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Comparison of Profile Similarity Measures for Genetic Interaction Networks

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  • Raamesh Deshpande
  • Benjamin VanderSluis
  • Chad L Myers

Abstract

Analysis of genetic interaction networks often involves identifying genes with similar profiles, which is typically indicative of a common function. While several profile similarity measures have been applied in this context, they have never been systematically benchmarked. We compared a diverse set of correlation measures, including measures commonly used by the genetic interaction community as well as several other candidate measures, by assessing their utility in extracting functional information from genetic interaction data. We find that the dot product, one of the simplest vector operations, outperforms most other measures over a large range of gene pairs. More generally, linear similarity measures such as the dot product, Pearson correlation or cosine similarity perform better than set overlap measures such as Jaccard coefficient. Similarity measures that involve L2-normalization of the profiles tend to perform better for the top-most similar pairs but perform less favorably when a larger set of gene pairs is considered or when the genetic interaction data is thresholded. Such measures are also less robust to the presence of noise and batch effects in the genetic interaction data. Overall, the dot product measure performs consistently among the best measures under a variety of different conditions and genetic interaction datasets.

Suggested Citation

  • Raamesh Deshpande & Benjamin VanderSluis & Chad L Myers, 2013. "Comparison of Profile Similarity Measures for Genetic Interaction Networks," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-11, July.
  • Handle: RePEc:plo:pone00:0068664
    DOI: 10.1371/journal.pone.0068664
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    References listed on IDEAS

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    Cited by:

    1. Yi Ge & Wen Dou & Jianping Dai, 2017. "A New Approach to Identify Social Vulnerability to Climate Change in the Yangtze River Delta," Sustainability, MDPI, vol. 9(12), pages 1-19, December.
    2. Stephanie Chang & Jackie Yip & Shona Zijll de Jong & Rebecca Chaster & Ashley Lowcock, 2015. "Using vulnerability indicators to develop resilience networks: a similarity approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(3), pages 1827-1841, September.
    3. Chihyun Park & JungRim Kim & Jeongwoo Kim & Sanghyun Park, 2018. "Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-15, July.

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