Biology subjects

Runghen, R.

Publications and source records attributed to Runghen, R..

3 recordsLinked to original sources

bioRxiv2026

Confounding effects of inferring gene co-expression networks from pooled data from different biological populations

Weighted Gene Co-expression Network Analysis (WGCNA) is routinely applied to pooled datasets from multiple biological populations, genotypes, or treatment groups, implicitly assuming a shared module structure across groups. While the distortion of pairwise correlations by pooling heterogeneous groups is well established statistically, three aspects of this problem have received little systematic attention in the context of co-expression network analysis: the extent to which pooling disrupts the discrete module-level community structure inferred by WGCNA; whether this disruption is detectable from the global topology metrics researchers routinely report; and how prevalent the pooling practice is in published multi-group WGCNA studies. Using analytical toy examples and a four-scenario simulation framework, we address all three questions. Module preservation Zsummary scores declined progressively with between-population divergence, from full preservation under identical populations (mean median Zsummary = 25.2 {+/-} 3.3, 95% interval 19.0-30.7 across 20 simulation replicates) to substantial disruption when both network structure and mean expression differed (mean median Zsummary = 11.9 {+/-} 1.0, 95% interval 10.2-13.5). This disruption was undetectable from global topology metrics: modularity and clustering coefficient remained stable across all scenarios, while edge density was sensitive but non-specific. These findings were corroborated in an empirical reanalysis of divergent lake and stream stickleback transcriptomes, where merged analysis collapsed 26 lake-specific and 59 stream-specific modules into only 19 merged modules. A survey of 100 publications found that 78.7% (95% CI 69.4-87.9%) of multi-group WGCNA studies with sufficient methodological reporting used a single merged analysis. Results were robust across network sizes of 250-1,000 genes and rewiring rates of 10-50%. We provide concrete recommendations including module preservation testing in both directions, population-specific baseline networks, and consensus WGCNA as a principled alternative.

bioinformatics↗

bioRxiv2024

Effects of hybridization and gene flow on gene co-expression networks

Gene co-expression networks are a widely used tool for summarizing transcriptomic variation between individuals, and for inferring the transcriptional regulatory pathways that mediate genotype-phenotype relationships. However, these co-expression networks must be interpreted with caution, as they can arise from multiple processes. Here, we show that hybridization and gene flow between populations can greatly modify co-expression networks. Admixture between populations produces correlated expression between genes experiencing linkage disequilibrium. This correlated expression does not reflect functional relationships between genes, but rather depends on migration rates and physical linkage on chromosomes. Given the prevalence of gene flow between divergent populations in nature, these introgression effects likely represent a significant force in network evolution, even in populations where hybridization is historical rather than contemporary. These findings emphasize the critical importance of considering both evolutionary history and genomic architecture when analyzing gene co-expression networks in natural populations.

genetics↗

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↗