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SCYN: Single cell CNV profiling method using dynamic programming

View ORCID ProfileXikang Feng, Lingxi Chen, Yuhao Qing, Ruikang Li, Chaohui Li, Shuai Cheng Li
doi: https://doi.org/10.1101/2020.03.27.011353
Xikang Feng
1Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
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  • ORCID record for Xikang Feng
Lingxi Chen
1Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
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Yuhao Qing
1Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
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Ruikang Li
1Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
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Chaohui Li
1Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
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Shuai Cheng Li
1Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
2Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
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  • For correspondence: shuaicli@cityu.edu.hk
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Abstract

Copy number variation is crucial in deciphering the mechanism and cure of complex disorders and cancers. The recent advancement of scDNA sequencing technology sheds light upon addressing intratumor heterogeneity, detecting rare subclones, and reconstructing tumor evolution lineages at single-cell resolution. Nevertheless, the current circular binary segmentation based approach proves to fail to efficiently and effectively identify copy number shifts on some exceptional trails. Here, we propose SCYN, a CNV segmentation method powered with dynamic programming. SCYN resolves the precise segmentation on two in silico datasets. Then we verified SCYN manifested accurate copy number inferring on triple negative breast cancer scDNA data, with array comparative genomic hybridization results of purified bulk samples as ground truth validation. We tested SCYN on two datasets of the newly emerged 10x Genomics CNV solution. SCYN successfully recognizes gastric cancer cells from 1% and 10% spike-ins 10x datasets. Moreover, SCYN is about 150 times faster than state of the art tool when dealing with the datasets of approximately 2000 cells. SCYN robustly and efficiently detects segmentations and infers copy number profiles on single cell DNA sequencing data. It serves to reveal the tumor intra-heterogeneity. The source code of SCYN can be accessed in https://github.com/xikanfeng2/SCYN. The visualization tools are hosted on https://sc.deepomics.org/.

  • List of abbreviations

    CNV
    Copy Number Variation
    scDNA-seq
    Single Cell DNA sequencing
    scRNA-seq
    Single Cell RNA sequencing
    aCGH
    array Comparative Genomic Hybridization
    CBS
    Circular Binary Segmentation
    HMM
    Hidden Markov Model
    ARI
    Adjusted Rand Index
    NMI
    Normalized Mutual Information
    JI
    Jaccard Index
    mBIC
    modified Bayesian information criteria
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    Posted March 29, 2020.
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    SCYN: Single cell CNV profiling method using dynamic programming
    Xikang Feng, Lingxi Chen, Yuhao Qing, Ruikang Li, Chaohui Li, Shuai Cheng Li
    bioRxiv 2020.03.27.011353; doi: https://doi.org/10.1101/2020.03.27.011353
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    SCYN: Single cell CNV profiling method using dynamic programming
    Xikang Feng, Lingxi Chen, Yuhao Qing, Ruikang Li, Chaohui Li, Shuai Cheng Li
    bioRxiv 2020.03.27.011353; doi: https://doi.org/10.1101/2020.03.27.011353

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