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Reverse engineering gene regulatory networks from measurement with missing values.

Oyetunji E Ogundijo1, Abdulkadir Elmas1, Xiaodong Wang1

  • 1Department of Electrical Engineering, Columbia University, 500 W 120th Street, New York, 10027 NY USA.

EURASIP Journal on Bioinformatics & Systems Biology
|January 28, 2017
PubMed
Summary

New point-based Gaussian approximation (PBGA) filters accurately infer gene regulatory networks from gene expression time series data with missing values. This method improves model parameter inference and gene regulatory network prediction compared to conventional filters.

Keywords:
Bayesian inferenceGaussian filtersGene expressionMissing dataNetwork inference

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Gene expression time series data are high-dimensional and often contain missing values, hindering analysis.
  • Missing data significantly impacts algorithms for gene expression analysis and gene regulatory network inference.
  • Few algorithms exist for inferring gene regulatory networks from incomplete gene expression data.

Purpose of the Study:

  • To develop a method for inferring gene regulatory networks (GRNs) from time series gene expression data with missing values.
  • To propose novel point-based Gaussian approximation (PBGA) filters for joint state and parameter estimation in the presence of missing measurements.
  • To enhance the accuracy of GRN inference by accounting for missing data points within a state-space model.

Main Methods:

  • Developed a nonlinear dynamic stochastic model for gene expression evolution.
  • Implemented point-based Gaussian approximation (PBGA) filters for one-step and two-step missing measurements.
  • Utilized quadrature rules like the unscented transform (UT), cubature rule, and central difference rule for posterior computation.

Main Results:

  • PBGA filters demonstrated satisfying performance on synthetic, in silico (DREAM project), and real biological (yeast IRMA) networks.
  • The proposed measurement model effectively incorporated missing data points into the sequential estimation algorithm.
  • Achieved more accurate GRN prediction compared to conventional Gaussian approximation (GA) filters that ignore missing data.

Conclusions:

  • PBGA filters provide a robust approach for elucidating GRNs from gene expression data with missing values.
  • The novel measurement model within the state-space framework improves parameter inference and GRN prediction accuracy.
  • This method offers a significant advancement for analyzing incomplete biological time series data.