What is it about?
In this study we assessed the effect of choosing different parameters for XCMS preprocessing of untargeted LCMS data. We found that XCMS parameters selected by an LCMS expert outperform the parameter selections Autotuner and IPO. Finally, the absolute worst results were obtained by using default XCMS parameters - our data suggest it is better doing something than nothing.
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Why is it important?
By optimizing the XCMS parameters, LCMS metabolomics studies will become more powerful by better integration of peaks and identification of more true positive peaks.
Read the Original
This page is a summary of: Assessment of XCMS Optimization Methods with Machine-Learning Performance, Analytical Chemistry, September 2021, American Chemical Society (ACS),
DOI: 10.1021/acs.analchem.1c02000.
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