Dalla Riva, G. V. | Search bioRxiv

bioRxiv2021

A Cartography of Differential Gene Methylation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Different Network Roles in the Protein-Protein Interactions Network Play Different, Biologically Relevant, Roles

Myalgic Encephalomyelitis, or Chronic Fatigue Syndrome (ME/CFS), is characterised by severe fatigue and associated with immune dysfunction. Previous studies of DNA methylation have found evidence of changes in immune cells for ME/CFS. However these studies have been limited by their small sample size. Here, we aggregate three comparable datasets to achieve a larger sample size and detect small changes to DNA methylation. We find 10,824 differentially methylated genes, with a small average change. Next, from the currently known interactions of relevant proteins, we build a Protein-Protein interaction network and,localising the network cartography analysis, we identify 184 hub genes. We find that different hub types play different, and meaningful, biological roles. Finally, we perform Gene ontology enrichment analysis, and we find that these hubs are involved in immune system processes, including response to TGF-{beta} and LPS, as well as mitochondrial functioning, supporting previous theories about ME/CFS. We also show that dopaminergic signalling may potentially contribute to immune pathology in ME/CFS, suggesting a possible interplay with Long Covid. Our results demonstrate the potentiality of network analysis in shedding light on the epigenetic contribution to the immune dysregulation of ME/CFS.

genomics↗

bioRxiv2021

Exploiting node metadata to predict interactions in large networks using graph embedding and neural networks

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes metadata. Graph embedding methods project the nodes of a network onto a--low dimensional--latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes metadata to their position in a latent feature space corresponds to a classic--and low dimensional--machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes meta-data. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

ecology↗

bioRxiv2017

Analyzing ecological networks of species interactions

Networks provide one of the best representations for ecological communities, composed of many species with sometimes complex connections between them. Yet the methodological literature allowing one to analyze and extract meaning from ecological networks is dense, fragmented, and unwelcoming. We provide a general overview to the field of using networks in community ecology, outlining both the intent of the different measures, their assumptions, and the contexts in which they can be used. When methodologically justified, we suggest good practices to use in the analysis of ecological networks. We anchor this synopsis with examples from empirical studies, and conclude by highlighting what identified as needed future developments in the field.

ecology↗