Statistical end-to-end analysis of large-scale microbial growth data with DGrowthR

Abstract

Abstract Quantitative analysis of microbial growth curves is essential for understanding how bacterial populations respond to environmental cues. Traditional analysis approaches make parametric assumptions about the functional form of these curves, limiting their usefulness for studying conditions that distort standard growth curves. In addition, modern robotics platforms enable the high-throughput collection of large volumes of growth data, thus requiring strategies that can analyze large-scale growth data in a flexible and efficient manner. Here, we introduce DGrowthR , a statistical R framework and standalone app with a no-code interface for the integrative analysis of large growth experiments. DGrowthR comprises methods for data pre-processing and standardization, exploratory functional data analysis, and non-parametric modeling of growth curves using Gaussian Process regression. Importantly, DGrowthR includes a rigorous statistical testing framework for differential growth (DG) analysis. To illustrate the range of application scenarios of DGrowthR , we analyzed three large-scale bacterial growth datasets targeting distinct scientific inquiries. On an in-house dataset comprising more than 20, 000 growth curves of two pathogens that were subjected to chemical perturbations, DGrowthR enabled the discovery of compounds with significant growth inhibitory effects as well as compounds that induce non-canonical growth dynamics. On two publicly available perturbation datasets (> 100, 000 growth curves), DG analysis recovered reported adjuvants and antagonists of antibiotic activity, as well as bacterial genetic factors that determine susceptibility to specific antibiotic treatments. We anticipate DGrowthR to streamline the analysis of high-volume growth experiments, enabling researchers to make biological discoveries in a standardized and reproducible manner.