Fig. 1: Gut viruses drive the differentiation of IBS from HC.

a Integration of gut multi-biome data through weighted similarity network fusion (WSNF) approach. Heatmap of similarity scores derived from WSNF analysis in IBS patients, with spectral clustering identifying two distinct patient subgroups (labeled Cluster 1 and Cluster 2). Color gradient represents pairwise similarity strength (scale bar at right). b Comparison of Bristol score of patients between two identified patient clusters. c Principal coordinate analysis (PCoA) of gut multi-biome of based on Bray–Curtis dissimilarity illustrates two patient clusters. d Random forest classifier model trained on multi-biome can predict the clustering of IBS patients. e Features from gut viruses contribute most to differentiating clusters in the random forest models. f, g Network visualization of key taxa in two clusters in HC (f) and IBS clusters (g). Each node represents a microbial taxon (e.g., a bacterial, viral or fungal species) included in the co-occurrence network. The shape of a node is a visual attribute used to distinguish between different types of microbes (e.g., bacteria vs. viruses vs. fungi). Circles, triangle, and inverted triangle represent microbes and lines represent their associated interactions. Node size (degree) reflects the number of direct interactions for a given microbe. Border thickness represents calculated stress centrality for each microbe, while color depth reflects the positive or negative of the interactions.