EZ-Pair graph: scalable unified-axis visualization method for summarizing large-scale paired data

Avoiding dual-axis visualization improves quantitative interpretation. Yet for large-scale paired datasets, combining raw data with summary metrics on dual axes often hinders interpretability, while single-axis displays risk visual saturation of paired lines. These limitations may be overcome by developing summary metrics that can be plotted on the same axis as the underlying data.

We developed EZ-Pair Graph as a suite of highly scalable methods that aggregate positional and slope information of numerous lines into a unified and interpretable axis. EZ-Pair Graph comprises three complementary tools: trapezoid plot, which summarizes ascending and descending groups of paired differences and their prevalence, and the clustered line or parallel arrow plot, which can reveal clustered patterns and directional heterogeneity in paired differences. By selectively emphasizing the rank and magnitude of paired differences, these plots facilitate the interpretation of distributional differences in large-scale paired data. We demonstrate the effectiveness of our methods using biological datasets that are difficult to visualize using conventional approaches. In each case, our methods revealed structured, localized, and heterogeneous trends through clear visual summaries. As datasets increase in scale and complexity, EZ-Pair Graph may be useful for detecting underlying patterns and localized variations that are often overlooked in conventional paired-data visualizations.

https://github.com/010049nn/EZ_pair_graph; EZ-Pair Graph outputs are available in multiple formats (PDF, SVG, PNG, HTML, and JSON). Installation via pip and docker is possible. Release archive DOI: 10.5281/zenodo.20437542.