Metabolomics is expected to boost data driven research. In biomarker discovery, powerful filtering methods to remove noise and outliers are essential for screening significant candidates from the huge volume of omic data. Here we propose a post-measurement peak filtering method (named P-BOSS) for CE electrospray ionization-time-of-flight MS (CE-TOFMS) data. Combining outlier detection method functions in parallel, we applied P-BOSS to the data using Escherichia coli knockout mutants of the tryptophan and purine biosynthesis pathways. As the result, P-BOSS showed remarkably superior performance, reducing 65% of all peaks, while leaving significant peaks.
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