Statistical Modelling 19 (4) (2019), 444–465
Identifying dynamical time series model parameters from equilibrium samples, with application to gene regulatory networks
William Chad Young,
Vaccine and Infectious Disease Division,
Fred Hutchinson Cancer Research Center,
Seattle, WA,
USA.
Ka Yee Yeung,
Institute of Technology,
University of Washington,
Tacoma, WA,
USA.
Adrian E Raftery,
Department of Statistics,
University of Washington,
Seattle, WA,
USA.
e-mail: raftery@uw.edu
Abstract:
Gene regulatory network reconstruction is an essential task of genomics in order to further our understanding of how genes interact dynamically with each other. The most readily available data, however, are from steady-state observations. These data are not as informative about the relational dynamics between genes as knockout or over-expression experiments, which attempt to control the expression of individual genes. We develop a new framework for network inference using samples from the equilibrium distribution of a vector autoregressive (VAR) time-series model which can be applied to steady-state gene expression data. We explore the theoretical aspects of our method and apply the method to synthetic gene expression data generated using GeneNetWeaver.
Keywords:
gene networks; network reconstruction; time series; VAR equilibrium.
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