Evaluating Directed Acyclic Graphs with DAGMetrics: Insights from Tuber and Soil Microbiome Data

Understanding and evaluating directed acyclic graphs (DAGs) is crucial for causal discovery, particularly in high-dimensional and small-sample datasets such as microbial abundance data. This study introduces DAGMetrics, an R package designed to evaluate and compare DAGs comprehensively. The package provides descriptive and comparative metrics, streamlining the assessment of outputs from various structure learning algorithms. It was applied to datasets generated for potato tubers and soils from different terroirs (continental and island) and stages (at harvest and post-harvest). Using a comprehensive set of descriptive and comparative metrics, DAGMetrics facilitated model selection by identifying balanced and robust DAGs. PC algorithm with Spearman correlation produced DAGs with moderate complexity and high stability across scaling and transformation setups. Additionally, the package enabled detailed exploration of the Markov blanket space, revealing small Markov blankets (up to 7 nodes) and numerous isolated nodes. Identified matching edges between Markov blankets across different terroirs and stages aligned with known microbial interactions, highlight the package’s utility in facilitating the discovery of biologically meaningful relationships. This study highlights the utility of DAGMetrics in providing objective, reproducible tools for DAG evaluation and its potential application in agronomic and other domains involving complex, structured data.