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The effect of statistical normalization on network propagation scores.

Sergio Picart-Armada1,2, Wesley K Thompson3,4, Alfonso Buil3

  • 1B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Barcelona, 08028, Spain.

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Network diffusion scores can be biased by topology. Statistical normalization removes this bias, making results more reliable for applications like gene-disease association. Decision to normalize should be data-driven.

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Area of Science:

  • Computational biology
  • Network analysis
  • Bioinformatics

Background:

  • Network diffusion and label propagation are key tools in computational biology.
  • Concerns exist regarding topological bias in diffusion scores, leading to permutation analyses.
  • The statistical properties and biases of these diffusion processes require characterization.

Purpose of the Study:

  • To characterize common null models used in permutation analysis for network diffusion.
  • To investigate the statistical properties and topological biases of diffusion scores.
  • To benchmark diffusion scores across different biological network applications.

Main Methods:

  • Characterized null models for permutation analysis.
  • Benchmarked seven diffusion scores on synthetic and real biological network data.
  • Analyzed diffusion scores using binary and quantitative labels.
  • Developed theoretical results for quantitative labels.

Main Results:

  • Diffusion scores are affected by label codification and exhibit topological bias.
  • Statistical normalization (parametric and non-parametric) removes codification dependence and equalizes bias.
  • Identified and quantified mean value and variance as sources of bias.
  • Normalization is beneficial when positive labels are not aligned with the bias.

Conclusions:

  • Normalization effectively addresses topological bias in network diffusion scores.
  • The decision to remove bias should be problem and data-driven, based on quantitative analysis.
  • Understanding bias sources is crucial for reliable interpretation of network diffusion results.