The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
Cleveland, W. S., & Devlin, S. J. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478639 · Cited by 5,742
Gene selection and classification of microarray data using random forest
Díaz-Uriarte, R., & Alvarez de Andrés, S. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-3 · Cited by 2,929
An assessment of the effectiveness of a random forest classifier for land-cover classification
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2011.11.002 · Cited by 2,907
Toward integrating feature selection algorithms for classification and clustering
Liu, H., & Yu, L. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.66 · Cited by 2,706
Design Sensitivity: Statistical Power for Experimental Research.
Eliason, S. R., & Lipsey, M. W. (1991) · Contemporary Sociology · doi:10.2307/2072167 · Cited by 1,040
Large-Scale Human Metabolomics Studies: A Strategy for Data (Pre-) Processing and Validation
Bijlsma, S., Bobeldijk, I., Verheij, E. R., Ramaker, R., Kochhar, S., Macdonald, I. A., van Ommen, B., & Smilde, A. K. (2006) · Analytical Chemistry · doi:10.1021/ac051495j · Cited by 867
Shah, A. D., Bartlett, J. W., Carpenter, J., Nicholas, O., & Hemingway, H. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwt312 · Cited by 798
The genetics of plant metabolism
Keurentjes, J. J. B., Fu, J., de Vos, C. H. R., Lommen, A., Hall, R. D., Bino, R. J., van der Plas, L. H. W., Jansen, R. C., Vreugdenhil, D., & Koornneef, M. (2006) · Nature Genetics · doi:10.1038/ng1815 · Cited by 499
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?
Touw, W. G., Bayjanov, J. R., Overmars, L., Backus, L., Boekhorst, J., Wels, M., & van Hijum, S. A. F. T. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs034 · Cited by 453
Cited by
Metabolomic and inflammatory signatures of symptom dimensions in major depression
Brydges, C. R., Bhattacharyya, S., Dehkordi, S. M., Milaneschi, Y., Penninx, B., Jansen, R., Kristal, B. S., Han, X., Arnold, M., Kastenmüller, G., et al. (2022) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2022.02.003 · Cited by 108
Self-citationEvaluating and minimizing batch effects in metabolomics
Han, W., & Li, L. (2022) · Mass Spectrometry Reviews · doi:10.1002/mas.21672 · Cited by 105
Metadata
- Authors
- Fan, S., Kind, T., Cajka, T., Hazen, S. L., Tang, W. H. W., Kaddurah-Daouk, R., Irvin, M. R., Arnett, D. K., Barupal, D. K., & Fiehn, O.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-02-13
- Volume
- 91
- Issue
- 5
- Pages
- 3590-3596
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2913413642
- Cited by
- 336
- References
- 42
- FWCI
- 7.88
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data. https://app.cochat.ai/papers/10.1021/acs.analchem.8b05592
- The paper
- Fan, S., Kind, T., Cajka, T., Hazen, S. L., Tang, W. H. W., Kaddurah-Daouk, R., Irvin, M. R., Arnett, D. K., Barupal, D. K., & Fiehn, O. (2019). Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data. Analytical Chemistry. https://doi.org/10.1021/acs.analchem.8b05592
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
