  Network modeling has become increasingly popular for analyzing genomic data,
to aid in the interpretation and discovery of possible mechanistic components
and therapeutic targets. However, genomic-scale networks are high-dimensional
models and are usually estimated from a relatively small number of samples.
Therefore, their usefulness is hampered by estimation instability. In addition,
the complexity of the models is controlled by one or more penalization (tuning)
parameters where small changes to these can lead to vastly different networks,
thus making interpretation of models difficult. This necessitates the
development of techniques to produce robust network models accompanied by
estimation quality assessments.
  We introduce Resampling of Penalized Estimates (ROPE): a novel statistical
method for robust network modeling. The method utilizes resampling-based
network estimation and integrates results from several levels of penalization
through a constrained, over-dispersed beta-binomial mixture model. ROPE
provides robust False Discovery Rate (FDR) control of network estimates and
each edge is assigned a measure of validity, the q-value, corresponding to the
FDR-level for which the edge would be included in the network model. We apply
ROPE to several simulated data sets as well as genomic data from The Cancer
Genome Atlas. We show that ROPE outperforms state-of-the-art methods in terms
of FDR control and robust performance across data sets. We illustrate how to
use ROPE to make a principled model selection for which genomic associations to
study further. ROPE is available as an R package on CRAN.
