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Activity Number: 340 - Novel Methods for Microbiome and Metabolomic Disease
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #313539
Title: WINN: Drift Correction by White Noise Normalization for Metabolomic Studies.
Author(s): Olga Demler* and Franco Giulianini and Samia Mora
Companies: Harvard Medical School and Harvard University and Harvard University
Keywords: metabolomics; signal correction; drift correction; white noise; LC-MS mass spectrometry

Non-targeted GC, LC/MS instruments are capable of detecting very low concentrations of thousands of metabolomic features and are an important tool for drug discovery, for establishing mechanisms of action and for discovery of novel biomarkers. In large cohorts, samples are analyzed in batches often over prolonged periods of time and are prone to instrument drifts, and other technical variation in experimental conditions. Sometimes, design of the experiment can eliminate drifts, but when it is not possible, metabolites' concentrations need to be corrected for systematic drifts. Correction should be conservative and eliminate drifts while keeping the true signal intact. We developed a novel signal-correction method for large non-targeted metabolomic studies that combines testing for presence of drifts using ensemble of white noise tests and then applies higher levels of drift correction such as smoothing splines to calculate drift correction. Results were validated in independent validation pooled plasma dataset.

Authors who are presenting talks have a * after their name.

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