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Translocation detection from Hi‐C data via scan statistics

Author

Listed:
  • Anthony Cheng
  • Disheng Mao
  • Yuping Zhang
  • Joseph Glaz
  • Zhengqing Ouyang

Abstract

Recent Hi‐C technology enables more comprehensive chromosomal conformation research, including the detection of structural variations, especially translocations. In this paper, we formulate the interchromosomal translocation detection as a problem of scan clustering in a spatial point process. We then develop TranScan, a new translocation detection method through scan statistics with the control of false discovery. The simulation shows that TranScan is more powerful than an existing sophisticated scan clustering method, especially under strong signal situations. Evaluation of TranScan against current translocation detection methods on realistic breakpoint simulations generated from real data suggests better discriminative power under the receiver‐operating characteristic curve. Power analysis also highlights TranScan's consistent outperformance when sequencing depth and heterozygosity rate is varied. Comparatively, Type I error rate is lowest when evaluated using a karyotypically normal cell line. Both the simulation and real data analysis indicate that TranScan has great potentials in interchromosomal translocation detection using Hi‐C data.

Suggested Citation

  • Anthony Cheng & Disheng Mao & Yuping Zhang & Joseph Glaz & Zhengqing Ouyang, 2023. "Translocation detection from Hi‐C data via scan statistics," Biometrics, The International Biometric Society, vol. 79(2), pages 1306-1317, June.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:1306-1317
    DOI: 10.1111/biom.13724
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    References listed on IDEAS

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    1. Vladimir Pozdnyakov & Joseph Glaz & Martin Kulldorff & J. Steele, 2005. "A martingale approach to scan statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(1), pages 21-37, March.
    2. Jesse R. Dixon & Siddarth Selvaraj & Feng Yue & Audrey Kim & Yan Li & Yin Shen & Ming Hu & Jun S. Liu & Bing Ren, 2012. "Topological domains in mammalian genomes identified by analysis of chromatin interactions," Nature, Nature, vol. 485(7398), pages 376-380, May.
    3. Wu, Tung-Lung & Glaz, Joseph, 2015. "A new adaptive procedure for multiple window scan statistics," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 164-172.
    4. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    5. Perone Pacifico, M. & Genovese, C. & Verdinelli, I. & Wasserman, L., 2007. "Scan clustering: A false discovery approach," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1441-1469, August.
    6. M. Perone Pacifico & C. Genovese & I. Verdinelli & L. Wasserman, 2004. "False Discovery Control for Random Fields," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1002-1014, December.
    7. Chan, Hock Peng & Zhang, Nancy Ruonan, 2007. "Scan Statistics With Weighted Observations," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 595-602, June.
    8. Glaz, Joseph, 1992. "Approximations for tail probabilities and moments of the scan statistic," Computational Statistics & Data Analysis, Elsevier, vol. 14(2), pages 213-227, August.
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