ArticleNucleic acids research2020
Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data.Genomics, proteomics & bioinformatics · 2025Article
- Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence.Bioinformatics (Oxford, England) · 2022Article
- Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics.Current opinion in systems biology · 2021Article
- scMC learns biological variation through the alignment of multiple single-cell genomics datasets.Genome biology · 2021Article
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Authors and funding
6 authors.
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Abstract
While the methods available for single-cell ATAC-seq analysis are well optimized for clustering cell types, the question of how to integrate multiple scATAC-seq data sets and/or sequencing modalities is still open. We present an analysis framework that enables such integration across scATAC-seq data sets by applying the CoGAPS Matrix Factorization algorithm and the projectR transfer learning program to identify common regulatory patterns across scATAC-seq data sets. We additionally integrate our analysis with scRNA-seq data to identify orthogonal evidence for transcriptional regulators predicted by scATAC-seq analysis. Using publicly available scATAC-seq data, we find patterns that accurately characterize cell types both within and across data sets. Furthermore, we demonstrate that these patterns are both consistent with current biological understanding and reflective of novel regulatory biology.
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