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Improving distance measures between genomic tracks with mutual proximity.

Thomas Haschka1, Jean Baptiste Morlot2, Leopold Carron2

  • 1Laboratoire Structure et Instabilité des Génomes - INSERM U1154 - CNRS 7196 Muséum National d'Histoire Naturelle - 43, rue Cuvier - 75005 Paris, France.

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|July 26, 2021
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Summary

Enhancement methods like mutual proximity (MP) significantly improve distance measures for comparing high-dimensional genomic data. MP effectively increases the separability of experimental replicates, enhancing epigenomic profile discrimination.

Keywords:
Lp-normChIP-seqDNA methylationRNA-seqdistance measurehigh dimensional datasetmutual proximity

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Area of Science:

  • Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • Genomic annotation relies on diverse functional data, including DNA methylation, histone modifications, and transcriptomes.
  • Comparing high-dimensional genomic vectors from various experimental conditions necessitates robust distance or dissimilarity measures.
  • Commonly used distances like Pearson, Cosine, and Lp-norm are applied to count and binary genomic data.

Purpose of the Study:

  • To evaluate the performance of enhanced distance measures for comparing genomic datasets.
  • To assess the impact of enhancement methods, specifically mutual proximity (MP) and local scaling, on common distance metrics.
  • To determine the effectiveness of these enhanced measures in discriminating between groups of experimental replicates.

Main Methods:

  • Systematic evaluation of enhanced distance measures using separability of experimental replicates as a performance metric.
  • Application of enhancement techniques, including mutual proximity (MP) and local scaling, to standard distance measures.
  • Comparison of performance across different epigenetic experiment types.

Main Results:

  • The mutual proximity (MP) enhancement method significantly boosts the performance of various distance measures.
  • MP-enhanced distances demonstrate a drastic increase in the separability of experimental groups.
  • Specific combinations, such as MP with Pearson, Cosine, L1, Yule, or Jaccard distances, are highly effective for epigenomic profile discrimination.

Conclusions:

  • Enhancement methods, particularly mutual proximity (MP), are crucial for improving the analysis of high-dimensional genomic data.
  • MP-enhanced distance metrics offer superior performance in distinguishing between epigenomic profiles across diverse experimental contexts.
  • The findings provide a valuable approach for more accurate and reliable comparative epigenomic studies.