# ROPE: high-dimensional network modeling with robust control of edge FDR

## TL;DR

ROPE is a new statistical method that improves the robustness and reliability of high-dimensional network models in genomic data analysis by controlling false discovery rates through resampling and mixture modeling.

## Contribution

It introduces ROPE, a novel resampling-based approach that enhances stability and FDR control in high-dimensional network estimation, with practical application to genomic data.

## Key findings

- ROPE outperforms existing methods in FDR control.
- ROPE provides robust network estimates across diverse datasets.
- The method is available as an R package on CRAN.

## Abstract

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.