Biomarker Heatmaps: an Application of an ‘-omics’ Approach to Detect Storm-induced Source Changes in Fluvial Particulate Organic Carbon

Abstract The rapid response of riverine particulate organic carbon (POC) to storm hydrograph and its complex chemical compositions due to the diverse sources needs high-resolution sampling and more complementary analytical techniques to understand its storm-induced source dynamics. Such experimental designs inevitably yield larger datasets that require a new data analysis approach to gain a comprehensive overview of the data. Here, we propose to apply the ‘-omics’ approach to seek patterns in source activation and transition and their timings during storm events more effectively and intuitively. Biomarker concentration data are scaled and used to construct a biomarker heatmap using the ComplexHeatmap package in R. Hierarchical clustering is performed on the heatmaps to reorder the biomarkers based on their concentration fluctuations during storm events. We demonstrate the application of this approach to our high-frequency biomarker data obtained from storm POC samples collected in Clear Creek, Iowa. Our heatmap with clustering showed clear time series patterns in biomarker concentration changes, which can be interpreted as source changes. Some possible hypotheses are also discussed based on the biomarker clusters and their unique activation timings captured in our heatmap. This biomarker heatmap approach will help scientists to assess broad patterns in storm-induced POC source changes by offering a new perspective to explore the data as well as help to generate relevant hypotheses to be tested in follow-up analyses.