Publication Date:
2022-05-26
Description:
© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in McLean, C., & Kujawinski, E. B. AutoTuner: high fidelity and robust parameter selection for metabolomics data processing. Analytical Chemistry, 92(8), (2020): 5724-5732, doi:10.1021/acs.analchem.9b04804.
Description:
Untargeted metabolomics experiments provide a snapshot of cellular metabolism but remain challenging to interpret due to the computational complexity involved in data processing and analysis. Prior to any interpretation, raw data must be processed to remove noise and to align mass-spectral peaks across samples. This step requires selection of dataset-specific parameters, as erroneous parameters can result in noise inflation. While several algorithms exist to automate parameter selection, each depends on gradient descent optimization functions. In contrast, our new parameter optimization algorithm, AutoTuner, obtains parameter estimates from raw data in a single step as opposed to many iterations. Here, we tested the accuracy and the run-time of AutoTuner in comparison to isotopologue parameter optimization (IPO), the most commonly used parameter selection tool, and compared the resulting parameters’ influence on the properties of feature tables after processing. We performed a Monte Carlo experiment to test the robustness of AutoTuner parameter selection and found that AutoTuner generated similar parameter estimates from random subsets of samples. We conclude that AutoTuner is a desirable alternative to existing tools, because it is scalable, highly robust, and very fast (∼100–1000× speed improvement from other algorithms going from days to minutes). AutoTuner is freely available as an R package through BioConductor.
Description:
We thank Titus Brown and Ben Temperton for advice on the algorithm validation, Arthur Eschenlauer for constructive feedback on the software design, Krista Longnecker for continuous support and discussions, Gabriel Leventhal for mathematics advice, the users of AutoTuner for debugging help through Github, and David Angeles-Albores and two anonymous reviewers for critical feedback on the manuscript. Funding support included the National GEM Consortium and NSF graduate research program fellowships (C.M.) and grants from the MIT Microbiome Center (Award 6936800, E.B.K.) and the Simons Foundation (Award ID #509034, E.B.K.).
Repository Name:
Woods Hole Open Access Server
Type:
Article
