5- Modeling Interactions among Samples, Genes, and Compounds¶

This module shifts the focus from isolated annotation profiles to the web of co-annotations that connects samples, genes, and compounds. Having established which KOs and compounds are annotated per sample (Modules 2–4), we now ask how these annotation components are organized into co-occurrence networks that can reveal shared annotation patterns, potential redundancy, and co-annotation structure. By integrating information across multiple layers—samples, annotated genes, chemical classes, and regulatory frameworks—we aim to reconstruct a co-annotation landscape that may be informative for hypothesis generation and experimental prioritization.

5.1. What is the structure of sample-level co-annotations?¶

We first examine the highest-level co-annotation patterns to understand how samples relate to each other and to their chemical and regulatory context. Chord diagrams are used to address three complementary questions: (i) how samples cluster based on shared compound annotation profiles; (ii) which samples are most co-annotated with particular chemical classes; and (iii) which samples are most relevant to specific regulatory agencies or frameworks. The result is a global co-annotation map that summarizes annotation patterns and shared compound coverage across samples, revealing broad and narrow annotation profiles and potential complementarity across the dataset.

5.2. What are the underlying molecular co-annotation networks that define this landscape?¶

To examine the annotation patterns driving the observed sample-level structure, we interrogate the molecular co-annotation layer. Here, we construct networks whose nodes represent genes and compounds, and whose edges encode co-annotation relationships—such as shared compound co-annotations, co-occurrence across samples, or joint presence in annotated pathways. This perspective allows us to ask: which genes are co-annotated through common compounds, and which compounds share annotated gene associations? The resulting networks may reveal gene co-annotation clusters and chemically coherent groupings that recur across samples.

5.3. Which co-annotation hubs and motifs are most prominent in the dataset?¶

Finally, we integrate the sample-level and molecular-level co-annotation views to identify key elements within the annotation landscape that appear frequently. By analyzing network properties such as connectivity, redundancy, and shared neighbors across samples, genes, and compounds, we may highlight co-annotation hubs (e.g., highly connected genes or compounds) and recurrent motifs (e.g., small gene–compound–sample triads) that may represent annotation patterns of interest. This synthesis can enable the prioritization of candidate samples, annotated pathways, and chemical targets based on their centrality within the co-annotation network, providing an annotation-level basis for experimental hypothesis generation.