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

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 and standalone app framework 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 analysis. To illustrate the range of application scenarios of DGrowthR, we analyzed three large-scale bacterial growth datasets that tackle distinct scientific questions. On an in-house large-scale growth dataset comprising two pathogens that were subjected to a large chemical perturbation screen, DGrowthR enabled the discovery of compounds with significant growth inhibitory effects as well as compounds that induce non-canonical growth dynamics. We also re-analyzed two publicly available datasets and recovered reported adjuvants and antagonists of antibiotic activity, as well as bacterial genetic factors that determine susceptibility to specific antibiotic treatments. We anticipate that DGrowthR will streamline the analysis of modern high-volume growth experiments, enabling researchers to gain novel biological insights in a standardized and reproducible manner.