Application of gradient boosting machine for signal processing in LC-MS metabolomics

Plyushchenko, I.V.; Rodin, I.A.

Авторы: Plyushchenko I.V., Rodin I.A.
Сборник: 13th Winter Symposium on Chemometrics. Book of Abstracts
Тезисы
Год издания: 2022
Место издания: Russian Chemometrics Society Moscow
Первая страница: 56
Последняя страница: 56
Аннотация: Signal drift correction with batch effect removal in LC-MS based metabolomics is aimed to removeunwanted variation components, such as within- and between-batch variations, both sample andinstrument sensitivity changes over time in a long injection sequence and dependency of measured valuesby injection order and/or batch number. “QC-LOESS” [1] regression for signal correction has become the“gold” standard over the last decade. Some improvements include the implementation of other nonlinearalgorithms, i.e. random forest (RF) [2].A complex examination of other regression models was performed [3] by various numeric criteria [4].As a result, the new signal correction method, which outperformed other QC regression algorithms, basedon gradient boosting machine (XGboost, QC-XGB) was proposed. A more precise comparison based ongraphical estimation was carried out on Amide [5] metabolomics dataset for QC-LOESS, QC-RF and QCXGB methods.References1. Dunn W.B., Broadhurst D., Begley P., Zelena E., Francis-McIntyre S., Anderson N., Brown M.,Knowles J.D., Halsall A., Haselden J.N., Nicholls A.W., Wilson I.D., Kell D.B., Goodacre R. Nat.Protoc. 6 (2011), 1060–1083.2. Luan H., Ji F., Chen Y., Cai Z. Anal. Chim. Acta. 1036 (2018), 66–72.3. Plyushchenko I. V., Fedorova E. S., Potoldykova N. V., Polyakovskiy K. A., Glukhov A. I., Rodin I.A. J. Proteome Res. (2021), 1c00392.4. Sánchez-Illana Á., Piñeiro-Ramos J.D., Sanjuan-Herráez J.D., Vento M., Quintás G., Kuligowski J.Anal. Chim. Acta. 1019 (2018), 38–48.5. Deng K., Zhang F., Tan Q., Huang Y., Song W., Rong Z., Zhu Z.-J., Li K., Li Z. Anal. Chim. Acta.1061 (2019), 60–69.
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