Application of linear and nonlinear discrete wavelet transforms to MALDI-MS measurements of bacteria for classification

Abstract

The linear and nonlinear discrete wavelet transforms (DWTs) were used to compress matrix-assisted laser desorption/ionization mass spectra to address two key challenges: the relatively high noise level and the underdetermined format of the data set. By applying the DWT to MALDI-MS spectra, the spectra were simultaneously smoothed and compressed. Multivariate projected difference resolution was used to evaluate the effects of the linear and nonlinear DWT on classification. The cross-validation study using bootstrapped Latin partition and partial least-squares (PLS-2) has proved that the classification accuracy increased after data compression. The best result was obtained when using Fisher's criterion to choose wavelet coefficients for compression. With the aid of principal component analysis (PCA), different wavelet filters may provide different mathematical perspectives to visualize the clustering of bacteria. The effect of growth time was directly observed with wavelet transform, which could not be observed using the original spectra.