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.

► New method of peaks alignment called supervised alignment (SA) was developed and introduced.
► Supervised alignment method was compared with correlation optimized warping (COW).
► Using simulated data sets it was shown that supervised alignment can very efficiently handle the systematic peak shifts.
► The alignment of a matrix that consisted of 208 urine samples was performed based on retention time of only 10 peaks.
► The total analysis time depend mainly on the number of peaks needed for alignment and can last for a few minutes only.