Comparison of a new spectrum alignment algorithm with other methods

Many data generated by the analytical techniques (such as chromatography, mass spectrometry (MS), near infrared (NIR) spectroscopy and nuclear magnetic resonance (NMR) spectroscopy) are not synchronized or aligned, where the same peak may show up at different positions for different samples. Before applying any analysis method to these spectra, data synchronization/alignment is needed to ensure that the same variable represents the same attribute in all samples. In this work, we propose a three-step self-calibrated warping (SCW) algorithm and apply it to align a clinical prostate cancer data set generated by mass spectrometry. The performance of SCW, in terms of alignment quality and computation speed, is compared with two existing methods. In addition, the robustness of SCW to its tuning parameters and data noise is studied.