Počet záznamů: 1
Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data
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SYSNO ASEP 0504336 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data Tvůrce(i) Fan, S. (US)
Kind, T. (US)
Čajka, Tomáš (FGU-C) RID, ORCID, SAI
Hazen, S.L. (US)
Tang, W.H.W. (US)
Kaddurah-Daouk, R. (US)
Irvin, M. R. (US)
Arnett, D. K. (US)
Barupal, D. K. (US)
Fiehn, O. (US)Zdroj.dok. Analytical Chemistry. - : American Chemical Society - ISSN 0003-2700
Roč. 91, č. 5 (2019), s. 3590-3596Poč.str. 7 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova lipidomics ; quality control ; data normalization ; cohort ; lipids Vědní obor RIV CB - Analytická chemie, separace Obor OECD Analytical chemistry Způsob publikování Omezený přístup Institucionální podpora FGU-C - RVO:67985823 UT WOS 000460709200057 EID SCOPUS 85062373533 DOI 10.1021/acs.analchem.8b05592 Anotace Large-scale untargeted lipidomics experiments involve the measurement of hundreds to thousands of samples. Such data sets are usually acquired on one instrument over days or weeks of analysis time. Such extensive data acquisition processes introduce a variety of systematic errors, including batch differences, longitudinal drifts, or even instrument-to instrument variation. Technical data variance can obscure the true biological signal and hinder biological discoveries. To combat this issue, we present a novel normalization approach based on using quality control pool samples (QC). This method is called systematic error removal using random forest (SERRF) for eliminating the unwanted systematic variations in large sample sets. We compared SERRF with 15 other commonly used normalization methods using six lipidomics data sets from three large cohort studies (832, 1162, and 2696 samples). SERRF reduced the average technical errors for these data sets to 5% relative standard deviation. We conclude that SERRF outperforms other existing methods and can significantly reduce the unwanted systematic variation, revealing biological variance of interest. Pracoviště Fyziologický ústav Kontakt Lucie Trajhanová, lucie.trajhanova@fgu.cas.cz, Tel.: 241 062 400 Rok sběru 2020 Elektronická adresa https://pubs.acs.org/doi/10.1021/acs.analchem.8b05592
Počet záznamů: 1