@article{10.1093/bib/bbad104,
    author = {Zhang, Hongning and Lu, Mingkun and Lin, Gaole and Zheng, Lingyan and Zhang, Wei and Xu, Zhijian and Zhu, Feng},
    title = "{SoCube: an innovative end-to-end doublet detection algorithm for analyzing scRNA-seq data}",
    journal = {Briefings in Bioinformatics},
    year = {2023},
    month = {03},
    abstract = "{Doublets formed during single-cell RNA sequencing (scRNA-seq) severely affect downstream studies, such as differentially expressed gene analysis and cell trajectory inference, and limit the cellular throughput of scRNA-seq. Several doublet detection algorithms are currently available, but their generalization performance could be further improved due to the lack of effective feature-embedding strategies with suitable model architectures. Therefore, SoCube, a novel deep learning algorithm, was developed to precisely detect doublets in various types of scRNA-seq data. SoCube (i) proposed a novel 3D composite feature-embedding strategy that embedded latent gene information and (ii) constructed a multikernel, multichannel CNN-ensembled architecture in conjunction with the feature-embedding strategy. With its excellent performance on benchmark evaluation and several downstream tasks, it is expected to be a powerful algorithm to detect and remove doublets in scRNA-seq data. SoCube is freely provided as an end-to-end tool on the Python official package site PyPi (https://pypi.org/project/socube/) and open-source on GitHub (https://github.com/idrblab/socube/).}",
    issn = {1477-4054},
    doi = {10.1093/bib/bbad104},
    url = {https://doi.org/10.1093/bib/bbad104},
    note = {bbad104},
    eprint = {https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbad104/49570241/bbad104.pdf},
}