scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously
説明
<jats:title>Abstract</jats:title><jats:p>It is a challenging task to integrate scRNA-seq and scATAC-seq data obtained from different batches. Existing methods tend to use a pre-defined gene activity matrix to convert the scATAC-seq data into scRNA-seq data. The pre-defined gene activity matrix is often of low quality and does not reflect the dataset-specific relationship between the two data modalities. We propose scDART, a deep learning framework that integrates scRNA-seq and scATAC-seq data and learns cross-modalities relationships simultaneously. Specifically, the design of scDART allows it to preserve cell trajectories in continuous cell populations and can be applied to trajectory inference on integrated data.</jats:p>
収録刊行物
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- Genome Biology
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Genome Biology 23 (1), 1-, 2022-06-27
Springer Science and Business Media LLC