Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985823%3A_____%2F19%3A00504336" target="_blank" >RIV/67985823:_____/19:00504336 - isvavai.cz</a>
Result on the web
<a href="https://pubs.acs.org/doi/10.1021/acs.analchem.8b05592" target="_blank" >https://pubs.acs.org/doi/10.1021/acs.analchem.8b05592</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1021/acs.analchem.8b05592" target="_blank" >10.1021/acs.analchem.8b05592</a>
Alternative languages
Result language
angličtina
Original language name
Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10406 - Analytical chemistry
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Analytical Chemistry
ISSN
0003-2700
e-ISSN
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Volume of the periodical
91
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
7
Pages from-to
3590-3596
UT code for WoS article
000460709200057
EID of the result in the Scopus database
2-s2.0-85062373533