Oates, Chris J. and Mukherjee, Sach (2012) Network inference and biological dynamics. Annals of Applied Statistics, Vol.6 (No.3). pp. 1209-1235. doi:10.1214/11-AOAS532

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Abstract

Network inference approaches are now widely used in biological applications to probe regulatory relationships between molecular components such as genes or proteins. Many methods have been proposed for this setting, but the connections and differences between their statistical formulations have received less attention. In this paper,
we show how a broad class of statistical network inference methods, including a number of existing approaches, can be described in terms of variable selection for the linear model. This reveals some subtle but important differences between the methods, including the treatment of time intervals in discretely observed data. In developing a
general formulation, we also explore the relationship between singlecell stochastic dynamics and network inference on averages over cells. This clarifies the link between biochemical networks as they operate at the cellular level and network inference as carried out on data that
are averages over populations of cells. We present empirical results, comparing thirty-two network inference methods that are instances of the general formulation we describe, using two published dynamical models. Our investigation sheds light on the applicability and limitations of network inference and provides guidance for practitioners
and suggestions for experimental design.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Science > Centre for Complexity Science Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	System analysis, Biological control systems -- Mathematical models
Journal or Publication Title:	Annals of Applied Statistics
Publisher:	Insitute of Mathematical Statistics
ISSN:	1932-6157
Official Date:	January 2012
Dates:	Date Event January 2012 Published
Volume:	Vol.6
Number:	No.3
Page Range:	pp. 1209-1235
DOI:	10.1214/11-AOAS532
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), National Cancer Institute (U.S.) (NCI)
Grant number:	EP/E501311/1 (EPSRC), U54 CA 112970 (NCI)

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