SIMA

Combines hierarchical pairwise correspondence estimation with simultaneous alignment and global retention time correction. SIMA employs a tailored multidimensional kernel function and a procedure based on maximum likelihood estimation to find the retention time distortion function that best fits the observed data. In a comparison with seven alternative methods on four different datasets, SIMA yields competitive and superior performance on real-world data. Conceptually, SIMA is not limited to the alignment of LC/MS data: by redefinition of the thresholded squared Mahalanobis distance function, it can easily be adapted to any time series with discrete events