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Metabr: An R Script For Linear Model Analysis Of Quantitative Metabolomic Data, Ben Ernest, Jessica R. Gooding, Shawn R. Campagna, Arnold M. Saxton, Brynn H. Voy

Chemistry Publications and Other Works

Background

Metabolomics is an emerging high-throughput approach to systems biology, but data analysis tools are lacking compared to other systems level disciplines such as transcriptomics and proteomics. Metabolomic data analysis requires a normalization step to remove systematic effects of confounding variables on metabolite measurements. Current tools may not correctly normalize every metabolite when the relationships between each metabolite quantity and fixed-effect confounding variables are different, or for the effects of random-effect confounding variables. Linear mixed models, an established methodology in the microarray literature, offer a standardized and flexible approach for removing the effects of fixed- and random-effect confounding variables from …