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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Testing network autocorrelation without replicates.

Kwun Chuen Gary Chan1, Jinhui Han2, Adrian Patrick Kennedy2

  • 1Department of Biostatistics, University of Washington, Washington, Seattle, United States of America.

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We developed a new statistical test to detect autocorrelation in network data. This method generalizes the Ljung-Box test for time series to graph structures, offering improved power for analyzing network dependencies.

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Area of Science:

  • Network analysis
  • Statistical modeling
  • Time series analysis

Background:

  • Autocorrelation analysis is crucial for understanding dependencies in data.
  • Existing methods like the Ljung-Box test are limited to time series data.
  • Graph-structured data presents unique challenges for autocorrelation testing.

Purpose of the Study:

  • To propose a novel portmanteau test for autocorrelation in graph-structured network data without replicates.
  • To generalize the Ljung-Box test to a network setting.
  • To provide a simple and implementable procedure for testing graph-structured autocorrelation.

Main Methods:

  • Generalization of the Ljung-Box test using a central limit theorem for weakly stationary random fields.
  • Derivation of the asymptotic distribution of the test statistic as chi-squared under the null hypothesis.
  • Numerical simulations to validate asymptotic results and compare power with permutation tests.

Main Results:

  • The proposed test follows a chi-squared distribution, simplifying implementation.
  • The test effectively detects graph-structured autocorrelation, including spatial and spatial-temporal cases.
  • Simulations show rapid convergence and increased statistical power compared to permutation tests.

Conclusions:

  • The new test provides a robust method for assessing autocorrelation in network data.
  • It offers a valuable tool for analyzing spatial and spatial-temporal dependencies in networked systems.
  • The method was successfully applied to model COVID-19 case distribution in New York state.