MultiDA: Chemometric software for multivariate data analysis based on Matlab

Abstract

Multivariate data analysis (MultiDA), a user-friendly interface chemometric software, is developed for the routine metabolomics/metabonomics data analysis. There are mainly two advantages for MultiDA. First, it could simultaneously provide multiply methods for data preprocessing and multivariate analysis. The main chemometric methods in MultiDA contains k-means cluster analysis, k-medoid cluster analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA), robust principal component analysis (ROPCA), non-linear PCA (NLPCA), non-linear iterative partial least squares (NIPALS), SIMPLS, discriminate analysis (DA), canonical discriminate analysis (CDA), stepwise discriminate analysis (SDA), uncorrelated linear discriminate analysis (ULDA) and some data preprocessing methods, such as standardization, outlier detection, genetic algorithm for feature selection (GAFS), orthogonal signal correction (OSC), weight analysis (Weight) etc. Second, multi-model comparison could be conducted to obtain the best outcome. Moreover, this software is available for free.