Metabolomics is the large scale study of small organic molecules (metabolites) within an organism, tissue, or cell. This technique and its application to systemic diseases is revealing of potential diagnoses and treatments offering a significant advancement to the medical and clinical communities. However, statistical methods for metabolomics are often misunderstood due to low sample sizes and large feature sets and interpretability of experimental results is difficult without established knowledge of biochemistry and the disease domain. This project aims to clarify many of the statistical techniques utilized in metabolomics, as well as apply them to a common systemic disease, acute kidney injury (AKI). AKI is associated with widespread effects on distant organs, including the heart, liver, and lung, and is seen in upwards of 20\% of hospitalized patients and 70\% of intensive care unit patients. In this study, metabolomics analysis was performed on five separate organ tissues to see the effect of AKI in mice, identify metabolites and biochemical pathways associated with organ dysfunction in AKI, and build classifiers to predict onset of AKI. Many computational techniques are applied offering insight into organ dysfunction, potential treatments, and assistive diagnostic tools for physicians.