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Visualization of Genomic Changes by Segmented Smoothing Using an L0 Penalty

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  • Ralph C A Rippe
  • Jacqueline J Meulman
  • Paul H C Eilers

Abstract

Copy number variations (CNV) and allelic imbalance in tumor tissue can show strong segmentation. Their graphical presentation can be enhanced by appropriate smoothing. Existing signal and scatterplot smoothers do not respect segmentation well. We present novel algorithms that use a penalty on the norm of differences of neighboring values. Visualization is our main goal, but we compare classification performance to that of VEGA.

Suggested Citation

  • Ralph C A Rippe & Jacqueline J Meulman & Paul H C Eilers, 2012. "Visualization of Genomic Changes by Segmented Smoothing Using an L0 Penalty," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-14, June.
    • Handle: RePEc:plo:pone00:0038230
    DOI: 10.1371/journal.pone.0038230
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    References listed on IDEAS

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    1. Zongzhi Liu & Ao Li & Vincent Schulz & Min Chen & David Tuck, 2010. "MixHMM: Inferring Copy Number Variation and Allelic Imbalance Using SNP Arrays and Tumor Samples Mixed with Stromal Cells," PLOS ONE, Public Library of Science, vol. 5(6), pages 1-14, June.
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    Cited by:

    1. Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.
    2. Goepp, Vivien & van de Kassteele, Jan, 2024. "Graph-based spatial segmentation of areal data," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).

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