  Understanding gene regulation is a fundamental step towards understanding of
how cells function and respond to environmental cues and perturbations. An
important step in this direction is to infer the transcription factor-gene
regulatory network (GRN). However gene regulatory networks are typically
constructed disregarding the fact that regulatory programs are conditioned on
tissue type, developmental stage, sex, and other factors. Collecting multitude
of features required for a reliable construction of GRNs such as physical
features and functional features for every context of interest is costly.
Therefore we need methods that is able to use the knowledge of a
context-agnostic network for construction of a context specific regulatory
network.
  To address this challenge we developed a computational approach that uses
context specific expression data and a GRN constructed in a different but
related context to construct a context specific GRN. Our method, NetREX, is
inspired by network component analysis that estimates TF activities and their
influences on target genes given predetermined topology of a TF-gene network.
To predict a network under a different condition, NetREX removes the
restriction that the topology of the TF-gene network is fixed and allows for
adding and removing edges to that network. To solve the corresponding
optimization problem, we provide a general mathematical framework allowing use
of the recently proposed PALM technique and develop a convergent algorithm.
  We tested our NetREX on simulated data and subsequently applied it to gene
expression data in adult females from Drosophila deletion (DrosDel) panel. The
networks predicted by NetREX showed higher biological consistency than
alternative approaches. In addition, we used the list of recently identified
targets of the Doublesex (DSX) transcription factor to demonstrate the
predictive power of our method.
