Bootstrap classification and point-based feature selection from age-staged mouse cerebellum tissues of matrix assisted laser desorption/ionization mass spectra using a fuzzy rule-building expert system.

A bootstrap method for point-based detection of candidate biomarker peaks has been developed from pattern classifiers. Point-based detection methods are advantageous in comparison to peak-based methods. Peak determination and selection are problematic when spectral peaks are not baseline resolved or on a varying baseline. The benefit of point-based detection is that peaks can be globally determined from the characteristic features of the entire data set (i.e., subsets of candidate points) as opposed to the traditional method of selecting peaks from individual spectra and then combining the peak list into a data set. The point-based method is demonstrated to be more effective and efficient using a synthetic data set when compared to using Mahalanobis distance for feature selection. In addition, probabilities that characterize the uniqueness of the peaks are determined. This method was applied for detecting peaks that characterize age-specific patterns of protein expression of developing and adult mouse cerebella from matrix assisted laser desorption/ionization (MALDI) mass spectrometry (MS) data. The mice comprised three age groups: 42 adults, 19 14-day-old pups, and 16 7-day-old pups. Three sequential spectra were obtained from each tissue section to yield 126, 57 and 48 spectra for adult, 14-day-old pup, and 7-day-old pup spectra, respectively. Each spectrum comprised 71,879 mass measurements in a range of 3.5-50 kDa. A previous study revealed that 846 unique peaks were detected that were consistent for 50% of the mice in each age group (C. Laurent, D.F. Levinson, S.A. Schwartz, P.B. Harrington, S.P. Markey, R.M. Caprioli, P. Levitt, Direct profiling of the cerebellum by MALDI MS: a methodological study in postnatal and adult mouse, J. Neurosci. Res. 81 (2005) 613-621.). A fuzzy rule-building expert system (FuRES) was applied to investigate the correlation of age with features in the MS data. FuRES detected two outlier pup-14 spectra. Prediction was evaluated using 100 bootstrap samples of 2 Latin-partitions (i.e., 50:50 split between training and prediction set) of the mice. The spectra without the outliers yielded classification rates of 99.1+/-0.1%, 90.1+/-0.8%, and 97.0+/-0.6% for adults, 14-day-old pups, and 7-day-old pups, respectively. At a 95% level of significance, 100 bootstrap samples disclosed 35 adult and 21 pup distinguishing peaks for separating adults from pups; and 8 14-day-old and 15 7-day-old predictive peaks for separating 14-day-old pup from 7-day-old pup spectra. A compressed matrix comprising 40,393 points that were outside the 95% confidence intervals of one of the two FuRES discriminants was evaluated and the classification improved significantly for all classes. When peaks that satisfied a quality criterion were integrated, the 55 integrated peak areas furnished significantly improved classification for all classes: the selected peak areas furnished classification rates of 100%, 97.3+/-0.6%, and 97.4+/-0.3% for adult, 14-day-old pups, and 7-day-old pups using 100 bootstrap Latin partitions evaluations with the predictions averaged. When the bootstrap size was increased to 1000 samples, the results were not significantly affected. The FuRES predictions were consistent with those obtained by discriminant partial least squares (DPLS) classifications.