencing (scRNA-seq), single-cell ATAC sequencing (scATAC-seq) or sc-methylation data alone, and a few are developed for the integrative analysis of multiple data types. The integrative analysis of multimodal single-cell genomic data sets leverages the power in multiple data sets and can deepen the biological insight. In this paper, we propose a coupled co-clustering-based unsupervised transfer learning algorithm (couple CoC) for the integrative analysis of multimodal single-cell data. Our proposed couple CoC builds upon the information theoretic co-clustering framework. In co-clustering, both the cells and the genomic features are simultaneously clustered. Clustering similar genomic features reduces the noise in single-cell data and facilitates transfer of knowledge across single-cell datasets. We applied couple CoC for the integrative analysis of scATAC-seq and scRNA-seq data, sc-methylation and scRNA-seq data and scRNA-seq data from mouse and human. We demonstrate that couple CoC improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic datasets. Our method couple CoC is also computationally efficient and can scale up to large datasets. Availability: The software and datasets are available at https://github.com/cuhklinlab/coupleCoC. [ABSTRACT FROM AUTHOR]
- Subjects :
- GENOMICS
KNOWLEDGE transfer
RNA sequencing
MACHINE learning
DATA analysis
Subjects
Details
- Language :
- English
- ISSN :
- 14675463
- Volume :
- 22
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Briefings in Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 152575525
- Full Text :
- https://doi.org/10.1093/bib/bbaa347