Stability Indicators in Network Reconstruction

TL;DR

This paper introduces four stability indicators to evaluate the reliability of network reconstructions from high-throughput data, especially when the true network is unknown, using a bootstrap-based approach.

Contribution

It presents novel quantitative stability measures for network reconstruction, applicable to biological data, and demonstrates their effectiveness on real and simulated datasets.

Findings

Indicators successfully assess network stability and variability.

Application to microarray data reveals stable and variable network components.

Bootstrap-based measures provide robust evaluation of network reconstructions.

Abstract

The number of algorithms available to reconstruct a biological network from a dataset of high-throughput measurements is nowadays overwhelming, but evaluating their performance when the gold standard is unknown is a difficult task. Here we propose to use a few reconstruction stability tools as a quantitative solution to this problem. We introduce four indicators to quantitatively assess the stability of a reconstructed network in terms of variability with respect to data subsampling. In particular, we give a measure of the mutual distances among the set of networks generated by a collection of data subsets (and from the network generated on the whole dataset) and we rank nodes and edges according to their decreasing variability within the same set of networks. As a key ingredient, we employ a global/local network distance combined with a bootstrap procedure. We demonstrate the use of the indicators in a controlled situation on a toy dataset, and we show their application on a miRNA microarray dataset with paired tumoral and non-tumoral tissues extracted from a cohort of 241 hepatocellular carcinoma patients.