Abstract
Differential network analysis has become a widely used technique to investigate changes of interactions among different conditions. Although the relationship between observed interactions and biochemical mechanisms is hard to establish, differential network analysis can provide useful insights about dysregulated pathways and candidate biomarkers. The available methods to detect differential interactions are heterogeneous and often rely on assumptions that are unrealistic in many applications. To address these issues, we develop a novel method for differential network analysis, using the so-called disparity filter as network reduction technique. In addition, we propose a classification model based on the inferred network interactions. The main novelty of this work lies in its ability to preserve connections that are statistically significant with respect to a null model without favouring any resolution scale, as a hard threshold would do, and without Gaussian assumptions. The method was tested using a published metabolomic dataset on colorectal cancer (CRC). Detected hub metabolites were consistent with recent literature and the classifier was able to distinguish CRC from polyp and healthy subjects with great accuracy. In conclusion, the proposed method provides a new simple and effective framework for the identification of differential interaction patterns and improves the biological interpretation of metabolomics data.
Highlights
It is widely known that a complex biological phenomenon, such as the outbreak and the progression of cancer, is often the result of a complex web of interactions between several biological components, occurring at different levels
The proposed network differential analysis allowed to identify hub metabolites and differential association patterns which were consistent with current knowledge and the original paper, and the partial least square-discriminant analysis (PLS-DA) classifiers were able to distinguish with a good accuracy colorectal cancer (CRC) sample from both healthy and polyp samples, suggesting that the identified differential networks can be meaningful and have discriminative power
The differential network analysis framework here proposed consists of three main steps: the inference of a weighted differential association network, the reduction of such network to its backbone by application of the disparity filter, and the construction of a model that exploits the information obtained from the differential network to classify the two phenotypes
Summary
It is widely known that a complex biological phenomenon, such as the outbreak and the progression of cancer, is often the result of a complex web of interactions between several biological components, occurring at different levels. The second method, instead of defining separate association networks that require separate decisions and thresholds, consists in testing directly for differential associations and building an edge-difference based differential network [10,11,12] According to this approach, the first step is to define a dissimilarity measure to evaluate each pairwise differential interaction among the nodes between the two conditions. We defined and applied a new simple but effective workflow to conduct a weighted differential correlation network analysis on metabolomics data and we propose a classification model based on the inferred network’s information able to measure the explanatory power of the resulting network model and to evaluate if the identified differential associations had a discriminative power. The proposed network differential analysis allowed to identify hub metabolites and differential association patterns which were consistent with current knowledge and the original paper, and the PLS-DA classifiers were able to distinguish with a good accuracy CRC sample from both healthy and polyp samples, suggesting that the identified differential networks can be meaningful and have discriminative power
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
