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.
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Abstract -
Li, Lingyun ; Huang, Fazheng ; Shi, Yi ; Luo, Zhong-Zhen ; Wang, Guo-Qiang ; Li, Xin-Xiong ; Li, Bingxuan ; Zhang, Lizhen ; Yu, Yi ; Feng, Ya-Nan ; et al ( , Journal of the American Chemical Society)