New supervised alignment method as a preprocessing tool for chromatographic data in metabolomic studies.
The purpose of this work was to develop a new aligning algorithm called supervised alignment and to compare its performance with the correlation optimized warping. The supervised alignment is based on a "supervised" selection of a few common peaks presented on each chromatogram. The selected peaks are aligned based on a difference in the retention time of the selected analytes in the sample and the reference chromatogram. The retention times of the fragments between known peaks are subsequently linearly interpolated. The performance of the proposed algorithm has been tested on a series of simulated and experimental chromatograms. The simulated chromatograms comprised analytes with a systematic or random retention time shifts. The experimental chromatographic (RP-HPLC) data have been obtained during the analysis of nucleosides from 208 urine samples and consists of both the systematic and random displacements. All the data sets have been aligned using the correlation optimized warping and the supervised alignment. The time required to complete the alignment, the overall complexity of both algorithms, and its performance measured by the average correlation coefficients are compared to assess performance of tested methods. In the case of systematic shifts, both methods lead to the successful alignment. However, for random shifts, the correlation optimized warping in comparison to the supervised alignment requires more time (few hours versus few minutes) and the quality of the alignment described as correlation coefficient of the newly aligned matrix is worse 0.8593 versus 0.9629. For the experimental dataset supervised alignment successfully aligns 208 samples using 10 prior identified peaks. The knowledge about retention times of few analytes' in the data sets is necessary to perform the supervised alignment for both systematic and random shifts. The supervised alignment method is faster, more effective and simpler preprocessing method than the correlation optimized warping method and can be applied to the chromatographic and electrophoretic data sets.
Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland.
Wiktoria
W
Struck
Paweł
P
Wiczling
Małgorzata
M
Waszczuk-Jankowska
Roman
R
Kaliszan
Michał Jan
MJ
Markuszewski
D000465:Algorithms; D002851:Chromatography, High Pressure Liquid / Q000379:methods*; D006801:Humans; D055432:Metabolomics; D016482:Urinalysis